United States
Environmental Protection
Agency
Office of Acid Deposition,
Environmental Monitoring and
Quality Assurance
Washington DC 20460
EPA/600/8-88/100
September 1988
Research and Development
Direct/Delayed Response
Project: Quality
Assurance Report for
Physical and Chemical
Analyses of
Soils from the
Southern Blue Ridge
Province of the
United States
-------
.EPA/600/8-88/100
September 1988
Direct/Delayed Response Project:
Quality Assurance Report for Physical and
Chemical Analyses of Soils from the
Southern Blue Ridge Province
of the United States
by
R.D. Van Remortel, 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
A Contribution to the
National Acid Precipitation Assessment Program
U.S. Environmental Protection Agency
Region 5, Library (5PL-16)
230 S. Dearborn L^-eet, Room 1670
Chicago, IL 60604
\(3 /"•*/* U'S- Environmental Protection Agency
Office of Research and Development
Washington. DC 20460
Environmental Monitoring Systems Laboratory - Las Vegas, NV 891 14
Environmental Research Laboratory - Corvallis, OR 97333
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Notice
The development of 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 &
Sciences Company (formerly Lockheed Engineering and Management Services Company) and
Cooperative Agreement Number 812189-03 to the Environmental Research Center of the University
of Nevada at Las Vegas. Additional cooperation has been provided under Contract Number 68-
03-3246 to NSI Technology Services Corporation. The document has been subject to the Agency's
peer and administrative review and it has been approved for publication as an EPA report.
Mention of corporation names, trade names, or commercial products does not constitute
endorsement or recommendation for use.
This document is one volume of a set which fully describes the Direct/Delayed Response
Project, Southern Blue Ridge and Northeast soil surveys. The complete document set includes the
major data report, quality assurance plan, analytical methods manual, field 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:
Van Remortel, R. D.1, G. E. Byers1, J. E. Teberg1, M. J. Miah1, C. J. Palmer2, M. L Papp1,
M. H. Battling1, A D. Tansey1, D. L Cassell3, and P. W. Shaffer3. 1988. Direct/Delayed
Response Project: Quality Assurance Report for Physical and Chemical Analyses 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.
1 Lockheed Engineering & Sciences Company, Las Vegas, Nevada 89119.
2 Environmental Research Center, University of Nevada, Las Vegas, Nevada 89114.
3 NSI Technology Services Corporation, Corvallis, Oregon 97333.
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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
Southern Blue Ridge Province Soil Survey was conducted during the summer of 1986 as a synoptic
physical and chemical survey to characterize watersheds located in a region of the United States
believed to be susceptible to the effects of acidic deposition. This document addresses the quality
assurance program and its implementation in the assessment of the verified analytical data base
for the Southern Blue Ridge Province 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 Southern Blue Ridge Province of the United
States.
Data quality is assessed by describing the detectability, precision, accuracy (interlaboratory
differences), representativeness, completeness, and comparability of the data for the quality
assurance samples used throughout the soil survey. The fifty-one parameters in the data base are
segregated into nine groups for ease in discussion.
This report is submitted in partial fulfillment of Contract Number 68-03-3249 by Lockheed
Engineering & Sciences Company (formerly Lockheed Engineering and Management Services
Company), Las Vegas, Nevada, under sponsorship of the U. S. Environmental Protection Agency.
in
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Contents
Page
Notice ii
Abstract iii
List of Figures vii
List of Tables xviii
Acknowledgments xx
List of Abbreviations xxi
1. Introduction 1
Overview of the Survey 1
Organization of the Report 3
Description of Parameter Groups 3
Description of Parameters 4
2. Quality Assurance Program 8
Selection of Analytical Laboratories 8
Statement of Work 8
Performance Evaluations 9
Contract Solicitations 9
Analytical Laboratory Operations 11
Data Reporting 11
System Audits 11
General Laboratory Protocols 11
Quality Assurance and Quality Control Samples 12
Description of Quality Assurance Samples 12
Sample Flow 13
Description of Quality Control Samples 13
Data Verification 15
Overview of Data Bases 15
Verification of Field Data 15
Verification of Analytical Data 16
Data Quality Objectives 20
Detectability 21
Precision 23
Accuracy (Interlaboratory Differences) 25
Representativeness 27
Completeness 27
Comparability 27
-------
Contents (continued)
3. Results and Discussion 29
Detectability 29
Precision 30
Moisture, Specific Surface, and Particle Size Analysis 31
Soil pH 38
Exchangeable Cations in Ammonium Chloride 45
Exchangeable Cations in Ammonium Acetate 54
Cation Exchange Capacity and Exchangeable Acidity 63
Extractable Cations in Calcium Chloride 74
Extractable Iron and Aluminum 87
Extractable Sulfate and Sulfate Adsorption Isotherms 100
Total Carbon, Nitrogen, and Sulfur 117
Accuracy (Interlaboratory Differences) 124
Significant Differences Among Laboratories 124
Relative Differences and Ranking of Laboratories 124
Mean Differences Among the Audit Samples 124
Representativeness 127
Completeness 127
Comparability 128
Comparison of Analytical and Preparation Methods 128
Comparison of Field Sampling Methods 129
Comparison of Audit Sample Distribution 129
4. Conclusions and Recommendations 130
Data Verification 130
Verification of Data Packages 130
Internal Consistency 130
Data Quality Objectives 131
Detectability 131
Precision 131
Accuracy (Interlaboratory Differences) 132
Representativeness 135
Completeness 135
Comparability 135
References 136
Appendices
A. Verification Flags Used in the Southern Blue Ridge Province Soil Survey 138
B. Data Verification Worksheets and Tables 140
C. Table of Statistics for Step Function Precision Estimates 159
D. Inordinate Data Points Influencing the Precision Estimates 175
E. Additional Precision Plots for Moisture, Specific Surface, and Particle Size Fractions 183
F. Table of General Statistics for the Analytical Parameters 193
G. Histograms of Range and Frequency Distributions 200
VI
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List of Figures
Number Page
2-1 Example of a two-tiered precision objective 24
3-1 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for SAND 32
3-2 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for SAND 33
3-3 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for SILT 34
3-4 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for SILT 35
3-5 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for CLAY 36
3-6 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for CLAY 37
3-7 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for PH_H20 39
3-8 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for PHJH20 40
3-9 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for PH_002M 41
3-10 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for PH_002M 42
VII
-------
List of Figures (continued)
Number Page
3-11 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for PH_01M 43
3-12 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for PH_01M 44
3-13 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for CA_CL 46
3-14 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for CA_CL 47
3-15 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for MG_CL 48
3-16 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for MG_CL 49
3-17 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for K_CL 50
3-18 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for K_CL 51
3-19 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for NA_CL 52
3-20 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for NA_CL 53
3-21 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for CA_OAC 55
3-22 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for CA_OAC 56
VIII
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List of Figures (continued)
Number Page
3-23 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for MG_OAC 57
3-24 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for MG_OAC 58
3-25 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for K_OAC 59
3-26 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for K_OAC 60
3-27 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for NA_OAC 61
3-28 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for NA_OAC 62
3-29 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for CEC_CL 64
3-30 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for CEC_CL 65
3-31 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for CEC_OAC 66
3-32 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for CEC_OAC 67
3-33 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for AC_KCL 68
3-34 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for AC_KCL 69
IX
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List of Figures (continued)
Number Page
3-35 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for AC_BACL 70
3-36 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for AC_BACL 71
3-37 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for AL_KCL 72
3-38 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for AL_KCL 73
3-39 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for CA_CL2 75
3-40 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for CA_CL2 76
3-41 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for MG_CL2 77
3-42 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for MG_CL2 78
3-43 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for K_CL2 79
3-44 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for K_CL2 80
3-45 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for NA_CL2 81
3-46 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for NA_CL2 82
-------
List of Figures (continued)
Number Page
3-47 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for FE_CL2 83
3-48. Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for FE_CL2 84
3-49 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for AL_CL2 85
3-50 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for AL_CL2 86
3-51 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for FE_PYP 88
3-52 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for FE_PYP 89
3-53 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for AL_PYP 90
3-54 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for AL_PYP 91
3-55 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for FE_AO 92
3-56 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for FE_AO 93
3-57 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for AL_AO 94
3-58 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for AL_AO 95
XI
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List of Figures (continued)
Number Page
3-59 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for FE_CD 96
3-60 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for FE_CD 97
3-61 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for AL_CD 98
3-62 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for AL_CD 99
3-63 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for SO4JH20 101
3-64 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for SO4JH2O 102
3-65 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for SO4_PO4 103
3-66 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for SO4_PO4 104
3-67 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for SO4_0 105
3-68 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for SO4_0 106
3-69 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for SO4_2 107
3-70 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for SO4_2 108
XII
-------
List of Figures (continued)
Number Page
3-71 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for SO4_4 109
3-72 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for SO4_4 110
3-73 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for SO4_8 111
3-74 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for SO4_8 112
3-75 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for S04_16 113
3-76 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for S04_16 114
3-77 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for SO4_32 115
3-78 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for SO4J32 116
3-79 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for C_TOT 118
3-80 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for C_TOT 119
3-81 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for N_TOT 120
3-82 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for N_TOT 121
XIII
-------
List of Figures (continued)
Number Page
3-83 Range and frequency distribution of the natural audit samples
and their relation to achievement of the analytical within-batch
precision objective for S_TOT 122
3-84 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for S_TOT 123
B-1 DDRP form 500 (data confirmation/reanalysis request) 141
B-2 Data completeness checklist 142
B-3 Quality assurance reanalysis template for specific surface 145
B-4 Quality assurance reanalysis template for particle size analysis 146
B-5 Quality assurance reanalysis template for pH 147
B-6 Quality assurance reanalysis template for exchangeable cations 148
B-7 Quality assurance reanalysis template for cation exchange capacity 149
B-8 Quality assurance reanalysis template for exchangeable acidities 150
B-9 Quality assurance reanalysis template for KCI-extractable aluminum 151
B-10 Quality assurance reanalysis template for extractable iron
and aluminum 152
B-11 Quality assurance reanalysis template for water-extractable
sulfate and phosphate-extractable sulfate 153
B-12 Quality assurance reanalysis template for sulfate isotherms 154
B-13 Quality assurance reanalysis template for total sulfur,
nitrogen, and carbon 155
E-1 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled estimates for MOIST 184
E-2 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for SP_SUR 185
E-3 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for VCOS 186
XIV
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List of Figures (continued)
Number Page
E-4 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for COS 187
E-5 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for MS 188
E-6 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for FS 189
E-7 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for VFS 190
E-8 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for COSI 191
E-9 Range and frequency distribution of sampling class/horizon
routine data partitioned into windows and their relation to
pooled precision estimates for FSI 192
G-1 Histogram of range and frequency distribution for air-dry moisture 201
G-2 Histogram of range and frequency distribution for specific surface 202
G-3 Histogram of range and frequency distribution for total sand 203
G-4 Histogram of range and frequency distribution for very coarse sand 204
G-5 Histogram of range and frequency distribution for coarse sand 205
G-6 Histogram of range and frequency distribution for medium sand 206
G-7 Histogram of range and frequency distribution for fine sand 207
G-8 Histogram of range and frequency distribution for very fine sand 208
G-9 Histogram of range and frequency distribution for total silt 209
G-10 Histogram of range and frequency distribution for coarse silt 210
G-11 Histogram of range and frequency distribution for fine silt 211
G-12 Histogram of range and frequency distribution for total clay 212
G-13 Histogram of range and frequency distribution for pH in water 213
xv
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List of Figures (continued)
Number Page
G-14 Histogram of range and frequency distribution for pH in 0002M calcium chloride .... 214
G-15 Histogram of range and frequency distribution for pH in 001M calcium chloride 215
G-16 Histogram of range and frequency distribution for calcium
in ammonium chloride 216
G-17 Histogram of range and frequency distribution for magnesium
in ammonium chloride 217
G-18 Histogram of range and frequency distribution for potassium
in ammonium chloride 218
G-19 Histogram of range and frequency distribution for sodium
in ammonium chloride 219
G-20 Histogram of range and frequency distribution for calcium
in ammonium acetate 220
G-21 Histogram of range and frequency distribution for magnesium
in ammonium acetate 221
G-22 Histogram of range and frequency distribution for potassium
in ammonium acetate 222
G-23 Histogram of range and frequency distribution for sodium
in ammonium acetate 223
G-24 Histogram of range and frequency distribution for cation exchange capacity
in ammonium chloride 224
G-25 Histogram of range and frequency distribution for cation exchange capacity
in ammonium acetate 225
G-26 Histogram of range and frequency distribution for exchangeable acidity
in potassium chloride 226
G-27 Histogram of range and frequency distribution for exchangeable acidity
in barium chloride triethanolamine 227
G-28 Histogram of range and frequency distribution for exchangeable aluminum
in potassium chloride 228
G-29 Histogram of range and frequency distribution for calcium in calcium chloride 229
G-30 Histogram of range and frequency distribution for magnesium in calcium chloride . . . 230
G-31 Histogram of range and frequency distribution for potassium in calcium chloride .... 231
G-32 Histogram of range and frequency distribution for sodium in calcium chloride 232
xvi
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List of Figures (continued)
Number Page
G-33 Histogram of range and frequency distribution for iron in calcium chloride 233
G-34 Histogram of range and frequency distribution for aluminum in calcium chloride .... 234
G-35 Histogram of range and frequency distribution for iron in pyrophosphate 235
G-36 Histogram of range and frequency distribution for aluminum in pyrophosphate 236
G-37 Histogram of range and frequency distribution for iron in acid oxalate 237
G-38 Histogram of range and frequency distribution for aluminum in acid oxalate 238
G-39 Histogram of range and frequency distribution for iron in citrate dithionite 239
G-40 Histogram of range and frequency distribution for aluminum in citrate dithionite .... 240
G-41 Histogram of range and frequency distribution for water-extractable sulfate 241
G-42 Histogram of range and frequency distribution for phosphate-extractable sulfate .... 242
G-43 Histogram of range and frequency distribution for the sulfate-zero isotherm 243
G-44 Histogram of range and frequency distribution for the sulfate-two isotherm 244
G-45 Histogram of range and frequency distribution for the sulfate-four isotherm 245
G-46 Histogram of range and frequency distribution for the sulfate-eight isotherm 246
G-47 Histogram of range and frequency distribution for the sulfate-16 isotherm 247
G-48 Histogram of range and frequency distribution for the sulfate-32 isotherm 248
G-49 Histogram of range and frequency distribution for total carbon 249
G-50 Histogram of range and frequency distribution for total nitrogen 250
G-51 Histogram of range and frequency distribution for total sulfur 251
XVII
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List of Tables
Number Page
1-1 Analytical Parameters Measured in the Southern Blue Ridge
Province Soil Survey 5
2-1 Distribution of Batches by Contract Solicitation/Laboratory 9
2-2 Contract-Required Detection Limits by Contract Solicitation 10
2-3 Soil Samples which Underwent Secondary Processing Following
Retrieval from the Disqualified Analytical Laboratory 10
2-4 Distribution of Field Duplicate Sample Pairs Among the
Sampling Crews, Preparation Laboratories, and Analytical Laboratories 13
2-5 Distribution of Preparation Duplicate Sample Pairs Among the
Preparation Laboratories and Analytical Laboratories 13
2-6 Distribution of the Natural Audit Sample Pairs Among the
Analytical Laboratories 13
2-7 Data Quality Objectives for Detectability and Analytical
Within-Batch Precision 22
2-8 Primary Horizon Types for Sampling Class/Horizon Groups 25
3-1 Detection Limits for the Contract Requirements, Instrument
Readings, and System-wide Measurement 30
3-2 Achievement of Data Quality Objectives for Analytical
Within-Batch Precision of Moisture, Specific Surface,
and Particle Size Analysis 31
3-3 Achievement of Data Quality Objectives for Analytical
Within-Batch Precision of the Soil pH Parameters 38
3-4 Achievement of Data Quality Objectives for Analytical
Within-Batch Precision of the Exchangeable Cations in
Ammonium Chloride 45
3-5 Achievement of Data Quality Objectives for Analytical
Within-Batch Precision of the Exchangeable Cations in
Ammonium Acetate 54
XVIII
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List of Tables (continued)
Number Page
3-6 Achievement of Data Quality Objectives for Analytical
Within-Batch Precision of Cation Exchange Capacity and
Exchangeable Acidity 63
3-7 Achievement of Data Quality Objectives for Analytical
Within-Batch Precision of the Extractable Cations in
Calcium Chloride 74
3-8 Achievement of Data Quality Objectives for Analytical
Within-Batch Precision of Extractable Iron and Aluminum 87
3-9 Achievement of Data Quality Objectives for Analytical
Within-Batch Precision of Extractable Sulfate and
Sulfate Adsorption 100
3-10 Achievement of Data Quality Objectives for Analytical
Within-Batch Precision of Total Carbon, Nitrogen, and Sulfur 117
3-11 Significant Interlaboratory Differences 125
3-12 Relative Difference and Rank by Laboratory and Mean
Laboratory Difference by Audit Sample Type 126
3-13 Summary of Significant Differences in the Distribution of the
Field and Preparation Duplicates Relative to the Routine Samples 127
4-1 Precision Indices Based on Pooled Within-Batch Precision
Estimates for Parameter Groups Across Concentration Ranges 132
4-2 Summary of Interlaboratory Differences by Laboratory and by
Audit Sample Type 133
A-1 Flags Used in the DDRP Southern Blue Ridge Province Soil Survey 138
B-1 Occurrences of Less-Than-Complete Compliance for Measurement
of Quality Control Check Samples 156
B-2 Internal Consistency Checks Performed for the Southern Blue
Ridge Province Analytical Verified Data Base 157
B-3 Completeness of Soil Analysis Using Data for Routine Samples
from the Verified and Validated Data Bases 158
C-1 Table of Statistics for Step Function Precision Estimates 159
D-1 Inordinate Data Points Having a High Degree of Influence on
the Precision Estimates for the Data Sets 175
F-1 Table of General Statistics for the Analytical Parameters 193
XIX
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Acknowledgments
External peer reviews by the following individuals are gratefully acknowledged: J. D. Bailey,
NSI Technology Services Corporation, Corvallis, Oregon; and P. M. Bertsch, University of Georgia,
Savannah River Ecological Laboratory, Aiken, South Carolina.
The authors wish to acknowledge the following individuals for their technical assistance
during the development of this document: M. R. Church and J. J. Lee, Environmental Research
Laboratory, U.S. Environmental Protection Agency, Corvallis, Oregon; R. D. Schonbrod,
Environmental Monitoring Systems Laboratory, U.S. Environmental Protection Agency, Las Vegas,
Nevada; D. S. Coffey, G. R. Holdren, J. S. Kern, and M. G. Johnson, NSI Technology Services
Corporation, Corvallis, Oregon; D. D. Schmoyer and D. A. Wolf, Martin Marietta Corporation, Oak
Ridge, Tennessee; R. S. Turner and C. C. Brandt, Oak Ridge National Laboratory, Oak Ridge,
Tennessee; P. Gowland, J. Goyert and K. Van Hoesen, Science Applications International
Corporation, Oak Ridge, Tennessee; T. H. Starks, University of Nevada, Las Vegas, Nevada;
W. H. Cole, R. L Slagle, K. A. Cappo, S. A. Snell. L. K. Hill, R. L Tidwell, G. A. Raab,
B. A. Schumacher, R. J. Anderson, J. M. Nicholson, R. K. Goldberg, K. C. Shines, J. V. Burton,
E. Eschner, and J. R. Wilson, Lockheed Engineering & Sciences Company, Las Vegas, Nevada.
The following individuals provided editorial and logistical support and are gratefully
acknowledged: L A. Stanley, K. M. Howe, B. N. Cordova, J. D. Hunter, P. F. Showers, L. M. Mauldin,
and J. L. Engels, Lockheed Engineering & Sciences Company, Las Vegas, Nevada.
Finally, we appreciate the support of our technical monitor, L. J. Blume, throughout the course
of this survey.
xx
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List of Abbreviations
AA atomic absorption
AERP Aquatic Effects Research Program
AC_BACL barium chloride triethanolamine exchangeable acidity
AC_KCL potassium chloride exchangeable acidity
AL_AO acid oxalate extractable aluminum
AL_CD citrate dithionite extractable aluminum
AL_CL2 extractable aluminum in calcium chloride
AL~KCL exchangeable aluminum in potassium chloride
AL_PYP pyrophosphate extractable aluminum
ANOVA analysis of variance
AS audit samples
CA_CL exchangeable calcium in ammonium chloride
CA_CL2 extractable calcium in calcium chloride
CA_OAC exchangeable calcium in ammonium acetate
CEC cation exchange capacity
CEC_CL ammonium chloride cation exchange capacity
CECTOAC ammonium acetate cation exchange capacity
CLAY total clay fraction
CLP Contract Laboratory Program
CRDL contract-required detection limit
COS coarse sand fraction
COSI coarse silt fraction
C_TOT total carbon
DDRP Direct/Delayed Response Project
DL detection limit
DL-QCCS detection limit quality control check sample
DQO data quality objective
EGME ethylene glycol monoethyl ether
EMSL-LV Environmental Monitoring Systems Laboratory at Las Vegas, Nevada
EPA U.S. Environmental Protection Agency
ERL-C Environmental Research Laboratory at Corvallis, Oregon
FD field duplicate sample
FE_AO acid oxalate extractable iron
FE_CD citrate dithionite extractable iron
FE_CL2 extractable iron in calcium chloride
FE_PYP pyrophosphate extractable iron
FIA flow injection analysis
FP flame photometry
FSI fine silt fraction
FS fine sand fraction
GIS geographic information system
1C ion chromatography
ICP inductively coupled plasma
IDL instrument detection limit
IFB invitation for bid
K_CL exchangeable potassium in ammonium chloride
XXI
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List of Abbreviations (continued)
K_CL2 extractable potassium in calcium chloride
K~OAC exchangeable potassium in ammonium acetate
MQ_CL exchangeable magnesium in ammonium chloride
MG_CL2 extractable magnesium in calcium chloride
MG_OAC exchangeable magnesium in ammonium acetate
MOIST air-dry soil moisture
MS medium sand fraction
NA_CL exchangeable sodium in ammonium chloride
NA~CL2 extractable sodium in calcium chloride
NA~OAC exchangeable sodium in ammonium acetate
NAPAP National Acid Precipitation Assessment Program
NCC National Computer Center
NSWS National Surface Water Survey
N_TOT total nitrogen
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 water
QA quality assurance
QC quality control
QCCS quality control check sample
RS routine samples
RSO relative standard deviation
SAND total sand fraction
SAS Statistical Analysis Systems, Inc.
SBRP Southern Blue Ridge Province
SCS Soil Conservation Service
SD standard deviation
SDL system detection limit
S/H sampling class/horizon
SILT total silt fraction
SO4_0 zero mg S/L sulfate isotherm parameter
SO4_2 two mg SVL 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
SO4_32 thirty-two mg S/L sulfate isotherm parameter
SO4JH2O water-extractable sulfate
SO4_PO4 phosphate-extractable sulfate
SOW statement of work
SP SUR specific surface
S_TOT total sulfur
USDA U.S. Department of Agriculture
VCOS very coarse sand fraction
VFS very fine sand fraction
XXII
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Section 1
Introduction
Overview of the Survey
The Direct/Delayed Response Project
(DDRP) is an integral part of the Aquatic
Effects Research Program (AERP) of the U.S.
Environmental Protection Agency (EPA). The
AERP is conducted under the federally man-
dated National Acid Precipitation Assessment
Program (NAPAP) and addresses the concern
over potential acidification of surface waters
by atmospheric deposition within the United
States. The DDRP is administered by the EPA
Environmental Research Laboratory in
Corvallis, Oregon (ERL-C).
The overall purpose of DDRP is to char-
acterize geographic regions of the United
States by predicting the long-term response of
watersheds and surface waters to acidic
deposition. The DDRP has been designed
under the concept of regionalized integrative
surveys which initially is approached from a
large region of study and leads to the selec-
tion and study of regionally characteristic
systems. These systems can be assessed
through detailed, process-oriented research
which will aid in the understanding of the
underlying mechanisms responsible for ob-
served effects. The projected responses of
watershed systems typical of the regional
population can then be extrapolated to a larger
regional or national scale.
The Southern Blue Ridge Province (SBRP)
of the United States was selected for study
because of its suspected sensitivity to acidic
deposition. In defining the regions of concern,
the intent was to focus on regionally represen-
tative watersheds that are potentially sensitive
to acidic deposition and that exhibit a wide
contrast in soil and watershed characteristics
and in levels of deposition. The SBRP Soil
Survey focused on the Blue Ridge Mountains
geographic area in eastern Tennessee, north-
central Georgia, northwestern South Carolina,
and western North Carolina. Special interest
watersheds in North Carolina and Virginia were
also sampled as part of the survey.
The EPA is assessing the role that
atmospheric deposition of sulfur plays in
controlling long-term acidification of surface
waters (EPA, 1985). Recent trend analyses
have indicated that the rate of sulfur deposi-
tion is slowly declining in the Northeastern
United States but is increasing in the South-
eastern United States. If a "direct" response
exists between increasing sulfur deposition
and decreasing surface water alkalinity, then
the impact of current effects on surface water
probably would increase with increasing levels
of deposition, and conditions could improve if
the levels of deposition decline. If surface
water chemistry changes in a "delayed" man-
ner, e.g., due to chemical changes in the
watershed, then future changes in surface
water chemistry (even with stable or declining
rates of deposition) become difficult to predict.
This range of potential effects has clear and
significant implications to public policy deci-
sions on sulfur emissions control strategies.
Specific goals of DDRP are to (1) define
physical, chemical, and mineralogical charac-
teristics of the soils and define other water-
shed characteristics across the regions of
concern, (2) assess the variability of these
characteristics, (3) determine which of these
characteristics are most strongly related to
surface-water chemistry, (4) estimate the
relative importance of key watershed pro-
cesses in controlling surface water chemistry
across the regions of concern, and (5) classify
the sample of watersheds with regard to their
-------
response to sulfur deposition and extrapolate
the results from the sample of watersheds to
the regions of concern.
A variety of data sources and methods
of analysis will be used to address the objec-
tives of DDRP. In addition to the data col-
lected 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]
• Adirondack Watershed [whole water-
shed chemistry]
• 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 will be used in
the data analysis. The data that are collected
will be analyzed at +hree levels:
• Level I - System description and
statistical analysis
• Level II — Single factor response-time
estimates
• Level III - Dynamic systems model-
ing
Field and laboratory data collected in
DDRP are included in the Level I system de-
scription. Next, these data are used in Level II
to develop single factor estimates of the
response time of watershed properties, e.g.,
sulfate adsorption capacity, to acidic deposi-
tion. The detailed data from special interest
watersheds are used in Level III to calibrate
three dynamic simulation models, MAGIC
(Cosby et al., 1984), ILWAS (Chen et al., 1984),
and Trickle-Down (Schnoor et al., 1984), that
predict regional responses to acidic deposition.
The soil sampling task leader at ERL-C
had overall responsibility for the soil mapping
and sampling, including quality assurance/-
quality control (QA/QC) for site selection, soil
characterization, and collection of bulk sam-
ples and clods. Logistical support and analyti-
cal QA/QC services were provided by the EPA
Environmental Monitoring Systems Laboratory
in Las Vegas, Nevada (EMSL-LV). There were
nine sampling crews, each consisting of three
to four soil scientists, involved in the SBRP
sampling phase. In addition to collecting 5.5-
kilogram routine soil samples, each sampling
crew collected one duplicate sample per day
for QA purposes. Details of the soil mapping
and sampling are contained in a separate QA
report (Coffey et al., 1987).
As part of the DDRP, two preparation
laboratories were established in the SBRP
region to process soil samples collected by the
sampling crews and to perform preliminary
analyses on these samples. The preparation
laboratories were located within the soil sci-
ence departments at the following land grant
universities:
• University of Tennessee, Knoxville,
Tennessee
• Clemson University, Clemson, South
Carolina
The handling of soil samples at each
preparation laboratory is discussed in a sepa-
rate QA report (Haren and Van Remortel, 1987).
Bulk samples were processed, homogenized,
and subsampled. Air-dry moisture content,
percent rock fragments in the 2- to 20-milli-
meter fraction, and inorganic carbon were
determined. In addition, the bulk density of
replicate soil clods was estimated. Approxi-
mately 500-gram analytical samples were
derived from the homogenized air-dry bulk soil
samples. The analytical samples were
grouped into batches and were randomized
within each batch. Field duplicates, natural
audit samples from EMSL-LV, and a prepara-
tion duplicate were placed in each batch for
QA purposes. The batches were distributed to
three analytical laboratories contracted by EPA
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The QA/QC measures were applied in
order to maintain consistency in the soil sam-
pling, preparation, and analysis protocols.
This ensured that the soil sample analyses
would yield results of known quality. The
sampling crews and preparation laboratory
personnel received training on their respective
activities. The QA personnel from EMSL-LV
and ERL-C conducted on-site systems audits
of the sampling crews, preparation labora-
tories, and contract analytical laboratories.
Weekly communication between QA personnel
and laboratory personnel was established to
identify, discuss, and resolve issues. Survey
participants attended an exit meeting held in
Park City, Utah, in July 1986. The purposes of
the meeting were to review the mapping,
sampling, and preparation activities, resolve
any remaining issues, and generate sugges-
tions for future surveys.
The integrity of the QA program affects
the ultimate quality of data derived from physi-
cal, chemical, and mineralogical analyses of
the soil samples. This level of quality enables
potential users of the data to determine
whether the data meet their specific needs. In
addition, the QA program was conceived as a
means to ensure that the data are comparable
within and across the regions of concern.
Soils were described, sampled, and processed
according to documented protocols (Bartz et
al., 1987) and the contract laboratory analyses
were conducted according to documented
protocols (Cappo et al., 1987) under three
separate EPA solicitations.
Mineralogical analyses are being per-
formed on about 10 percent of the routine
samples, including semiquantitative X-ray
diffraction and X-ray fluorescence. Data from
these analyses will be evaluated in a forth-
coming EPA report.
Organization of the Report
This document has been organized into
four main sections. The first section provides
an overview of DDRP objectives and the SBRP
analytical data base. The second section
addresses the overall QA program, its relation
to data quality assessment, and the use of
QA/QC samples during the various stages of
data collection. The third section provides
results and discussion concerning the QA data
analysis and the internal verification checks for
eight parameter groups. The fourth section
addresses the conclusions and recommenda-
tions that have been generated from these
findings, particularly in regard to issues of
concern, improvement in QA design, and prep-
aration for QA efforts in the DDRP Mid-Appala-
chian Soil Survey and other future surveys.
Data quality is discussed in terms of
detectability, internal consistency, precision,
accuracy (interlaboratory differences), repre-
sentativeness, completeness, and comparabili-
ty. The relationship of data quality achieve-
ments to the data quality objectives (DQOs)
established at the beginning of the program is
evaluated.
Description of Parameter Groups
The DDRP QA staff organized the 51
analytical parameters into nine groups and
subsequently evaluated each group independ-
ently according to the DQOs specified for the
SBRP survey. The nine parameter groups are
briefly summarized below:
(1) Moisture, Specific Surface, and Part-
icle Size - The air-dry soil moisture content is
determined in order to place all subsequent
aliquots on an oven-dry weight basis. Specific
surface is measured in mineral soils using a
gravimetric saturation method and is corre-
lated with data for cation exchange capacity,
sulfate adsorption and desorption, and clay
mineralogy. Particle size analysis is performed
on the less than 2-millimeter size fractions of
mineral soils for characterization and classi-
fication purposes.
(2) Soil pH - The pH is an indication of
free hydrogen ion activity. The pH measure-
ments are determined in three soil suspen-
sions: deionized water, 0.01M calcium chloride,
and 0.002M calcium chloride.
(3) Exchangeable Cations in Ammonium
Chloride ~ The exchangeable cations (calcium,
magnesium, potassium, and sodium) are
extracted during the cation exchange capacity
(CEC) determinations and can be used to
calculate the percent base saturation of the
soil and to define selectivity coefficients and
cation pools for the DDRP models.
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(4) Exchangeable Cations in Ammonium
Acetate - The exchangeable cations (calcium,
magnesium, potassium, and sodium) are
extracted during the CEC determinations and
can be used to calculate the percent base
saturation of the soil and to define selectivity
coefficients and cation pools for the DDRP
models.
(5) Cation Exchange Capacity and
Exchangeable Acidity - The CEC indicates the
ability of a soil to adsorb cations, especially
the exchangeable basic cations mentioned
above. The CEC is highly correlated with the
buffering capacity of the soil. Two saturating
solutions for the exchangeable cation com-
ponent are used: buffered ammonium acetate
solution to measure "total" CEC and neutral
ammonium chloride solution to measure "effec-
tive" CEC. Exchangeable acidity is a measure
of the exchangeable cations, i.e., hydrogen and
aluminum, that are held on a soil particle
surface, in contrast to the active acidity of
these cations in solution. Two methods of
analysis for acidity are used: a buffered bar-
ium chloride triethanolamine extraction and a
neutral potassium chloride extraction. The first
method is a back-titration which indicates
"total" exchangeable acidity, including alumi-
num. The second method is a direct titration
which estimates "effective" exchangeable
acidity. Exchangeable aluminum was also
determined in potassium chloride.
(6) Extractable Cations in Calcium Chlo-
ride -- Lime potential [pH - 1/2 pCa] is used in
lieu of base saturation as an input for certain
predictive models. Aluminum potential [3pH -
pAI] is another important characteristic for
watershed modeling. The soil is extracted with
0.002M calcium chloride and analyzed for
calcium and aluminum concentrations. The
magnesium, potassium, sodium, and iron
concentrations also are determined and are
compared to cation concentrations in other
extracts.
(7) Extractable Iron and Aluminum - The
presence of iron and aluminum is highly cor-
related to sulfate adsorption. Each of three
extractions yields an estimate of a specific
iron or aluminum fraction: sodium pyrophos-
phate which estimates organic iron and alumi-
num; acid oxalate which estimates organic iron
and aluminum plus sesquioxides; and citrate-
dithionite which estimates nonsilicate iron and
aluminum.
(8) Extractable Sulfate and Sulfate
Adsorption Isotherms -- Sulfate is determined
in two different extracts: deionized water,
which estimates interstitial and loosely-bound
sulfate; and 500 mg P/L as sodium phosphate,
which estimates the readily extractable sulfate
on the anion exchange sites. The ability of
soil to adsorb sulfate is related to anion
adsorption capacity. Isotherms are developed
by placing soil samples in six magnesium
sulfate solutions of different concentrations:
0, 2, 4, 8, 16, and 32 mg S/L A determination
is made of the amount of sulfate remaining in
solution after one- hour contact with the soil
and subtraction yields the net sulfate sorption.
The isotherms represent the maximum "stable"
sulfate adsorption capacity of the soil under
laboratory conditions and are used to predict
changes in sorbed and dissolved sulfate as a
result of altered deposition.
(9) Total Carbon, Nitrogen, and Sulfur --
Total carbon and nitrogen are closely related
to the type and amount of soil organic matter.
Total sulfur is used as a benchmark to monitor
future inputs of anthropogenic sulfur.
Description of Parameters
Throughout this document, parameters
are referenced either by a data-variable or
descriptive parameter name. A list of data-
variable parameters and their corresponding
descriptions based on a similar presentation in
Turner et al. (1987) is given in Table 1-1. The
order of the parameters is consistent with
their order of presentation in this report.
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Table 1-1. Analytical Parameters Measured In the Southern Blue Ridge Province Soil Survey
Parameter Description of Parameter
MOIST Percent air-dry soil moisture measured at the analytical laboratory and expressed as a percentage on an
oven-dry weight basis. Mineral soils were dried at 105'C, organic soils at 60*C.
SP SUR Specific surface area determined by a gravimetric method of saturation with ethylene glycol monoethyl ether
(EGME).
SAND Total sand is the portion of the sample with particle diameter between 0.05 mm and 2.0 mm. It was
calculated as the summation of percentages for individual sand fractions: VCOS + COS + MS + FS + VFS.
VCOS Very coarse sand is the sand fraction between 1.0 mm and 2.0 mm. It was determined by sieving the sand
which had been separated from the silt and clay.
COS Coarse sand is the sand fraction between 0.5 mm and 1.0 mm. It was determined by sieving the sand
which had been separated from the silt and clay.
MS Medium sand is the sand fraction between 0.25 mm and 0.50 mm. It was determined by sieving the sand
which had been separated from the silt and clay.
FS Fine sand is the sand fraction between 0.10 mm and 0.25 mm. It was determined by sieving the sand
which had been separated from the silt and clay.
VFS Very fine sand is the sand fraction between 0.05 mm and 0.10 mm. It was determined by sieving the sand
which had been separated from the silt and clay.
SILT Total silt is the portion of the sample with particle diameter between 0.002 mm and 0.05 mm. It was
calculated by subtracting from 100 percent the sum of the total sand and clay.
COSI Coarse silt is the silt fraction between 0.02 mm and 0.05 mm. It was calculated by subtracting the fine
silt fraction from the total silt.
FSI Fine silt is the silt fraction between 0.002 mm and 0.02 mm. It was determined by the pipet method
(USDA/SCS, 1984) and was calculated by subtracting the clay fraction from the less than 0.02 mm fraction.
CLAY Total clay is the portion of the sample with particle diameter of less than 0.002 mm and is determined
using the pipet method.
PHJH20 pH determined in a deionized water extract using a 1:1 mineral soil to solution ratio and 1:5 organic soil
to solution ratio. The pH was measured with a pH meter and combination electrode.
PH_002M pH determined in a 0.002M calcium chloride extract using a 1:2 mineral soil to solution ratio and 1:10
organic soil to solution ratio. The pH was measured with a pH meter and combination electrode.
PH_01M pH determined in a 0.01M calcium chloride extract using a 1:1 mineral soil to solution ratio and 1:5 organic
soil to solution ratio. The pH was measured with a pH meter and combination electrode.
CA_CL Exchangeable calcium determined with an unbuffered 1M ammonium chloride solution. A 1:26 mineral soil
to solution ratio and 1:52 organic soil to solution ratio were used. Atomic absorption spectrometry or
inductively coupled plasma atomic emission spectrometry was specified.
MG_CL Exchangeable magnesium determined with an unbuffered 1M ammonium chloride solution. A 1:26 mineral
soil to solution ratio and 1:52 organic soil to solution ratio were used. Atomic absorption spectrometry
or inductively coupled plasma atomic emission spectrometry was specified.
K_CL Exchangeable potassium determined with an unbuffered 1M ammonium chloride solution. A 1:26 mineral
soil to solution ratio and 1:52 organic soil to solution ratio were used. Atomic absorption spectrometry
was specified.
NA_CL Exchangeable sodium determined with an unbuffered 1M ammonium chloride solution. A 1:26 mineral soil
to solution ratio and 1:52 organic soil to solution ratio were used. Atomic absorption spectrometry or
inductively coupled plasma atomic emission spectrometry was specified.
(continued)
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Table 1-1. Continued.
Parameter Description of Parameter
CA_OAC Exchangeable calcium determined with 1M ammonium acetate solution buffered at pH 7.0. A 1:26 mineral
soil to solution ratio and 1:52 organic soil to solution ratio were used. Atomic absorption spectrometry
or inductively coupled plasma atomic emission spectrometry was specified.
MGJDAC Exchangeable magnesium determined with 1M ammonium acetate solution buffered at pH 7.0. A 1:26
mineral soil to solution ratio and 1:52 organic soil to solution ratio were used. Atomic absorption
spectrometry or inductively coupled plasma atomic emission spectrometry was specified.
KJDAC Exchangeable potassium determined with 1M ammonium acetate solution buffered at pH 7.0. A 1:26
mineral soil to solution ratio and 1:52 organic soil to solution ratio were used. Atomic absorption
spectrometry was specified.
NA_OAC Exchangeable sodium determined with 1M ammonium acetate solution buffered at pH 7.0. A 1:26 mineral
soil to solution ratio and 1:52 organic soil to solution ratio were used. Atomic absorption spectrometry
or inductively coupled plasma atomic emission spectrometry was 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. A 1:26
mineral soil to solution ratio and 1:52 organic soil to solution ratio were used. Samples were analyzed for
ammonium content by one of three methods: automated distillation/titration; manual distillation /
automated titration; or ammonium displacement / flow injection analysis.
CECJDAC 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. A 1:26 mineral soil to solution ratio and 1:52 organic soil to solution ratio were used. Samples
were analyzed for ammonium content by one of three methods: automated distillation/titration; manual
distillation / automated titration; or ammonium displacement / flow injection analysis.
AC_KCL Effective exchangeable acidity determined by titration in an unbuffered 1M potassium chloride extraction
using a 1:20 soil to solution ratio.
AC_BACL Total exchangeable acidity determined by titration in a buffered (pH 8.2) barium chloride triethanolamine
extraction using a 1:30 soil to solution ratio.
AL_KCL Extractable aluminum determined by an unbuffered 1M potassium chloride extraction using a 1:20 soil to
solution ratio. Atomic absorption spectrometry or inductively coupled plasma atomic emission spectrometry
was specified.
CA_CL2 Extractable calcium determined by a 0.002M calcium chloride extraction. A 1:2 mineral soil to solution
ratio and 1:10 organic soil to solution ratio were used. The calcium is used to calculate lime potential.
Atomic absorption spectrometry or inductively coupled plasma atomic emission spectrometry was specified.
MG_CL2 Extractable magnesium determined by a 0.002M calcium chloride extraction. A 1:2 mineral soil to solution
ratio and 1:10 organic soil to solution ratio were used. Atomic absorption spectrometry or inductively
coupled plasma atomic emission spectrometry was specified.
K_CL2 Extractable potassium determined by a 0.002M calcium chloride extraction. A 1:2 mineral soil to solution
ratio and 1:10 organic soil to solution ratio were used. Atomic absorption spectrometry was specified.
NA_CL2 Extractable sodium determined by a 0.002M calcium chloride extraction. A 1:2 mineral soil to solution
ratio and 1:10 organic soil to solution ratio were used. Atomic absorption spectrometry or inductively
coupled plasma atomic emission spectrometry was specified.
FE_CL2 Extractable iron determined by a 0.002M calcium chloride extraction. A 1:2 mineral soil to solution ratio
and 1:10 organic soil to solution ratio were used. Atomic absorption spectrometry or inductively coupled
plasma atomic emission spectrometry was specified.
AL_CL2 Extractable aluminum determined by a 0.002M calcium chloride extraction. A 1:2 mineral soil to solution
ratio and 1:10 organic soil to solution ratio were used. The aluminum concentration obtained from this
procedure is used to calculate aluminum potential. Atomic absorption spectrometry or inductively coupled
plasma atomic emission spectrometry was specified.
(continued)
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Table 1-1. Continued.
Parameter Description of Parameter
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. Atomic absorption spectromelry or
inductively coupled plasma atomic emission spectrometry was 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. Atomic absorption spectrometry
or inductively coupled plasma atomic emission spectrometry was 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. Atomic absorption
spectrometry or inductively coupled plasma atomic emission spectrometry was 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. Atomic
absorption spectrometry or inductively coupled plasma atomic emission spectrometry was 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. Atomic absorption spectrometry or
inductively coupled plasma atomic emission spectrometry was 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. Atomic absorption
spectrometry or inductively coupled plasma atomic emission spectrometry was 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 1:20 soil to solution ratio. Ion chromatography
was specified.
S04 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 1:20 soil to solution ratio. Ion
chromatography was specified.
S04_0 Sulfate remaining in a 0 mg S/L solution following equilibration with a 1:5 mineral soil to solution ratio and
1:20 organic soil to solution ratio. The data are used to develop sulfate isotherms. Ion chromatography
was specified.
SO4_2 Sulfate remaining in a 2 mg S/L solution following equilibration with a 1:5 mineral soil to solution ratio and
1:20 organic soil to solution ratio. The data are used to develop sulfate isotherms. Ion chromatography
was specified.
S04_4 Sulfate remaining in a 4 mg S/L solution following equilibration with a 1:5 mineral soil to solution ratio and
1:20 organic soil to solution ratio. The data are used to develop sulfate isotherms. Ion chromatography
was specified.
S04_8 Sulfate remaining in a 8 mg S/L solution following equilibration with a 1:5 mineral soil to solution ratio and
1:20 organic soil to solution ratio. The data are used to develop sulfate isotherms. Ion chromatography
was specified.
S04J6 Sulfate remaining in a 16 mg S/L solution following equilibration with a 1:5 mineral soil to solution ratio
and 1:20 organic soil to solution ratio. The data are used to develop sulfate isotherms. Ion
chromatography was specified.
S04_32 Sulfate remaining in a 32 mg S/L solution following equilibration with a 1:5 mineral soil to solution ratio
and 1:20 organic soil to solution ratio. The data are used to develop sulfate isotherms. Ion
chromatography was specified.
C_TOT Total carbon determined by rapid oxidation followed by thermal conductivity detection using an automated
CHN analyzer. Total carbon can be used to characterize the amount of organic material in the soil.
N_TOT Total nitrogen determined by rapid oxidation followed by thermal conductivity detection using an automated
CHN analyzer. Total nitrogen can be used to characterize the organic material in the soil.
S_TOT Total sulfur determined by automated sample combustion followed by infrared detection or titration of
evolved sulfur dioxide.
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Section 2
Quality Assurance Program
Quality assurance has been defined as
"those operations and procedures which are
undertaken to provide measurement data of
stated quality with a stated probability of
being right" (Taylor, 1987). The QA/QC proce-
dures for the SBRP survey were designed to
ensure that the best possible data were col-
lected and that the quality of the data could
be evaluated and documented. These proce-
dures ir-luded the preparation of written
protocols and manuals describing: (1) soil
mapping, sampling, preparation and analysis,
(2) application of QA/QC during field and
laboratory activities, and (3) verification of the
descriptive and analytical data. The protocols
were tested and implemented in the survey.
Specific aspects of the QA program are de-
scribed in the following subsections.
Selection of Analytical
Laboratories
Specifications for the laboratory analysis
were defined during the initial development of
the QA program. The estimated number of
samples to be analyzed and the schedule of
sample collection were defined during logistics
planning. No single EPA laboratory had the
analytical capabilities or resources to provide
the required analytical services, hence, these
services were obtained through solicitations
with commercial analytical laboratories. The
Contract Laboratory Program (CLP) had al-
ready been established to support the hazard-
ous waste monitoring activities of EPA. The
use of multiple analytical laboratories, how-
ever, required that the selection and documen-
tation of analytical methods and QA activities
had to be carefully implemented and monitored
to ensure consistent and adequate perform-
ance in all laboratories. The solicitation pro-
cess involved the following activities:
• preparation of a detailed statement
of work (SOW) which defined the
analytical and QA/QC requirements in
a contractual format.
• preparation and advertisement of an
invitation for bid (IFB) to solicit ana-
lytical support.
• an evaluation of all bidders within a
competitive range to ensure that
qualified laboratories were selected.
Statement of Work
Monitoring of analytical performance at
each contractor analytical laboratory was
necessary in order to minimize data variability
both within and among the laboratories.
Although the DDRP Analytical Methods Manual
(Cappo et al., 1987) and the DDRP QA Plan
(Bartz et al., 1987) were drafted in the early
phases of the planning process, the methods
and QA/QC requirements had to be restruc-
tured in a SOW in order to obtain support
services. This involved careful review of the
analytical and logistical requirements, i.e.,
reporting and QC stipulations, to ensure their
clarity in the SOW and their ability to be satis-
fied according to contract specifications. The
primary administrative protocols in the SOW
were as follows:
• A contractor could bid on the analysis
of one or more bid lots (600 samples
per bid lot) that would be delivered to
the analytical laboratory at a maxi-
mum rate of 60 samples per week,
grouped in batches of approximately
42 samples per batch.
-------
• Delivery of the completed data pack-
age by the contractor was required
within 60 days of sample receipt for
Solicitation 1 and within 45 days of
sample receipt for Solicitations 2 and
3. An incentive for early delivery of
data and a consideration for late
delivery of data were established.
• Failure of the contractor to provide
adequate QA/QC data and deliverables
as required by the SOW resulted in a
penalty of up to 15 percent of the bid
price initially withheld. All analytical
laboratories eventually were paid the
entire 15 percent withholding after the
data were verified and any confirma-
tion/reanalysis requests were serviced.
The analytical laboratories were required
to follow the methods exactly as specified in
the SOW. The project officer was authorized
to provide technical clarifications for the con-
tractor laboratory, but contractual changes
were made only with the approval of the EPA
contract officer.
Performance Evaluations
The IFB was advertised in Commerce
Business Daily. All interested laboratories
received a set of pre-award performance
evaluation (PE) samples as the next step in
the qualification process. These laboratories
were required to analyze PE samples and to
report the results within 25 days after sample
receipt. The PE samples were intended to
represent soil samples at both the low and
high analyte concentrations expected for the
survey. Data packages received from each
laboratory were evaluated and graded on the
accuracy of analytical data as well as the
quality and completeness of the data package
using the scoring sheet provided in the DDRP
QA Plan (Bartz et al., 1987). This procedure
identified those laboratories that could not
successfully perform the analytical tasks.
All laboratories successfully passing the
PE sample evaluation were audited by EPA
representatives in order to verify the ability of
these laboratories to meet the contractual
requirements. The EPA team determined
whether or not each analytical laboratory had
adequate facilities, equipment, personnel, and
technical capabilities to analyze samples in
accordance with the SOW. These visits also
provided an opportunity to clarify contractual
specifications with laboratory personnel and to
identify deficiencies that were observed during
the PE phase.
Four laboratories successfully passed
both the performance and on-site evaluations
and were awarded contracts to provide analyt-
ical services for the SBRP survey. During the
routine analysis of samples, however, it was
determined that one of the laboratories could
not maintain the specified level of quality in
the analyses and this laboratory was eventu-
ally disqualified. The remaining samples in
archive were retrieved by QA staff and were
redistributed to two of the other three labora-
tories for analysis. Data from the disqualified
fourth laboratory have been removed from the
SBRP data bases.
Contract Solicitations
The analytical methods and associated
QA/QC protocols that were used in the SBRP
survey were selected so that the data could be
compared with other similar data bases, e.g.,
the DDRP Northeastern survey data bases.
On-site system audits and thorough evalua-
tions of analytical data ensured that the proce-
dures were followed correctly, as certain differ-
ences in methodology and reporting units oc-
curred among the three contract solicitations.
The distribution of batches among the labora-
tories, by solicitation, is outlined in Table 2-1.
Table 2-1. Distribution of Batches by Contract
Solicitation/Laboratory
Solicitation/
Laboratory Batch numbers
S1 / L3a 20602, 20608, 20609, 20610, 20611, 20612,
20613
S2 / L1 20701, 20702, 20703, 20707, 20708, 20710
S2 / L2 20614, 20704, 20705, 20706, 20709, 20711,
20712
S36 / L1 29603, 29605, 29606
S3" / L2 29601, 29604, 29607
Laboratory 3 reanalyzed the cations under Solicitation
3.
Reanalysis solicitation for batches retrieved from
disqualified analytical laboratory.
-------
Prior to beginning routine sample analy-
sis, the original contract solicitation containing
the analytical methodology was employed in
the analysis of audit sample data from three
referee laboratories. This solicitation was
modified to specify the handling of organic
samples, clarify the data reporting format, and
lower some of the contract-required detection
limits (CRDLs), as presented in Table 2-2. This
became the basis for Solicitation 1, which
required the laboratories to report both raw
and blank-corrected data. When the CRDLs
were lowered, all samples that were previously
analyzed under a higher CROL were reanalyzed
at the lower CRDL
About half of the SBRP soil samples
were analyzed under the requirements of
Solicitation 1. The principal changes in specifi-
cations for Solicitation 2 were the additional
lowering of CRDLs for the cation analyses and
the omission of a dilution step for SO4_PO4.
Reanalysis of the affected parameters was
performed on all previous samples at EPA
expense.
Solicitation 3 was initiated to allow two
of the other laboratories to provide analysis on
the samples that were retrieved from the
disqualified laboratory. Certain samples
among those retrieved underwent additional
processing at EMSL-LV in order to prepare
them for analysis, as presented in Table 2-3.
This processing consisted of rehomogenization
and relabelling of the affected samples.
Table 2-3. Soil Samples which Underwent Secondary
Processing Following Retrieval from the
Disqualified Analytical Laboratory
Batch
Sample Numbers
29601 All except 22, 27, 29, 33, 34, 35
29603 All except 4, 11, 13, 21, 25, 30, 34, 35, 37, 39
29604 All except 2, 3, 5, 6, 9, 11, 15, 17, 18, 20, 24, 27,
29, 32, 34, 38
29605 All except 3, 30, 34, 36
29606 All except 2, 7, 12, 14, 15, 19, 22, 25, 27, 30, 37,
38
29607 All except 1, 2, 3, 4, 6, 8, 11, 12, 15, 16, 18, 19, 21,
22, 24, 28
The only substantive differences between
batches analyzed under the different solicita-
tions are with the CRDLs. Sample reanalyses
have corrected all data affected by methods
changes which occurred as the survey pro-
gressed. An international interlaboratory study
is underway using DDRP audit samples to
compare analytical data from the SBRP sam-
ples to data from other methods currently in
use at soil characterization laboratories
throughout the United States and Canada.
Results of the study will be summarized in a
forthcoming report (Palmer et al., in
preparation).
Table 2-2. Contract-Required Detection Limits by Contract Solicitation
Parameter
Solicitation •
2
CA_CL, CAJDAC, CA_CL2"
MG CL, MGJDAC, MG CL2
K_Cl, K OAC, K CL2 ~
NA_CL, NAJDAC7 NA_CL2
CEC_OAC, CEC CL
AC KCL
AC_BACL
AL KCL
FEICL2, AL_CL2
FE PYP, FE_AO, FE CD
AL~PYP, AL_AO, AL'CD
SO4 H2O, SO4_P04, SO4 0-32
C_TOT, N_TOT
S TOT
0.20 mg/L
0.20 mg/L
0.20 mg/L
0.20 mg/L
0.01 meq/L
0.25 meq/L
0.40 meq/L
0.50 mg/L
0.50 mg/L
0.50 mg/L
0.50 mg/L
0.10 mg S04/L
0.005 wt %
0.01 wt %
0.05 mg/L
0.05 mg/L
0.05 mg/L
0.05 mg/L
0.01 meq/L
0.25 meq/L
0.40 meq/L
0.10 mg/L
0.05 mg/L
0.50 mg/L
0.50 mg/L
0.10 mg S/L
0.01 wt %
0.01 wt %
0.05 mg/L
0.05 mg/L
0.05 mg/L
0.05 mg/L
0.01 meq/L6
0.25 meq
0.40 meq
0.10 mg/L
0.05 mg/L
0.50 mg/L
0.50 mg/L
0.10 mg S/L
0.01 wt %
0.01 wt %
" CRDL for CA_CL2 reported as standard deviation of ten nonconsecutive blanks.
" meq for distillation/titration analysis and meq/L for flow injection analysis.
10
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Analytical Laboratory
Operations
Data Reporting
All samples received at the analytical
laboratory were checked in by a receiving clerk
who: (1) recorded on the shipping form the
date the samples were received, (2) checked
the samples and sample labels to identify
discrepancies on the shipping form, and (3)
sent copies of the shipping form to the EPA
Sample Management Office in Washington,
D.C., and to QA staff at EMSL-LV. If there
were any discrepancies or problems, such as
sample leakage or insufficient sample volume,
the QA manager was notified immediately for
instructions. The samples were refrigerated at
4°C as soon as possible and were kept under
refrigeration when not in use. After all analy-
ses were completed and the results were
checked, the samples were placed in long-term
cold storage at 4°C in the event that reanaly-
sis was requested.
Analytical data were reported according
to the protocols specified in the DDRP Analyti-
cal Methods Manual (Cappo et al., 1987). After
each sample was completely analyzed, the
results were summarized on summary data
forms. Where appropriate, the data were
annotated with the data qualifiers, or flags,
listed in Appendix A. The laboratory managers
signed each completed form to indicate that
the data had been reviewed and that the
samples were analyzed exactly as described
in the SOW. Each manager was responsible
for documenting any deviations from the SOW.
An index of the data forms used by the analyt-
ical laboratories is provided in Appendix B.
Copies of the raw data were submitted
upon request of the QA manager when poten-
tial discrepancies were found. Otherwise, all
original raw data were retained at the analyti-
cal laboratories. The raw data include data
system printouts, chromatograms, notebooks,
individual data sheets, and QC charts.
System Audits
Each analytical laboratory underwent a
minimum of two system audits, i.e., on-site
evaluations. The first audit was performed
after the laboratory had successfully analyzed
the set of pre-award PE samples or, occasion-
ally, during the PE sample analyses. The QA
manager or authorized representative evalu-
ated each of the laboratory functions that
were pertinent to the analyses; a questionnaire
was used to assist in this evaluation (see
example in Bartz et al., 1987). The auditor
summarized all observations in an audit report
and brought any discrepancies to the attention
of the laboratory manager.
The second on-site audit was conducted
after sample analyses had begun. The evalua-
tion questionnaire was completed with an
emphasis on all changes occurring since the
first audit. Data from the audit sample pairs
and QC samples received to date were re-
viewed. An audit report was written for this
and any subsequent on-site evaluations.
Daily communication was maintained
between the QA staff and the laboratories
during the periods when samples were being
analyzed. The objectives of daily communica-
tion were to assure that each laboratory was
satisfying the QC requirements and to obtain
a preliminary evaluation of data quality and
laboratory performance. This enabled the QA
auditors to become familiar with analytical
difficulties and with preliminary data, hence,
verification of the data was underway prior to
receipt of the data package by QA staff.
General Laboratory Protocols
General laboratory QC protocols included
the use of suitable laboratory facilities,
appropriate instrumentation with documented
performance characteristics, reagents and
labware of sufficient quality for the specific
purpose, and adequately trained personnel.
Documentation of the standard operating
procedures of the laboratory, a list of in-
house samples, and up-to-date QC charts
were required. The laboratories were not
required to use specific makes or models of
instruments, although recommendations were
given.
The analytical instruments for all of the
methods required some form of calibration.
For most methods, a series of standards was
analyzed and a calibration curve was derived.
The range of analyte concentrations in the
calibration standards was required to bracket
the expected analyte concentrations in the
11
-------
routine samples without exceeding the linear
dynamic range of the instrument. This range
was determined by a least squares regression
analysis (Steel and Torrie, 1960). with a
correlation requirement for concentration ver-
sus instrument response of 0.99 or greater.
Quality Assurance and Quality
Control Samples
The QA samples were used for
independently assessing data quality and for
monitoring the internal QC procedures. QA
samples differ from QC samples in that they
are submitted as blind samples to the
laboratories, i.e., their identity in the batch and
their composition are unknown to the analyst.
QA data assessment is undertaken in
statistical terms and is accomplished by the
inclusion of replicate (usually duplicate)
samples with the routine samples for analysis.
The QC samples were used to reduce
random errors and systematic errors, or to
maintain these errors within specified tolerable
limits. These samples are created and used
by the laboratories to evaluate the calibration
and standardization of instruments and to
identify problems such as contamination or
analytical interference.
Description of Quality Assurance
Samples
Three types of QA samples were used in
the SBRP survey: (1) field duplicates, (2) prep-
aration duplicates, and (3) natural audit sam-
ples. The number and percentage of QA and
routine samples used in data quality assess-
ment was as follows:
• Total QA and routine = 984 samples
• QA field duplicates = 106 samples
(11 percent of total)
• QA preparation duplicates = 26
samples (2.5 percent of total)
• QA natural audits = 104 samples
(10.5 percent of total)
• Routine = 748 samples (76 percent
of total): 704 mineral samples (94
percent of routine) and 44 organic
samples (6 percent of routine)
Field Duplicate Samples -
Each sampling crew was required lo
randomly sample one horizon in duplicate per
day, collecting alternate portions of soil for
each sample (Bartz et al., 1987). One sample
was considered to be the routine sample and
the other was designated the field duplicate.
Since more than one pedon could be sampled
on an average day, not all pedons were sam-
pled for a duplicate. A pedon is a three-
dimensional body of soil having a lateral area
large enough (1 to 10 square meters) to permit
the study of soil horizons. After processing,
the field duplicates were placed randomly with
their associated pedon samples in batches of
approximately 42 soil samples each.
Certain QA data sets utilized only the
106 field duplicates, while other analyses used
the 106 field duplicate pairs, i.e., the field du-
plicates in conjunction with their associated
routine samples. The distribution of field du-
plicate pairs among the preparation labora-
tories and the analytical laboratories is shown
in Table 2-4.
Preparation Duplicate Samples --
Each preparation laboratory selected one
routine soil sample per batch and subsampled
a duplicate sample with a Jones-type, 3/8-inch
riffle splitter. Each preparation duplicate was
placed randomly within its associated batch.
Certain QA data sets utilized only the 26 prep-
aration duplicates, while other analyses used
the 26 preparation duplicate pairs, i.e., the
preparation duplicates in conjunction with their
associated routine samples. The distribution
of preparation duplicate pairs among the ana-
lytical laboratories is shown in Table 2-5.
Natural Audit Samples --
Bulk soil samples representing five typi-
cal soil horizons of the eastern United States
were collected in large storage drums and
used as natural audit sample material. The
soil horizons represented by these samples
were Oa, A, Bs, Bw, and C horizons. Sub-
samples from each of these bulk samples
were prepared by EMSL-LV staff and were
forwarded to the preparation laboratories. The
12
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Table 2-4. Distribution of Field Duplicate Sample Pairs Among the Sampling Crews, Preparation Laboratories, and
Analytical Laboratories
Sampling crew
GA01
GA02
NC01
NC02
NC03
NC04
TN01
TN02
VA01
Sampling(total)
Preparation
Analytical
1
Clemson
10
0
5
2
4
0
0
0
0
21
A
p
UTenn
0
0
0
0
5
0
9
0
0
14
35
nalytical laborator
2
reparation laborat
Clemson
5
4
4
0
2
9
0
0
0
24
o[y
UTenn
0
2
2
0
4
0
0
7
0
15
39
3
UTenn
0
16
0
0
3
0
3
5
5
32
32
Crew
totals
15
22
11
2
18
9
12
12
5
106
Table 2-5. Distribution of Preparation Duplicate
Sample Pairs Among the Preparation
Laboratories and Analytical Laboratories
Analytical
laboratory
1
2
3
Total
Preparation laboratoj^
Clemson UTenn
6 3
6 4
.0 1
12 14
Total
9
10
_7
26
samples were randomly placed into batches at
a rate of two pairs per batch without further
handling or processing by laboratory
personnel. One of the two pairs in each batch
was always A horizon audit material. The
distribution of the natural audit pairs among
the analytical laboratories is presented in
Table 2-6.
Table 2-6. Distribution of the Natural Audit Sample
Pairs Among the Analytical Laboratories
Laboratory
1
2
3
Total
Oa
0
0
2
2
• — Audit horizon --
A Bs Bw
9
10
_7
26
5
6
_0
11
3
2
2
7
C
1
2
3
6
Total
18
20
14
52
Since the same audit material (assumed
to be homogeneous) was utilized throughout
the survey, data from the audit samples were
used to evaluate within-batch precision and
analytical differences among laboratories.
These data were also used for independent QA
comparisons to data from the analytical dupli-
cate QC samples. Additional checks on preci-
sion were made using the field duplicates and
preparation duplicates.
Sample Flow
The routine and field duplicate samples
were collected by the nine sampling crews and
were delivered to the two preparation labora-
tories. The laboratories processed and pre-
pared the samples and subsampled the prepa-
ration duplicates. Batches of soil samples
were assembled, each containing field dupli-
cates (two to six per batch, depending on the
number of pedons represented in the batch),
one preparation duplicate, and two pairs of
audit samples, with the balance of the batch
being composed of routine samples from the
pedons. The batches were distributed among
the contracted analytical laboratories for
analysis.
Description of Quality Control
Samples
Seven types of QC samples were used in
the SBRP survey: calibration blanks, reagent
blanks, QC check samples (QCCS), detection
limit QC check samples (DL-QCCS), matrix
13
-------
spikes, analytical duplicates, and ion chroma
tography (1C) standards, as described below.
Control limits were established for measure
ments of each of the QC samples. The results
from each laboratory were examined with
reference to these established limits.
One calibration blank per batch was
analyzed immediately following the initial
instrument calibration in order to detect instru-
mental drift or to test for evidence of sample
contamination. The calibration blank was
defined as a "0" concentration standard and
contained only the matrix of the calibration
standards.
For methods that -required sample prepa-
ration, e.g., soil extractions, a reagent blank
was included in each batch of samples. The
reagent blank, sometimes referred to as a
process blank, was composed of all the rea-
gents used and in the same quantities used
in preparing a soil sample for analysis This
blank underwent the same digestion and
extraction procedures as a routine sample and
was used to identify contamination of the
reagents. If the observed concentration of the
calibration blank or the reagent blank was
greater than the CRDL, the instrument vva^.
rezeroed, the calibration was checked, rind ihrj
source of contamination or error WMS :nvjsii
gated and eliminated. A blank exceeding the
CRDL for more than 25 percpnt of the samples
in a batch was cause for reanalysis of the
affected parameter.
A QCCS containing the analyte of inter-
est at a concentration in the mid-calibration
range was analyzed immediately following the
standardization of an instrument, after the
routine analysis of groups of ten samples, and
after the last sample in each batch The
QCCS was prepared from a source which WRS
independent of the calibration standards and
was used to determine the accuracy and
consistency of instrument calibration. The
control limit for the QCCS was in percent of
the theoretical value (rj percent for sulfate
parameters and 1 percent for paiticle size
parameters). When an unacceptable QCCS
value was obtained, the instalment was recali-
brated and all samples analyzed beyond the
last acceptable QCCS were reanalyzed The
QCCS samples were plotted on the QC chart
and the 95- and 99-percent confidence inlprvnls
were calculated. The 99-pprcent confident
interval, i.e., the control limit, was required to
be within the maximum control limit specified
by the QA staff.
The DL-QCCS contained the analyte of
interest at a concentration two to three times
above the CRDL (Cappo et al., 1987). The
purposes of this sample were to eliminate the
necessity of formally determining the detection
limit on a daily basis and to determine accu-
racy at the lower end of the linear dynamic
range of measurement. The measured value
of the DL-QCCS was required to be within 20
percent of the theoretical concentration. If the
difference was greater than this limit, the
source of error was identified and corrected,
and acceptable results were obtained before
initiating routine sample analysis. The CRDL
often was far below the concentration of the
lowest-level analyte, hence, discriminating the
DL-QCCS from background or instrument noise
was difficult.
One matrix spike, i.e., a known quantity
of analyte added to a sample aliquot, was
examined in each batch to determine the
sample matrix effect on the analytical labora-
tory measurements for most of the parame-
ters. The spike concentration was approxi-
mately equal to the endogenous level or ten
times the detection limit, whichever was larger,
of the analyte being measured. The volume or
weight of the added spike was required to be
negligible for the purposes of calculation.
Analytes that were extracted prior to analysis
were spiked after extraction. If there was
insufficient sample volume to spike all of the
aliquots from one sample, the matrix spike
analysis was performed on a per-aliquot basis.
If the spike recovery was not within 15
percent of the initial spike volume or weight,
two additional samples were spiked with each
of the analytes in question. The two addi-
tional samples were then analyzed and their
respective recoveries were calculated. If the
spike recovery in one or both of the samples
was riot within 15 percent, the entire batch of
samples was reanalyzed for each of the
parameters in question. The samples were
diluted or the spike level was adjusted if the
concentration of the matrix spike was not
within the linear dynamic range for the analyti-
cal method.
14
-------
One soil sample per batch was sub-
sampled and analyzed in duplicate by the
analytical laboratories. This QC sample, the
analytical duplicate, was used in estimating
the within-batch precision for each analytical
laboratory and for identifying significant instru-
mental drift. The percent relative standard
deviation (RSD) of each analytical duplicate
pair, i.e., the duplicate and its companion
routine sample, was calculated by dividing the
standard deviation of the pair by the mean of
the pair and multiplying this value by 100. If
the RSD and the mean concentration of an
analytical duplicate pair were greater than 10
percent and ten times the CRDL, respectively,
then an explanation for the discrepancy was
sought and another duplicate sample was
analyzed. Routine sample analyses were
stopped until instrumental control was re-
stored, unless permission to proceed was
obtained from the QA manager.
An 1C resolution test was performed
once per analytical run by analyzing a standard
that contained approximately equal concentra-
tions (1 mg/L) of sulfate and nitrate ions. If
the resolution did not exceed 60 percent, the
column was replaced and the resolution test
was repeated.
Data Verification
Overview of Data Bases
The field sampling data and the analyti-
cal data were entered into the SBRP data base
using a compiled dBase III entry system at
Oak Ridge National Laboratory (ORNL) in Oak
Ridge, Tennessee. These data also were sent
to the QA staff at EMSL-LV for concurrent
data verification. All data were double-entered
into data sets and were visually checked,
thereby allowing errors in transcription to be
identified and removed. The data bases
progressed through three stages: (1) raw data
base, (2) verified data base, and (3) validated
data base.
The raw data base contains the data
that were entered directly from the field data
forms and analytical data packages through
double entry by ORNL and EMSL- LV. The two
entries were compared and discrepancies were
corrected so that the data sets were identical.
One version was discarded and the other was
frozen to become the official raw data base.
A magnetic tape of this data base was sent to
the National Computer Center (NCC) in
Research Triangle Park, North Carolina, where
the data tape was uploaded and made acces-
sible to the QA staff.
Verification of the raw data base was
accomplished by a systematic evaluation of
completeness, precision, consistency, and
coding accuracy. Discrepancies were flagged
unless they could be corrected. After verifica-
tion was completed, the data base was frozen
and became the verified data base. A magnet-
ic tape was generated and was sent to ORNL.
The verified data base underwent addi-
tional evaluation through a process called
validation. The validation procedures included
specific assessment of outlying data points for
inclusion or omission in data sets based on
assigned levels of confidence. These data
warrant special attention or caution by the
data user during analysis of the survey results.
After the data were evaluated and the suspect
values were confirmed or flagged, the data
were frozen as the validated data base (Turner
et al., in preparation).
Verification of Field Data
After locating specified sampling sites in
designated watersheds, the sampling crews
excavated, characterized, and sampled soil
pedons representing the desired sampling
classes (Coffey et al., 1987). The pedons were
characterized and sampled by United States
Department of Agriculture (USDA) Soil Conser-
vation Service (SCS) soil scientists who con-
sistently utilized SCS computer-coded field
data forms (SCS-SOI-232 forms) to record the
soil descriptions. Use of the field data forms
allowed sampling crews to gather comparable
data for each pedon.
The completed field data forms were
sent to ORNL where the data were entered
into two data base subsets. The 232BA
subset includes data from the first page of the
field data form concerning general site and
pedon characteristics. The 232HO subset
includes data from the second, third, and
fourth pages of the field data form concerning
specific characteristics of the individual soil
horizons. As ORNL staff were double-entering
15
-------
the raw data, QA staff at EMSL-LV were
reviewing the data for outliers. An anticipated
computer verification system to check field
parameters for coding accuracy and complete-
ness was not available, therefore, the data
were reviewed manually.
Raw data were evaluated for each sam-
pling crew. Outliers were identified and placed
on discrepancy forms (see Appendix B) which
were sent to the appropriate SCS state office
for confirmation. The individual sampling
crews reviewed these forms and entered either
the corrected values or a notation indicating
that the requested information could not be
discerned. In either case, all outliers identified
on the form were addressed and initialed by
the reviewer. The discrepancy forms were
returned to the QA staff, who edited the cor-
rections on the original field data forms. In
all, three separate sets of discrepancy forms
were sent to the sampling crews during data
verification. Because of the hand-checking
procedure, various outliers were overlooked
during the initial review but surfaced during the
second or third reviews.
When the raw data base became acces-
sible to QA personnel, a set of procedures for
entering and editing the data base was em-
ployed. Editing was accomplished on a
working copy of the official raw data base
supplied by ORNL. All changes were made on
this data base through a special editing pro-
gram, thereby protecting the official raw data
base. A subset of the raw data base was
keyed into this analysis program. The subset,
sorted by state, was moved into a temporary
working file and underwent manual editing.
After completion of editing, the manual system
was exited and a transaction file of both
edited and original data was created automati-
cally. At the end of each editing session, the
transaction file was printed and reviewed.
After the edits were verified, the local
master data base was updated with the edited
information in the transaction file. This infor-
mation also entered a history file, which
recorded all transactions made on the local
master data base. The verified master data
base was completed in October 1987.
ORNL personnel ran a thorough check by
comparing data on the tape with the original
field data forms. Occasional entry and editing
errors were discovered and, after correction, a
second tape was generated. It was decided
that QA staff would make no further edits on
the official verified data base. Subsequent
discoveries of outliers were jointly discussed
and documented by EMSL-LV and ORNL
Further changes in the data base were made
only upon written confirmation by the QA staff.
Additional tests were performed on the
verified data which generated a small set of
outliers. Discrepancy forms identifying these
outliers were sent to the appropriate SCS
state and field offices for confirmation. A new
list of edits was compiled by QA staff and
was sent to ORNL for entry. EMSL-LV also
entered the edits into a working file that was
maintained by the QA staff. Comparisons of
the ORNL and EMSL- LV files were made to
evaluate completeness and consistency of the
edits.
Verification of Analytical Data
Analytical data reported on 100-level data
reporting forms and 200-level blank-corrected
data forms were entered into a data set by
ORNL. A magnetic tape of the data was
added to the catalogue file at NCC, where it
was loaded for remote access by the QA staff.
Exceptions programs, used to highlight dis-
crepancies in the data sets, were applied in
the data quality assessment.
The steps identified below were estab-
lished to identify and correct suspected data
errors. Information obtained by this process
was used to edit data on the magnetic tapes
sent by ORNL. New data and flags were
entered into the raw data set to correct or flag
the original data.
Review of Data Packages --
When data packages were received from
the analytical laboratories, the QA staff
checked to ensure that the correct sequence
and number of forms were submitted and that
each form contained data for all samples in
the batch. The laboratory manager's signature
and the date of analysis was confirmed on
each form. Each data package was then
subjected to the following QA/QC checks:
16
-------
• Audit data were evaluated with the
data verification template (see Appen-
dix B).
• The RSDs of all duplicate pairs were
checked.
• Standard analyte relationships were
evaluated.
• Blank concentrations were checked for
compliance, i.e., less than the CRDL
as outlined previously in Table 2-2.
• Instrument detection limits (IDLs)
were checked for compliance, i.e., less
than their corresponding CRDLs.
• Matrix spikes were checked for com-
pliance in preparation, i.e., concentra-
tions were ten times the CRDL or
twice the endogenous level, whichever
was greater; data were checked to
ensure a spike recovery within 15
percent of the original spike
concentration.
• QCCS data were checked for com-
pliance, i.e., values within the calcu-
lated control limits.
• Reported and blank-corrected data
were checked for proper calculations.
The QA staff compiled verification reports
for each batch data package submitted. A
response letter was sent to each laboratory
after data package evaluation describing
potential discrepancies within the reported
data and occasionally suggesting where errors
may have occurred, e.g., transposed numbers
or erroneous dilution factors. Through use of
the Form 500 (see Appendix B), the labora-
tories were required to respond promptly with
confirmation or reanalysis of the parameters in
question. Reanalysis was performed on whole
batches of samples rather than individual
samples.
Compliance for Quality Control Check
Samples --
Analysis of QCCS was not required on
the titrimetric analytical methods used to
determine the CEC and exchangeable acidity
parameters. For the other analyses, the QCCS
sample concentrations were formulated to
represent an approximate mid-range of the
routine samples. The QCCS data were used
to verify the analytical consistency of the
laboratories.
The chemical characteristics and concen-
trations of the QCCS were known to the
analytical laboratories, hence, it was expected
that the observed values of the QCCS would
be within 10 percent of their respective theoret-
ical values. Due to the importance of the
sulfate analysis to DDRP, the observed values
were required to be within 5 percent of the
theoretical values. The QCCS observations
outside of these ranges are tabulated in
Table B-1 of Appendix B. The application of a
Type I error equation (Aronoff, 1984) generated
a list of QCCS values whose compliance was
estimated at the 0.05 significance level. The
large number of QCCS samples outside of the
range for the particle size classes suggests
that the control limits were too tight for this
parameter group. Other low concentration
parameters were also susceptible to falling
outside of the range.
Standard Relationships -
The audit pairs and the field, preparation,
and analytical duplicates were used in the
preliminary QA/QC assessment. The QA
acceptance criteria, i.e., audit windows, initially
were calculated for each of the parameters as
the 95-percent confidence interval of audit
sample data from the DDRP Northeastern Soil
Survey. The audit sample windows were
updated periodically on the basis of incoming
data from the analytical laboratories. Audit
pairs were first checked for their inclusion
within the audit windows. Precision of each
pair was estimated by calculating the percent
RSD, with less than 10 percent being accept-
able if the mean of the pair was greater than
ten times the CRDL.
The natural audit pairs were also
checked for consistency as set forth in the
following standard analyte relationships:
• Particle Size Analysis: SAND + SILT
+ CLAY = 100
The summation of total sand, silt, and
clay fractions in mineral soil samples
should equal 100 percent ±0.1 percent.
17
-------
Also, samples labeled as organic soils
are checked for having 12 percent or
more organic carbon.
• Soil pH: PH_H2O > PH_002M >
PH_01M
Calcium ions in the calcium chloride
extracts displace hydrogen ions by
mass action on the exchange sites,
thereby increasing the hydrogen con-
centration in the soil solution relative
to that of the water extract. A higher
concentration of calcium will more
effectively displace hydrogen ions and
will result in a lower pH.
• Cation Exchange Capacity (CEC):
CEC_OAC > CEC_CL
Ammonium in a buffered (pH 7.0)
ammonium acetate solution displaces
other cations from exchange sites.
This method was used in conjunction
with AC_BACL to establish a theoret-
ical maximum for CEC in the soil.
Ammonium in an unbuffered ammoni-
um chloride solution provides a more
accurate estimation of the actual CEC
of the soil when included with
AC_KCL Generally, the CEC in
amThonium chloride is less than the
CEC in ammonium acetate (excep-
tions include soils with very low CEC
or high pH).
• Exchangeable Acidity: AC_BACL >
AC_KCL
A buffered (pH 8.2) barium chloride
triethanolamine solution was used to
assess the total potential acidity. The
unbuffered potassium chloride method
estimates the actual exchangeable
acidity in soils. Generally, the
exchangeable acidity in potassium
chloride is less than that in barium
chloride triethanolamine (exceptions
include some coarse-textured or low
CEC soils).
• Extractable Sulfate:
SO4 H2O
SO4 PO4 >
The phosphate anion, because of its
size and chemical properties, readily
exchanges with the sulfate anion.
The phosphate extraction gives an
indication of the total exchangeable
sulfate in the soil. The water extrac-
tion measures only those sulfate ions
that are easily displaced and is an
accepted indicator of available sulfate
in the soil. Generally, the sulfate
concentration in the water extraction
is less than in the phosphate extrac-
tion (exceptions include some soils
with low sulfate adsorption or high
organic matter).
• Sulfate Isotherms: SO4 0 < 2 < 4 <
8 < 16 < 32
The isotherm relationship is a re-
sponse to increased concentrations of
sulfate and should advance in a linear
fashion until the threshold of sulfate
adsorption is reached.
Internal Consistency --
Most of the verification checks and
evaluations of analytical laboratory measure-
ments were performed on data from QA sam-
ples and from analytical QC samples.
Although an assessment of data quality could
be drawn from these samples, the QA staff
decided that an additional evaluation was
needed to identify specific errors in the data
from the routine soil samples. The purpose of
this evaluation was to identify values for each
analytical parameter that were not consistent
with the majority of values observed. These
values were checked for errors in transcription,
data entry, or editing. If no discrepancies
were encountered, these data values were
qualified, or "flagged", as routine data outliers
with an "X" flag (see Appendix A). Time did
not permit the QA staff to identify the cause
of all outliers, nor was it feasible to confirm
the accuracy of outliers with the laboratory
personnel.
An internal consistency program created
at ERL-C was used to identify the routine data
outliers (D. L Cassell, unpublished data). The
first step was to correlate analytical data for
each parameter with all other analytical
parameters measured in the SBRP survey.
The strongest correlations, based on the co-
efficient of determination (r2), were investi-
gated. When the r2 value generated by the
18
-------
correlation of one parameter with another was
greater than about 0.80, the correlation having
the highest r2 was selected and the internal
consistency computer program was applied to
all of the routine data points. If r for the
highest correlations was less than 0.80, a
separation of data between organic and min-
eral samples was used in order to ascertain
whether or not the groupings had an effect on
the correlation. The correlation was used if r2
increased significantly after the organic and
mineral samples were correlated separately.
In some cases, the values for one
parameter did not correlate well with values
for any other parameter. In these cases, a
percentage of the highest and the lowest
values for that parameter were checked for
errors. Correlations were not performed on
parameters within the same extract or from
the same measurement, e.g., CA_OAC values
were not correlated with MG_OAC values even
though the resulting r2 value" had the highest
value. The reason for this decision is that it
was recognized that certain errors, e.g., incom-
plete extraction, would not be identified by
performing correlations within the same ex-
tracting solution. Although correlations were
performed for particle size parameters, the
highest and lowest values in each particle size
class were also checked.
The internal consistency program was
designed using a weighted linear regression
model (SAS, 1986) because the data exhibited
heteroscedasticity, i.e., the variances were not
the same for the entire population. The
weighting factor (w) which was used in the
regression was calculated as the reciprocal of
the analyte concentration of the independent
variable (w = 1 / x). The correlation was run
by plotting values for one parameter on the X-
axis and values for another parameter on the
Y-axis. Outliers were defined as those points
having a studentized residual (Belsley et al.,
1980) of 3.0 or greater. The X- and Y-axes of
each parameter then were reversed and the
regression was repeated. The results from
both regressions were combined in order to
identify the outliers.
For each regression, the studentized
residual was calculated by subtracting the
regression estimate of the dependent variable
from its corresponding observed value and
dividing by the estimated standard error of the
residual, as follows:
where:
ith value of the dependent
variable
ith predicted value of the
dependent variable by the
regression equation
S(i) = standard error estimated
without the ith observation
hi = ith leverage factor
The studentized residual is an appro-
priate robust technique used to investigate
outlying data points. A possible limitation in
the capability of the studentized residual to
determine an outlier was that the outlier itself
strongly influenced the regression estimates of
the slope or intercept, thereby abnormally
affecting the value of the residual. Another
outlier measurement technique involved the use
of a DFFITS statistic (Belsley et al., 1980),
which was used to measure the change in the
predicted value resulting from the exclusion of
a specific observation in the regression analy-
sis. The DFFITS statistic was used to exam-
ine the significance of large differences in
residuals and was calculated as follows:
(Y, -
where: y:
y(i)
ith predicted value with the
current observation
included
ith predicted value with the
current observation
excluded
S(i) = standard error estimated
with the ith observation
excluded
h(l) = leverage factor with the ith
observation excluded
As in the studentized residual, division by
the estimated error normalized 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 tended to have a high
19
-------
DFFITS value. The critical point which was
used to define a high value, i.e., the critical
DFFITS, and its corresponding outlier was
calculated as follows:
2 • [(m -t- 1) + n]
1/2
where: m = number of independent
variables
n = number of points or obser-
vations regressed
A data outlier was identified as any data
point exceeding the critical values which had
been defined for the studentized residual or
DFFITS statistic. These points were temporar-
ily removed from the set of observations being
analyzed. Using the remaining data, a second
regression was performed on the same param-
eters. Utilizing the regression equation, i.e.,
slope and intercept estimates, from the sec-
ond regression performed and the mean and
corrected sum of squares from data points
defined as outliers in the first regression, a
residual test was performed to examine and
return data outliers to the set of "good" or
viable data points. Any outliers that failed to
pass this test were considered outliers and
underwent additional internal consistency
checks. Results were checked for accuracy in
transcription against the values in the data
package and, where necessary, corrections
were made.
After edits were made in the data base,
the internal consistency program was repeated
and a second set of outliers was generated.
Any new outliers which appeared in the second
correlation were checked for accuracy. No
errors in transcription were found in the
second regression.
Table B-2 in Appendix B contains a list of
correlations that were performed for each
parameter, the parameter groups, and the r2
values for the first and second correlations.
Most of the correlations resulted in r2 values
greater than 0.80. When correlations were
performed for the sulfate isotherms and for
total sulfur/nitrogen, it was observed that a
disproportionate percentage of the outliers
were organic samples having high variability.
Separating the organic horizons from the
mineral horizons aided in identifying mineral
soil outliers.
The following types of errors in the SBRP
data base were identified by the internal
consistency checks and were subsequently
confirmed or corrected:
• Data entry errors: values from the
analytical laboratory data packages
that were entered incorrectly.
• Transcription errors: data that were
transposed or transcribed at the
analytical laboratories incorrectly, e.g.,
pH 5.34 instead of 3.54; most of these
suspect values had been identified
earlier and confirmation requests were
sent to the laboratories, where the
values were corrected, although the
values had missed the editing loop.
• Batch errors: systematic or sporadic
calculation errors that were discovered
when most or all of the data in specif-
ic batches was outlying.
• Laboratory errors: systematic or
sporadic calculation errors that were
discovered when some or all of the
data in batches from a specific labo-
ratory was outlying.
Data Quality Objectives
To address the DDRP objectives, con-
clusions must be based on scientifically sound
interpretations of the data base. To achieve
this end, the EPA requires all monitoring and
measurement programs to have established
objectives for data quality based on the pro-
posed end uses of the data (Blacker et al.,
1986). Computer models are being used to
predict results and hypotheses have been
developed to test the models. The utility of
the data, and thus the project itself, is defined
by the ability to confirm, reject, or discriminate
between hypotheses. The primary purpose of
the QA program is to increase the likelihood
that the resulting data base meets or exceeds
specific DQOs. Through the proper develop-
ment of DQOs, the quality of data can be
quantified, thereby allowing the data user to
differentiate hypotheses. In practice, DQOs
are statements of the levels of uncertainty that
a data user is willing to accept in the results
derived from the data.
20
-------
The DQOs for the SBRP survey were
established for detectability, precision, repre-
sentativeness, completeness, and compara-
bility. Due to the naturally low analyte concen-
trations in the soils under investigation, con-
tract-required detectability standards were
established to further enhance interpretability
of the data base. The DQOs for precision are
quantitative criteria that were developed for
specific components of the data collection
activities and measurement system used in the
survey. The DQOs for representativeness,
completeness, and comparability were some-
what qualitative in nature and were assessed
primarily by the research design and selection
of methodologies. There were no DQOs estab-
lished for accuracy, although an attempt has
been made in this report to relate accuracy
considerations to interlaboratory differences.
Detectability
An important factor to consider in the
evaluation of data quality is the detection limit,
which is the lowest concentration of an ana-
lyte that an analytical process can reliably
detect. The primary consideration is whether
or not a measured sample value can be con-
sidered significantly different than the meas-
ured value of a sample blank. The probability
that an analytical signal is not simply a ran-
dom fluctuation of the blank is dependent on
how many standard deviations the analytical
signal varies from the mean value of blank
responses (Long and Winefordner, I960). The
specific application of detectability in the SBRP
survey required the investigation of precision in
low concentration samples.
A commonly recognized value for the
detection limit is three times the standard
deviation of the blank samples (American
Chemical Society, 1983). A signal measured at
this level or greater would have less than a 0.1
percent chance of being the result of a random
fluctuation of the blank, assuming the blank
samples have a normal distribution. In the
absence of blank samples, low concentration
replicate samples are often used to estimate
the standard deviation expected of blank
samples. Although liquid blank samples have
been used extensively in the aquatics surveys
of the National Surface Water Survey, to date
it has been unknown how to develop a soil
blank suitable for system-wide use in DDRP.
With this in mind, the following three types of
detection limits are described in this report.
(1) All analytical laboratories were
required to satisfy the contract-required detec-
tion limit (CRDL) for specified parameters, as
presented in Table 2-7. The CRDLs were
established for instrument readings in the
analytical phase only.
(2) A calculated instrument detection
limit (IDL) was used to estimate the lowest
concentration of an analyte that the analytical
instruments used by the laboratories could
reliably detect. Although IDLs were calculated
from analytical blank samples and were
reported by the laboratories, these values are
not included in this report. Instead, an inde-
pendent check of the IDLs was possible by
examining the variability in the DL-QCCS sam-
ples and by assuming that the variability of
this low level sample should have been about
the same as that of the blank samples. The
IDLs reflect variability in the analytical phase
only.
(3) It is recognized that laboratory
analysis is only one of many steps in the
overall process of generating raw data for a
soil sample collected from the field. If it were
possible to route "soil blank" samples with
zero concentration of analyte through the
sampling crews and subsequently through all
phases of the measurement system, a system
detection limit (SDL) could be estimated.
Overall variability in the blank sample would
encompass variability in sampling, preparation,
extraction, and analysis, and would include
sample contamination at any of these steps.
Calculation of a SDL from such a sample
would allow a data user to identify when any
given soil sample had a measured concentra-
tion that could be considered as statistically
different from that of a reagent blank or cali-
bration blank. For this report, reasonable
substitutes for blank samples are the field
duplicates which are routed through the major
components of the measurement system and
exhibit many of the features that would be
expected in soil blanks. By selecting the field
duplicates of least concentration, e.g., the
lowest 10 percent of the duplicates, the result-
ing variability would be expected to parallel
that of system-wide blanks.
21
-------
Table 2-7. Data Quality Objectives for Detectablllty and Analytical Wlthln-Batch Precision
Parameter
MOIST
SP SUR
SAND*
SILT*
CLAY
PH H2O
PH 002M
PH~01M
CA CL
MG CL
K CL
NA_CL
CA OAC
MG OAC
K OAC
NA_OAC
CEC CL
CEC~OAC
AC KCL
AC BACL
ALJCCL
CA CL2
MG CL2
K C"L2
NA CL2
FE CL2
ALICL2
FE PYP
AL PYP
FE AO
AL AO
FE CD
AL_CD
SO4 H2O
SO4 PO4
SO4~0-32
C TOT
N~TOT
S"tOT
Reporting
units
wt %
m2/g
wt %
ii
n
pH units
n
11
meq/100g
"
11
11
meq/100g
11
n
"
meq/100g
11
ii
11
n
meq/100g
n
11
"
11
11
wt %
n
11
11
11
11
mg S/kg
11
mg S/L
wt %
"
ii
__ — ppn
units
—
—
—
—
—
0.003
0.011
0.003
0.006
0.006
0.011
0.006
0.006
0.002
0.002
0.11
0.75
0.80
0.0007
0.0002
0.0004
0.0005
0.0001
0.005
0.005
0.005
0.005
0.002
0.002
2.0
2.0
0.10
0.01
0.01
0.01
L«
mg/L
—
—
—
...
—
...
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
O.Ol""
o.oi01'"
0.40s
0.25"
0.10
... f
0.05
0.05
0.05
0.05
0.05
0.50
0.50
0.50
0.50
0.50
0.50
0.10
0.10
0.10
0.010s
0.010s
0.010s
lower (SO)
1.0
1.0
1.0
0.15
0.15
0.15
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.25
0.25
0.50
0.50
0.50
—
—
—
—
...
0.05
0.05
0.05
0.05
0.05
0.05
1.0
1.0
0.05
0.05
0.01
0.01
- Prftf*icirtn — ™™. __
1 lOVrflOHJI 1 ™™ — — .
upper (RSD)
—
—
...
—
...
15%
15%
15%
15%
15%
15%
15%
15%
10%
10%
20%
20%
20%
5%
10%
10%
10%
10%
10%
15%
15%
15%
15%
15%
15%
10%
10%
5%
15%
10%
10%
knot
—
...
—
...
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
2.5
2.5
2.5
2.5
2.5
—
—
—
—
...
0.33
0.33
0.33
0.33
0.33
0.33
10.0
10.0
1.0
0.33
0.10
0.10
" Contract-required detection limit in reporting units and parts per million, respectively.
b Precision objectives below and above the knot separating the lower tier (standard deviation in reporting units) and
the upper tier (relative standard deviation in percent); the knot is in reporting units.
c DQOs were not established for size fractions of this parameter.
a Units are meq/L for this parameter for flow injection analysis.
* Units in meq for this parameter for titration.
' CRDL reported as standard deviation of ten nonconsecutive blanks.
9 Units are weight percent (wt %) for this parameter.
22
-------
The calculated IDL was estimated as
three times the pooled standard deviation of a
low level DL-QCCS. The SDL was estimated
as three times the pooled standard deviations
of the lowest ten percent of all field duplicate
pairs. These limits, together with the CRDL
and the converted IDL (the calculated IDL in
comparable reporting units), are given in the
results and discussion. The effect of adjusting
CRDLs for certain parameters during the
course of the survey is also examined.
An important factor to consider in the
evaluation of detectability is the implication of
the calculated detection limits for data quality
of the routine data set. By estimating the
percentage of data from the routine samples
that were greater than the corresponding
SDLs, specific parameters were identified that
might not have been measured with sufficient
precision to satisfy the requirements of data
users. This was not necessarily the result of
improper CRDLs, for it is evident that the
instrumental error was the source of only a
small portion of the variability in the low
concentration field duplicates used to estimate
the SDLs.
Precision
Development of the Precision
Objectives -
The precision DQOs for the SBRP survey
were established for analytical within-batch
precision of most of the physical and chemical
parameters listed in Table 1-1 of Section 1.
There were no specific DQOs established for
the sampling or preparation phases of the
survey. The initial DQOs were based on the
requirements of EPA data users, the selection
of appropriate methods to obtain the required
data, and the results of a pilot study. Modifi-
cations were implemented based on review
comments from the users and cooperating
scientists. In addition, the analytical results
from specific methods, procedures, and instru-
mentation were useful in the adjustment of
DQOs for future DDRP surveys.
The primary characteristics of the preci-
sion objectives were the development and
implementation of a two-tiered system for
characterizing the DQOs. Similar parameters
were grouped together according to their type
of reporting units. Intralaboratory within-batch
piecibion goals were defined, based on a
percent RSD for concentrations above a specif-
ic level, defined as the "knot", and an absolute
standard deviation for concentrations below
the knot (see Table 2-7). The upper tier con-
centration range above the knot defines the
region of the data where the analytical results
ate relative and expressed as a percentage.
The lower tier concentration range below the
knot defines the region of the data where the
analytical results are absolute and expressed
as a standard deviation in reporting units.
This system avoids setting restrictive precision
requirements for low concentration samples
which generally are more difficult to analyze
with a high degree of precision. The knot was
established by dividing the precision objective
at the lower tier by the precision objective at
the upper tier (see Figure 2-1).
Data from the homogenized natural audit
samples were used to assess the DQOs for
analytical within-batch precision because they
had no sampling error and were assumed to
have negligible preparation error. As such, the
precision DQOs developed for the SBRP survey
were not intended to serve as project level
DQOs.
Estimation of the Data Collection
Error -
For any large survey, the collection of
ddta is a multi-phase process. In the DDRP,
those phases are field sampling, sample
preparation, and sample analysis. The QA
samples were introduced at these different
data collection phases so that analytical data
from the samples could be used to control and
assess the uncertainty for each phase. For
example, data from field duplicates can be
used to estimate the confounded error associ-
ated with field sampling, sample preparation,
and sample analysis. Data from the prepara-
tion duplicate samples can be used to esti-
mate the confounded error associated with
subsampling in the preparation laboratory and
analysis at the analytical laboratory. Data
from the audit samples can be used to esti-
mate the error of the sample analysis. The
audit samples are assumed to have negligible
preparation en or for the purposes of the error
estimates that are based on the following
model:
y - A/ <- e
23
-------
DQO
C,
in
knot
Mean (weight pet)
Figure 2-1. Example of a two-tiered precision objective.
where: y is an observed sample characteris-
tic; fj is the true sample characteristic; and e
is the data collection error, which is assumed
to be the sum of the errors generated by the
three independent data collection phases.
Standard operating procedures, or proto-
cols, were followed in each phase of the SBRP
survey. Depending on its limitations or
assumptions, each operating procedure
induces a random error for each physical or
chemical characteristic of a soil sample. The
sum of the errors induced by each procedure
can be defined as data collection error, which
is treated as a random variable. It is neces-
sary to characterize this variable in order to
identify the effect of the error on the routine
soil samples. This involves identifying the
distributional form and estimating the
moments.
The identification of the error distribution
requires a large number of replicate measure-
ments which, from a budgetary and logistical
standpoint, imposes a serious limitation;
however, a relatively small number of observa-
tions can be used to estimate the first two
moments, i.e., the mean and variance, of the
data collection error. The mean and variance
are sufficient to measure the precision and
accuracy of the routine data in an additive
model, where the observed analyte concentra-
tion is assumed to be the sum of the true
analyte concentration and the data collection
error. For this report, the standard deviation
in reporting units and the RSD, i.e., coefficient
of variation, in percent are used to measure
precision.
The within-batch precision component
measures the reproducibility of audit sample
data for a given set of soil samples analyzed
for one analytical run by one laboratory. The
between-batch precision component measures
the reproducibility of audit sample data for
different batches of soil samples analyzed on
different days by different laboratories. It is
expected that the within-batch variability is
smaller than the between-batch variability.
Two pairs of natural audit samples were
placed in each of the 26 batches for a total of
52 audit sample pairs for the SBRP survey. To
assess the within- batch precision, the stan-
dard deviations for each of the pairs were
pooled by averaging the variances and taking
the square root to generate a within-batch
standard deviation. A standard deviation was
calculated for the pooled means of the audit
pairs for between-batch precision.
It was found that the variance changes
with analyte concentration, and it was not
possible to identify a normal relationship
between the soil analyte concentration and the
error variance. However, the range of the soil
analyte concentration was arbitrarily divided
into intervals, i.e., windows, by grouping clus-
ters of data in such a way that the error
variance was relatively constant within each
window. It was then possible to fit a step
function across the windows to represent the
error variance for the entire concentration
range.
For each QA sample type, a step func-
tion was used to represent the appropriate
standard deviation. Values for the fitted step
24
-------
function were pooled and used as an estimate
of the associated standard deviation, e.g.,
data from the preparation duplicates were
used to estimate the standard deviation of the
confounded preparation and analytical error.
The standard deviations were pooled (sp) by
using the degrees of freedom (df) as a weight-
ing device according to the formula:
sp = [ Z{,.w (df, • s,2)
df]
1/2
where: s, is the standard deviation for the ith
window with corresponding degrees of free-
dom df|. The BSD was used to assess data in
the upper concentration ranges and was
obtained by dividing the pooled standard
deviation by the weighted mean.
It was also important to evaluate the
effect of measurement precision on the routine
sample data. Since the error standard devia-
tion changes with analyte concentration, the
expected standard deviation is estimated by
considering its variability over the range of
routine samples. In order to estimate this
effect, the standard deviations for different
windows are pooled with weighted proportions
of routine samples, grouped by sampling
class/horizon criteria, within the respective
windows. This pooled value, delta (6), is used
as a measure of system-wide data uncertainty
in the routine sample data due to data collec-
tion error. Delta is defined as:
6 = Z,,
(Pi
where: Pi is the proportion of routine samples
in the ith window, and s, is the estimated
standard deviation for the ith window. Occa-
sionally, a lack of QA sample data within the
concentration limits of a particular window
made it impossible to calculate a standard
deviation for that portion of the data set. In
those cases, delta is the conditional measure
of data uncertainty, the condition being de-
pendent on the availability of QA data. Hence,
certain windows are excluded from the calcula-
tion of delta.
An assumption is stated that the sam-
pling class/horizon groups define homoge-
neous sets of soil samples, each having a
specific variance. The 12 sampling classes
and the 19 primary horizon types associated
with these classes are known "effects" that
define soil differences in the SBRP survey. By
specifying these characteristics in the model,
the variation due to these effects was
removed. Table 2-8 presents the number and
percentage of primary horizon types selected
as a basis for the sampling class/horizon
criteria used in grouping the 748 routine sam-
ples, and the number of sampling classes
each horizon spans.
Table 2-8. Primary Horizon Types for Sampling
Class/Horizon Groups
Horizon
type
A
AB
AC
Ap
B
BA
BC
BE
Bg
Bs
Bt
Bw
Bx
C
Cg
Cr
E
Oa
Oe
Routine
number
136
25
1
12
3
21
49
2
3
1
112
201
2
111
6
10
10
2
41
samples
percent
18.2
3.3
0.1
1.6
0.4
2.8
6.6
0.3
0.4
0.1
15.0
26.9
0.3
14.8
0.8
1.3
1.3
0.3
5.5
Sampling classes
represented
12
9
1
4
2
8
9
2
1
1
9
11
1
11
2
4
6
2
9
Accuracy (Interlaboratory
Differences)
Accuracy is the ability of a specific
component of a measurement system to
approximate a true value. The audit samples
used in the SBRP survey were natural soil
samples, hence, their true chemical composi-
tion and physical characteristics are unknown.
Natural soil samples were used because a
procedure for preparing synthetic samples has
not been established. Therefore, accuracy of
the analytical data cannot be determined
because neither synthetic soil audit samples
nor natural soil audit samples of known
composition could be used as audit samples.
An international interlaboratory comparison
study, however, is currently being conducted to
provide data on the chemical composition and
physical characteristics of the natural audit
samples (Palmer et al., in preparation). Data
from the analyses of the audit samples by 22
25
-------
external laboratories can possibly be consid-
ered to represent the known composition of
these samples. These data will be compared
to data in the verified data base to estimate
interlaboratory bias. In the interim, data from
the natural soil audit samples are used to
establish interlaboratory differences for this
report.
Absolute Differences --
The absolute difference (d,) is defined as
the variation between the mean of a repeated
measurement for a given laboratory and the
mean for the measurement among all labora-
tories, as follows:
d, =
x.-X I
where: d, = absolute difference for the
ith laboratory
x, = mean for the ith laboratory
X = mean for all laboratories
Significant Differences Among
Laboratories --
For each of the parameters, the analysis
of variance (ANOVA) was used to determine
the significant differences among the audit
sample data reported by the analytical labora-
tories. An initial review of the data showed
that the analytical variances across audit
sample types were not identical. Because of
this lack of homogeneity, a nested ANOVA
model (Steel and Torrie, 1960) was used for
each audit sample type to test the significance
of laboratory differences by comparing labora-
tory means, based on a similar approach in
Schmoyer et al. (1988). The model is as fol-
lows:
Yuk =
+
+ T,, + e,Jk
where: YIJk = the ith laboratory observa-
tion of the kth audit sam-
ple in the jth batch
\i = the expected value of the
audit samples
I, = the ith analytical laboratory
effect
T,, = the jth batch effect within
the ith laboratory
€„„ = the random error
IJK
Where laboratory differences were signif-
icant, a pair-wise comparison was performed
on the laboratory means by using Scheffe's
multiple comparisons test (Arnold, 1981). The
results of this test were used to select values
of high significance and to describe the rank-
ing order in which the analytical laboratories
can be arranged.
Pooled Data for Laboratories and
Audit Sample Types --
Data pooled across audit samples to
eliminate horizon effects were used to estab-
lish each laboratory's performance for individ-
ual parameters. This was accomplished by
ranking the laboratories according to the
magnitude of the difference from the grand
mean (smallest to largest) after first compar-
ing the difference to the overall laboratory
mean. Three of the five audit sample types,
the A, Bw, and C horizons, were analyzed by
all three laboratories. Interlaboratory differ-
ences were determined, therefore, by pooling
only the data for the A, Bw, and C audit sam-
ples for each laboratory, as follows:
= A,Bw,C}
nia)
= A,Bw,C}
A.Bw.C} ("a ' '"'a)
100
= A,Bw,C}
where: d,a = absolute difference for the
ith laboratory and the ath
audit sample
nia = number of samples from
the ith laboratory and the
ath audit sample
Xa = mean for all laboratories for
the ath audit sample
na = total number of samples
for the ath audit sample
Pooling audit sample data to eliminate
laboratory effects allowed an evaluation to be
made of the mean laboratory difference for
four of the five audit sample types (the Oa
sample was analyzed by only one laboratory
and was not used in this evaluation). If the
26
-------
range of chemical and physical data of the
audit samples is comparable to that of the
routine samples in the survey, an evaluation
can be made of the ability of the laboratories
as a group to analyze certain soils using the
specified analytical methods. For example, if
the differences were very high for all labora-
tories for a parameter or group of parameters
determined by a specific analytical method, the
method itself could be in question concerning
its selectivity of the parameter. The overall
laboratory difference for each audit sample
was determined as follows:
(d,a • nia) +
100
where: dia = difference for the ith labor-
atory and the ath audit
sample
n,a = number of samples for the
ith laboratory and the ath
audit sample
Xa = mean for all laboratories
for the ath audit sample
Representativeness
The evaluation of representativeness
includes: (1) determining whether the routine
samples collected were representative of the
sampling class characteristics, (2) assessing
the homogenization procedure by measuring
the ability of each preparation laboratory to
prepare representative subsamples from the
bulk soil samples collected by the sampling
crews, and (3) assessing the ability of the QA
samples to adequately represent the range
and frequency distribution of analyte concen-
trations in the routine samples. Data from the
preparation duplicates were used in the sec-
ond assessment, while the Kolmogorov-
Smirnov two-sample test, i.e., the KS-statistic,
was used to estimate the maximum distance
between two data sets as a measure of
resemblance between the sets (Conover, 1980).
Three data sets encompassing data for
the routine samples (RS), the field duplicates
(FD), and the preparation duplicates (PD) were
used in the latter assessment. The FD and PD
sets were tested independently against the RS
set by using the p05, p50, and p95 percentiles
to assess the range and frequency distribution
within the data sets. The significant KS-statis-
tics, i.e., significant at the 0.05 level, were
defined according to the critical value (Vc) for
each data set comparison. The critical value
is based on a sample size n, and n2 for the
data sets being compared, where:
Vc = 1.36 • [(n, + ry + (n, • nj]
1/2
This algorithm yielded critical values for the
data set pairings, where Vc for FD_RS is 0.141
and Vc for PD_RS is 0.271. If the KS-statistic
exceeded the critical value for a particular data
set pairing, the QA data set was not represen-
tative of the distribution of routine samples.
Completeness
Soil sampling protocols in the SBRP
survey specified the sampling of 100 percent of
the designated pedons. The soil preparation
protocols specified that each batch of samples
sent to an analytical laboratpn/ includes a
preparation duplicate sample. The soil anal-
ysis protocols specified the complete analysis
of all samples collected for 90 percent or more
of the parameters. These three aspects of
completeness were evaluated using the SBRP
verified and validated data bases.
Comparability
Data comparability is ensured by the
uniform use of documented procedures for soil
collection, preparation, and analysis and by the
use of equivalent units for reporting the data.
The analytical methods and associated QA/QC
protocols that were used in the SBRP survey
were selected so that the data could be com-
pared with other similar data bases, e.g., the
DDRP Northeastern data base. On-site system
audits and thorough evaluations of analytical
data were employed to ensure that the proce-
dures were being followed correctly.
The DDRP Analytical Methods Manual
(Cappo et al., 1987) contains detailed descrip-
tions of each of the analytical techniques,
including examples of calculations and
appropriate references. The internal QC proce-
dures for each method are described in an
introductory section and are summarized in
tabular form. The QC protocols also are
described within each of the analytical method
descriptions. Data quality objectives, data
27
-------
qualifiers, and decimal reporting requirements
are listed in tabular form.
The ODRP Quality Assurance Plan (Bartz
et al., 1987) was based on previously devel-
oped planning documents for the National
Surface Water Survey. The QA Plan includes
several introductory sections describing the
project organization, sampling strategy, and
field operations. The QA objectives and the
sampling, preparation, and analytical QC
procedures are described in detail and are also
summarized in tabular form. Analytical meth-
ods are listed with the appropriate references.
These methods generally are descriptive of the
methods specified in the overall SOW as well
as the subsequent EPA special analytical
services solicitations.
Before it can be ascertained whether the
field sampling or sample preparation activities
are comparable between regions, the analytical
laboratories must be shown to have provided
comparable data. This assessment was made
by examining data from the natural audit
samples. If the analytical data are compara-
ble across regions, the sample preparation can
be compared using data from the preparation
duplicates. If the preparation data are compa-
rable across regions, then the field sampling
can be compared using data from the field
duplicates and from validation activities, e.g.,
aggregation. For this report, noncpmparable
field and laboratory methods used in the two
surveys were documented and the QA dupli-
cate samples inserted at certain steps during
the surveys were used to assess comparability
of the soil sampling, preparation, and analysis
phases. Comparability of the data bases
could not be evaluated because the statistical
approach taken for the Northeastern survey
data assessment was different from that of
the SBRP survey.
28
-------
Section 3
Results and Discussion
The results described in this section are
based on the analysis of data values in the
official SBRP verified data base. An assess-
ment of completeness used some data from
the official SBRP validated data base.
Detectability
Data relating to detection limits for
contract requirements, instrument readings,
and system-wide measurement in the SBRP
survey are presented in Table 3-1.
The SDLs were always larger than the
corresponding IDLs, which indicated the addi-
tional sources of variability in system-wide
measurement. As anticipated from the experi-
ences of previous surveys, variability in the
selected low concentration field duplicates
exceeded the variability in the selected DL-
QCCS. Only seven parameters did not have
over 85 percent of the data from their respec-
tive routine samples above the SDL. Only five
of the 31 IDLs were higher than their corre-
sponding CRDLs, and all were only slightly
higher except for CA_CL2.
Reduction of the CRDL for the exchange-
able base cations, from 0.20 to 0.05 mg/L, had
little effect on reducing the IDLs. The IDLs
were less than the corresponding CRDLs for
all cations at the 0.20 mg/L limit, although the
IDLs exceeded the CRDLs at the 0.05 mg/L
limit for CA_CL. The SDL was high in relation
to the routine samples only for NA_CL and
CA_OAC.
The IDLs for the CEC and exchangeable
acidity parameters were calculated by averag-
ing the IDLs reported by the laboratories
because the DL-QCCS data for these param-
eters were incomplete. The IDLs were slightly
higher than the CRDLs for CEC. The reduction
of the CRDL for AL_KCL, from 0.50 to
0.10 mg/L, reduced the IDL only slightly. Of
this group, the SDL was high in relation to the
routine samples only for AL_KCL.
Reduction of the CRDL for the extract-
able base cations, from 0.20 to 0.05 mg/L, had
little effect on reducing the IDLs. Reduction of
the CRDL for FE_CL2 and AL_CL2 from 0.50 to
0.05 resulted in a two-fold drop in the IDLs.
The IDLs were less than the corresponding
CRDLs for all cations at the 0.20 mg/L limit,
although the IDLs exceeded the CRDLs at the
0.05 mg/L limit for CA_CL2, NA_CL2 and
AL_CL2. The SDL was high in relation to the
routine samples only for FE_CL2 and AL_CL2,
both of which had very low analyte
concentrations.
The IDLs were lower than the CRDL for
each of the extractable iron and aluminum
parameters. The SDLs were higher than the
IDLs by an order of magnitude or more, but
were not high in relation to the routine
samples.
The IDLs were lower than the CRDLs for
the extractable sulfate and sulfate isotherm
parameters. The IDLs were converted from a
solution concentration to a soil concentration
that enabled comparisons to be made with the
SDLs. The SDLs for extractable sulfate were
three to six times higher than the IDLs, but
were not high in relation to the routine
samples.
The increase in the CRDL for total carbon
and nitrogen, from 0.005 to 0.010 weight per-
cent, resulted in a marked reduction in the IDL
for C_TOT but not for N TOT. The IDL was
lower~than the CRDL foF S_TOT. The SDLs
were high in relation to the routine samples for
N TOT and S TOT.
29
-------
Table 3-1. Detection Limits for the Contract Requirements, Instrument Readings, and System-wide Measurement
Parameter
CRDL*
Gate IDL*
Conv IDLC
SDL and %RS>SDLrf
CACL
MG~CL
KC1
NA_CL
CA OAC
MG~OAC
KCfAC
NA_OAC
CEC CL
CEC OAC
AC KCL
AC BACL
AL_KCL
CA CL2
MG~CL2
K C~L2
NA CL2
FE CL2
AL_CL2
FE PYP
AL~PYP
FE AO
AL~AO
FE CD
AL^CD
SO4 H20
S04~P04
S04~0
C TOT
N~TOT
SlJOT
0.05 mg/L
0.05 mg/L
0.05 mg/L
0.05 mg/L
0.05 mg/L
0.05 mg/L
0.05 mg/L
0.05 mg/L
0.01 meq/L
0.01 meq/L
0.25 meq/L
0.40 meq/L
0.10 mg/L
— mg/L'
0.05 mg/L
0.05 mg/L
0.05 mg/L
0.05 mg/L
0.05 mg/L
0.50 mg/L
0.50 mg/L
0.50 mg/L
0.50 mg/L
0.50 mg/L
0.50 mg/L
0.10 mgS/L
0.10 mgS/L
0.10 mgS/L
0.010 wt %
0.010 wt *
0.010 wt %
0.0524 mg/L
0.0369 mg/L
0.0364 mg/L
0.0415 mg/L
0.0314 mg/L
0.0121 mg/L
0.0330 mg/L
0.0448 mg/L
0.0153 meq/L'
0.0155 meq/L'
0.0060 meq/L'
0.1840 meq/L'
0.0840 mg/L
0.6071 mg/L
0.0187 mg/L
0.0335 mg/L
0.0560 mg/L
0.0402 mg/L
0.0616 mg/L
0.1434 mg/L
0.2278 mg/L
0.1941 mg/L
0.2282 mg/L
0.1340 mg/L
0.1998 mg/L
0.0141 mgS/L
0.0367 mgS/L
0.0494 mgS/L
0.0105 wt %
0.0114 wt %
0.0026 wt %
0.0068 meq/100g
0.0079 meq/100g
0.0024 meq/100g
0.0046 meq/100g
0.0041 meq/100g
0.0026 meq/100g
0.0022 meq/100g
0.0051 meq/100g
0.0306 meq/100g
0.0311 meq/100g
0.0188 meq/100g
0.3681 meq/100g
0.0186 meq/100g
0.0160 meq/100g
0.0003 meq/100g
0.0002 meq/100g
0.0005 meq/100g
0.0004 meq/100g
0.0014 meq/100g
0.0015 wt *
0.0023 wt %
0.0019 wt %
0.0023 wt %
0.0004 wt %
0.0006 wt %
0.2828 mgS/kg
0.9186 mgS/kg
-.„-
0.0311 meq/100g
0.0328 meq/100g
0.0423 meq/100g
0.0195 meq/100g
0.0725 meq/100g
0.0220 meq/100g
0.0363 meq/100g
0.0098 meq/100g
1.0724 meq/100g
0.5809 meq/100g
0.3870 meq/100g
3.7750 meq/100g
0.4780 meq/100g
0.0565 meq/100g
0.0041 meq/100g
0.0020 meq/100g
0.0031 meq/100g
0.0021 meq/100g
0.0071 meq/100g
0.0273 wt %
0.0220 wt %
0.0509 wt %
0.0547 wt %
0.1449 wt %
0.0426 wt %
1.7394 mgS/kg
3.2539 mgS/kg
0.0759 mgS/L
0.0821 wt %
0.0247 wt %
0.0178 wt %
89.8
92.4
90.0
69.1
77.5
96.1
92.2
92.0
99.9
100
92.1
89.8
83.1
99.6
99.7
99.6
98.9
12.7
51.3
93.8
99.5
93.7
96.3
98.5
99.3
92.0
99.7
91.4
96.7
71.2
44.6
' Contract-required detection limit.
6 Calculated instrument detection limit, estimated as three times the cooled standard deviation of a low level DL-
QCCS.
c Converted instrument detection limit, based on the specified reporting units.
d System detection limit, estimated as three times the pooled standard deviations of the lowest 10 percent of field
duplicates, independent of the CRDL; Percent of routine samples exceeding the system detection limit.
" Estimated by averaging laboratory-reported IDLs for incomplete DL-QCCS data.
' CRDL reported as standard deviation of ten nonconsecutive blanks.
NOTE: Detection limits not applicable for the physical parameters, soil pH, and the remainder of the sulfate isotherm
parameters.
Precision
The following sets of tables, figures, and
text are designed to satisfy the requirements
of the SBRP data users for summary precision
estimates of the routine and QA sample data.
The assessment of precision relates directly to
the achievement of intralaboratory within-batch
DQOs established in the DDRP QA Plan (Bartz
et al., 1987). In most cases, the DQOs have
knot values which represent the separation
point for the data uncertainty expressed as a
standard deviation for low concentrations and
as a relative standard deviation in percent for
higher concentrations.
The precision data are presented in
sequential order of the parameters listed in
Table 1-1 of Section 1 of this report. For each
of the nine parameter groups, a table of sta-
tistics presents the QA and routine sample
30
-------
data below and above the knot. These tables
show the relationship of the QA data to the
DQOs.
Two figures are presented for each
parameter within each parameter group. The
first figure is a plot of the mean and standard
deviation of data from each of the five audit
samples and their relationship to the DQO for
each parameter. The second figure is a plot
of the mean and standard deviation of data
from the routine samples, grouped by sam-
pling class/horizon criteria. The variability seen
in the sampling class/horizon data is princi-
pally the result of spatial heterogeneity among
the population of soils within each group.
Also included in this plot are sets of four
horizontal lines representing within-batch
standard deviations for the field duplicates,
preparation duplicates, and natural audit
samples, and between-batch standard devia-
tion for the natural audit samples. Each set of
lines represents the data uncertainty within the
windows that were established by the step
function across the total range of concentra-
tion. Although the data uncertainty is not
always constant within the windows for each
type of sample represented, the lines are
treated as constants. This latter figure is
intended to show the contribution of measure-
ment uncertainty to the overall variability of the
routine data.
Additional tables corresponding to the
step function statistical procedure for each of
the parameters are given in Appendix C as
supplemental information for the derivation of
the precision data provided in the plots.
Appendix D presents tables of data points that
were sorted according to the sampling class/
horizon group and the batch/sample number.
These data correspond to routine or QA sam-
ples having inordinately high or low values that
exert a disproportionate influence on the
assessment of data quality and are of interest
to data users when making evaluations of
individual data sets represented in the plots or
of individual batches of samples from a given
analytical laboratory.
Moisture, Specific Surface, and
Particle Size Analysis Table 3-2
Figures 3-1 through 3-6
The analytical within-batch precision DQO
for total sand, silt, and clay was not satisfied
Table 3-2. Achievement of Data Quality Objective* for
Analytical Wlthln-Batch Precision of
Moisture, Specific Surface, and Particle
Size Analysis
Data
set*
AS
PD
FD
S/H
* AS
FD
Pairs > DQO
Parameter
MOIST
SP SUR
SAND
VCOS
COS
MS
FS
VFS
SILT
COSI
FSI
CLAY
MOIST
SP SUR
SAND
VCOS
COS
MS
FS
VFS
SILT
COSI
FSI
CLAY
MOIST
SP SUR
SAND
VCOS
COS
MS
FS
VFS
SILT
COSI
FSI
CLAY
MOIST
SP SUR
SAND
VCOS
COS
MS
FS
VFS
SILT
COSI
FSI
CLAY
df
50
50
50
50
50
50
50
50
50
50
50
47
26
26
26
26
26
26
26
26
26
26
26
26
104
102
102
101
102
102
102
102
102
102
102
102
609
608
608
608
608
608
608
608
608
608
608
608
= Audit samples; PD
SD* DQO*
0.2910
2.7636
1.9639 1.00
0.8262
1.3240
0.8099
1.5903
1.0753
2.5757 1.00
3.1846
0.9564
1.2016 1.00
0.1442
3.3767
1.7419
1.0037
1.5593
0.5419
0.6286
0.6844
1.8274
1.6481
1.2356
0.7125
0.5149
5.2200
2.3027
1.1022
0.8049
0.8018
0.8429
1.0898
1.6107
1.4002
1.5825
1.5015
1.1321
16.1132
13.0921
3.5589
5.2780
5.5616
6.5113
5.3261
10.2580
5.0158
7.3044
6.8395
= Preparation
n
11
13
7
3
7
4
35
30
24
%
22.0
26.0
14.9
11.5
26.9
15.4
34.0
29.1
23.3
duplicates;
= Field duplicates; S/H = Sampling class/horizon
routine samples.
b Standard deviation data
reported in weight
percent
for mineral soil samples,
31
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in the SBRP survey (see Table 3-2). For SAND
and SILT, the DQO was exceeded by a factor
of two, while the DQO for CLAY was only
slightly exceeded. Based on data from the
audit sample pairs, however, the DQO was
satisfied 75 percent of the time or more for all
of the parameters. A general pattern of
increasing standard deviation with increased
sources of confounded error was found, i.e.,
the standard deviations for the field duplicates
exceeded those of the preparation duplicates
and audit samples. Specific DQOs were not
defined for moisture, specific surface, or the
sand and silt fractions.
For MOIST, the analytical within-batch
standard deviation observed in the audit sam-
ples was notably higher than the confounded
analytical/preparation standard deviation
observed in the preparation duplicates. It is
thought that the drier climatic conditions under
which the QA staff prepared the audit samples
may have allowed a greater fluctuation in
moisture among the different samples, thereby
resulting in greater variability than was ob-
served in the preparation duplicates. This
variability had no effect on the calculation of
air-dry/oven-dry coefficients for reporting
routine sample data on an oven-dry weight
basis.
Figures 3-1 through 3-6 are plots of the
audit sample data in relation to the DQOs and
of the routine sample data in relation to the
QA samples. The plots presented are provided
only for those particle size parameters for
which precision DQOs were defined, i.e., SAND,
SILT, and CLAY Appendix E contains the
routine data plots for the remaining particle
size parameters. Supplemental information
relating to the delta and proportion values is
presented in Appendix C, and the identification
of inordinate data values is presented in
Appendix D.
SoilpH
Table 3-3
Figures 3-7 through 3-12
samples (see Table 3-3). A comparison of
error estimates in the preparation duplicates
and the audit samples suggests that the
preparation error was negligible. A general
pattern of increasing standard deviation with
increased sources of confounded error was
maintained.
Table 3-3. Achievement of Data Quality Objective* for
Analytical Wlthln-Batch Precision of the
Soil pH Parameters
Data
set*
AS
PD
FD
S/H
Parameter
PH H2O
PH 002M
PHJJ1M
PH H20
PH 002M
PH_01M
PH H2O
PH 002M
PH_01M
PH H2O
PH 002M
PH 01M
df
50
50
50
26
26
26
104
104
104
609
609
609
Pairs>DQO
SD* DQO" n %
0.0349 0.15
0.0361 0.15
0.0354 0.15
0.0350
0.0253
0.0307
0.1009
0.0917
0.0846
0.3331
0.3433
0.3516
1 2.0
1 2.0
8 7.7
5 4.8
4 3.8
The analytical within-batch precision DQO
was easily satisfied in all cases for the pH
parameters using data from the natural audit
a AS = Audit samples; PD = Preparation duplicates;
FD = Field duplicates; S/H = Sampling class/horizon
routine samples.
b Standard deviation data reported in pH units.
The standard deviation did not show any
marked pattern of change over the measured
pH range, hence, it was not necessary to fit a
step function to the data from the three pH
parameters. Unlike the other SBRP param-
eters, the error variance was calculated for the
entire concentration range.
Figures 3-7 through 3-12 are plots of the
audit sample data in relation to the DQO and
of the routine sample data in relation to the
QA samples. Supplemental information relat-
ing to the delta and proportion values is
presented in Appendix C, and the identification
of inordinate data values is presented in
Appendix D.
38
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Exchangeable Cations In Ammonium
Chloride Table 3-4
Figures 3-13 through 3-20
The analytical within-batch precision
DQOs were satisfied for all of the cations
except K_CL in the upper tier (see Table 3-4).
The inordinate effect of data from one audit
sample pair prevented this particular DQO
from being met. The preparation duplicates
and field duplicates also satisfied the analyti-
cal DQO for the lower tier even though these
samples were susceptible to additional con-
founded errors from soil sampling or prepara-
tion. The general trend of increasing standard
deviation with increased sources of con-
founded error was maintained. For NA_CL,
the lack of data in the upper concentration
window renders the precision estimates condi-
tional on the presence of sufficient data within
this range.
Figures 3-13 through 3-20 are plots of the
audit sample data in relation to the DQOs and
of the routine sample data in relation to the
QA samples. Supplemental information relat-
ing to the delta and proportion values is
presented in Appendix C, and the identification
of inordinate data values is presented in
Appendix D.
Table 3-4. Achievement of Data Quality Objective* for Analytical Wlthln-Batch Precision of the Exchangeable
Catlona In Ammonium Chloride
Data
set"
AS
PD
FD
S/H
Parameter
CA CL
MG CL
K CL
NA_CL
CA CL
MG~CL
K CL
NA_CL
CA CL
MG CL
K CL
NA_CL
CA CL
MG CL
K CL
NA CL
df
9
29
23
48
17
13
19
24
56
59
80
101
224
279
476
609
••-——-— DOIUVV IIIO Ml'
SD DQO
0.0250 0.03
0.0073 0.03
0.0102 0.03
0.0187 0.03
0.0314
0.0140
0.0093
0.0097
0.0308
0.0250
0.0185
0.0172
0.1179
0.1147
0.0817
0.0350
U L •——-""-—-•-—
Pairs > DQO
n %
1
4
4
1
9
7
10
8
11.1
8.3
23.5
7.7
15.8
11.9
12.5
7.9
df
41
21
26
•
9
13
7
47
45
24
1
385
330
133
*-*""-" rujv/y
BSD
12.4%
4.3%
34.7%
5.1%
16.1%
5.1%
42.3%
47.0%
29.3%
10.8%
170.3%
99.4%
61.4%
D tire IVIIUL —————————
Pairs>DQO
DQO n %
15% 3 7.3
15%
15% 1 3.8
15%
1 7.7
•
13 28.3
6 13.3
6 25.0
• AS = Audit samples; PD = Preparation duplicates; FD - Field duplicates; S/H - Sampling class/horizon routine
samples.
* Standard deviation and RSD data in reporting units and percent, respectively, for mineral soil samples below
above the knot point of 0.20 meq/100g; a dot signifies a lack of data occupying that range.
45
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Exchangeable Cations In Ammonium
Acetate Table 3-5
Figures 3-21 through 3-28
The analytical within-batch precision
DQOs were satisfied for all parameters except
for K_OAC data above the knot which slightly
exceeded the DQO (see Table 3-5). A com-
parison of data from the preparation dupli-
cates and the audit samples suggests that the
preparation component of the data collection
error is very small. A general pattern of
increasing standard deviation with increased
sources of confounded error was maintained.
Figures 3-21 through 3-28 are plots of the
audit sample data in relation to the DQOs and
of the routine sample data in relation to the
QA samples. Supplemental information
relating to the delta and proportion values is
presented in Appendix C, and the identification
of inordinate data values is presented in
Appendix 0.
Table 3-5. Achievement of Data Quality Objectives for Analytical Wlthln-Batch Precision of the Exchangeable
Cations In Ammonium Acetate
Data
set*
AS
PD
FD
S/H
Parameter
CA OAC
MG~OAC
K OAC
NA_OAC
CA OAC
MG~OAC
K OAC
NA OAC
CA~OAC
MG OAC
K OAC
NA_OAC
CA OAC
MG OAC
K OAC
NAOAC
df
17
25
24
48
15
12
18
25
52
58
79
101
218
231
486
608
SO DQO
0.0220 0.03
0.0063 0.03
0.0087 0.03
0.0119 0.03
0.0214
0.0072
0.0085
0.0074
0.0270
0.0229
0.0168
0.0122
0.1259
0.1195
0.0756
0.0420
\\J L ---•—-—--——»-
Pairs>DQO
n %
2
1
1
12
6
7
2
11.8
2.1
6.7
23.1
10.3
8.8
2.0
df
33
25
26
11
14
7
49
46
25
1
391
378
123
,___—*____ i-vj\JV\
RSD
12.1%
6.9%
15.8%
12.4%
11.8%
14.0%
50.8%
37.3%
32.3%
9.0%
169.7%
97.0%
58.4%
a LI 10 iu HJL --——-—--———
Pairs>DQO
DQO n %
15% 8
15% 1
15% 3
15%
2
1
1
16
6
7
24.2
4.0
11.5
18.2
7.1
14.3
327
13.0
29.2
AS = Audit samples; PD = Preparation duplicates; FD = Field duplicates; S/H = Sampling class/horizon routine
samples.
Standard deviation and RSD data in reporting units and percent, respectively, for mineral soil samples below and
above the knot point of 0.20 meq/100g; a dot signifies a lack of data occupying that range.
54
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Cation Exchange Capacity and
Exchangeable Acidity Table 3-6
Figures 3-29 through 3-38
The CEC_CL parameter did not meet the
DQO for analytical within-batch precision
below the knot (see Table 3-6). The AC_BACL
parameter was only slightly above the DQO for
data below the knot. In all other cases the
DQOs for this parameter group were satisfied.
In most cases, the preparation duplicates and
field duplicates also met the analytical DQOs,
even though the samples were susceptible to
additional confounded errors from sampling or
preparation.
The estimated standard deviations for
CEC_CL in the PD and S/H data sets, and
CEC_OAC and AC_BACL in the FD and S/H
data sets, have insufficient degrees of freedom
to place confidence in these portions of the
data.
Figures 3-29 through 3-38 are plots of
the audit sample data in relation to the DQOs
and of the routine sample data in relation to
the QA samples. Supplemental information
relating to the delta and proportion values is
presented in Appendix C, and the identification
of inordinate data points is presented in
Appendix D.
Table 3-6. Achievement of Data Quality Objectives for Analytical Wlthln-Batch Precision of Cation Exchange
Capacity and Exchangeable Acidity
Data
set"
AS
PD
FD
S/H
Parameter
CEC CL
CEC OAC
AC KCL
AC'BACL
ALJDQO
n % df
2 33.3 44
44
38
3 50.0 44
36
25
26
9
26
9
2 33.3 98
102
4 6.3 41
101
3 4.1 30
608
608
207
608
197
,__«.—___ /-ujvry
RSD
8.9%
7.1%
12.8%
10.4%
8.5%
13.0%
9.5%
10.8%
15.3%
12.3%
14.4%
15.6%
15.2%
33.1%
11.3%
51.2%
52.1%
71.3%
64.9%
77.6%
W LI TO r\l KJL ™— »-—-———
Pairs>DQO
DQO n %
10% 6
10% 5
20% 1
20% 2
20% 2
6
3
1
7
1
30
25
4
22
3
13.6
11.4
2.6
4.5
5.6
24.0
11.5
11.1
26.9
11.1
30.6
24.5
9.8
21.8
10.0
* AS = Audit samples; PD = Preparation duplicates; FD = Field duplicates; S/H = Sampling class/horizon routine
samples.
" Standard deviation and RSD data in reporting units and percent, respectively, for mineral soil samples below and
above the knot point of 0.20 meq/100g; a dot signifies a lack of data occupying that range.
63
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Extractable Cations in Calcium
Chloride Table 3-7
Figures 3-39 through 3-50
Of the six extractable cations in calcium
chloride, the analytical within-batch precision
DQO was satisfied only for MG CL2 (see
Table 3-7). The RSD for AL_CL2 only slightly
exceeded the DQO, while The RSD for the
remaining cations were from 1.2 to 2 times
higher than the DQO. It appears that the
single-tiered DQO for this parameter group
was generally inappropriate and unattainable,
as there was no contingency made for a
lower-tier DQO to accomodate low analyte
concentrations. Indeed, the majority of the
routine data for these parameters was
distributed in the extremely low zone of
concentration near the detection limit. For
example, the FE_CL2 concentrations were so
low that, after correction for blank analysis,
many of the data showed up as negative
values. This was the case for 17 of the 26
preparation duplicates and 60 of the 104 field
duplicates, as seen in the high RSD value for
the FD data set.
Figures 3-39 through 3-50 are plots of
the audit sample data in relation to the DQOs
and of the routine sample data in relation to
the QA samples. Supplemental information
relating to the delta and proportion values is
presented in Appendix C, and the identification
of inordinate data values is presented in
Appendix D.
Table 3-7. Achievement of Data Quality Objectives
for Analytical Wlthln-Batch Precision of
the Extractable Cations In Calcium
Chloride
Data
set"
AS
PD
FD
S/H
Parameter
CA CL2
MG~CL2
K CL2
NA CL2
FE~CL2
AL/CL2
CA CL2
MG~CL2
K CL2
NA CL2
FE"CL2
AL~CL2
CA CL2
MG~CL2
K CL2
NA CL2
FE"CL2
AL.JCL2
CA CL2
MG~CL2
K CL2
NA CL2
FE"CL2
AL~CL2
df
50
50
50
50
42
49
26
26
26
26
9
24
104
104
104
104
44
87
609
609
609
609
543
602
RSD6
18.4%
9.8%
12.3%
20.5%
17.2%
10.8%
5.4%
8.7%
12.8%
12.0%
36.5%
67.8%
41.1%
52.7%
92.8%
34.5%
496.7%
94.6%
40.7%
64.1%
81.0%
274.6%
658.9%
138.0%
Pairs>DQO
DQO6 n %
5% 24
10% 13
10% 16
10% 17
10% 12
10% 24
8
8
13
12
4
14
40
40
61
72
24
63
48.0
26.0
32.0
34.0
28.6
49.0
30.8
30.8
50.0
46.2
44.4
58.3
38.5
38.5
58.6
69.2
53.3
72.4
AS = Audit samples;
FD = Field duplicates;
routine samples.
Data reported as %RSD.
PD = Preparation duplicates;
S/H = Sampling class/horizon
74
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Extractable Iron and Aluminum
Table 3-8, Figures 3-51 through 3-62
The analytical within-batch precision DQO
for the six extractable iron and aluminum
parameters was satisfied except for the
FE_AO concentrations below the knot (see
Table 3-8). In this case, the achieved preci-
sion only slightly exceeded the DQO. In most
cases, the preparation duplicates and field
duplicates also met the DQO in spite of the
additional confounded error due to soil sam-
pling and preparation. The effect of one
inordinate preparation pair in the FE_AO data
above the knot prevented the data set for this
parameter from meeting the DQO as well.
Generally, the relationship of increasing stan-
dard deviation with increased sources of
confounded error was maintained.
Figures 3-51 through 3-62 are plots of the
audit sample data in relation to the DQOs and
of the routine sample data in relation to the
QA samples. Supplemental information
relating to the delta and proportion values is
presented in Appendix C, and the identification
of inordinate data values is presented in
Appendix D.
Table 3-8. Achievement of Data Quality Objective* for Analytical Wlthln-Batch Precision of Extractable Iron and
Aluminum
Data
set'
AS
PD
FD
S/H
Parameter
FE PYP
AL~PYP
FE AO
AL~AO
FE~CD
ALjCD
FE PYP
AL~PYP
FE~AO
AL~AO
FE~CD
AlTCD
FE PYP
AL>YP
FE~AO
AL~AO
FE~CD
ALJJD
FE PYP
AL~PYP
FE~AO
AL~AO
FE~CD
AL~CD
df
6
6
6
7
6
6
17
17
15
17
1
16
51
62
62
69
4
43
275
293
296
330
1
194
.-—•—— Doiv/n 11 iv rvn
SD DQO
0.0063 0.05
0.0063 0.05
0.0657 0.05
0.0107 0.05
0.0319 0.05
0.0066 0.05
0.0153
0.0227
0.0353
0.0177
0.0127
0.0104
0.0411
0.0356
0.0324
0.0357
0.0237
0.0184
0.1455
0.1116
0.1736
0.1219
0.0283
0.1228
Wl — "• •"'' "" '
Pairs>DQO
n %
3 50.0
1 16.7
1 5.9
2 13.3
8 15.7
7 11.5
8 12.9
7 10.1
1 2.3
df
44
44
44
43
44
44
9
9
11
9
25
10
53
42
42
35
100
61
334
316
313
279
608
415
'""•—— nuwv
RSD
6.7%
8.1%
10.0%
9.3%
10.2%
10.2%
5.4%
12.0%
32.4%
21.3%
4.9%
4.5%
14.7%
17.6%
12.0%
14.4%
13.4%
12.7%
73.3%
65.1%
81.9%
76.9%
58.0%
52.5%
B 1119 IMIUl '•'
Pairs>DQO
DQO n %
15% 2
15% 4
15% 2
15% 3
15% 3
15% 1
2
1
2
1
6
6
10
7
9
8
4.5
9.1
4.5
7.0
6.8
2.3
22.2
11.1
18.2
11.1
11.3
14.0
23.8
20.0
9.0
13.1
AS = Audit samples; PD - Preparation duplicates; FD = Field duplicates; S/H = Sampling class/horizon routine
samples.
Standard deviation and RSD data in reporting units and percent, respectively, for mineral soil samples below and
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Extractab/e Sulfate and Sutfate
Adsorption Isotherms Table 3-9
Figures 3-63 through 3-78
The analytical within-batch precision
DQOs were satisfied for all of the sulfate
parameters except SO4_PO4 and SO4_0 (see
Table 3-9). In these two cases, a significant
amount of scatter in the Bs and C audit sam-
ples was responsible for large variability above
the knot and below the knot, respectively. In
most cases, the preparation duplicates also
met the analytical DQOs, which suggests that
preparation error is minor for these param-
eters. The effect of increasing sulfate levels
tends to promote decreasing variability in the
isotherm parameters. A general pattern of
increasing standard deviation with increased
sources of confounded error was maintained.
Table 3-9. Achievement of Data Quality Objectives for Analytical Wlthln-Batch Precision of Extractable Sulfate
and Sulfate Adsorption
Dalnu/ tha Irnnt4.
Data
set'
AS
PD
FD
S/H
Parameter
SO4 H2O
SO4~PO4
S04~0
S04~2
S04~4
SO4~8
SO4 16
SO4J32
S04 H2O
SO4~PO4
SO4~0
S04~2
SO4~4
SO4~8
SO4 16
SO4J32
SO4 H2O
SO4 PO4
SO4~0
SO4 2
SO4~4
SO4 8
SO4 16
SO4JJ2
SO4 H2O
SO4~P04
SO4 0
SO4~2
SO4~4
SO4~8
SO4~16
SO4 32
df
17
6
8
16
3
20
5
4
2
1
53
8
59
28
21
11
4
357
4
397
201
45
1
1
i_r«?iw*v 11 ro rvi iwt
Pairs >DQO
SO DQO n %
0.8916 1.00
2.2402 1.00
0.0921 0.05
0.05
0.05
0.05
0.05
0.05
0.7341
0.9141
0.0728
0.0177
0.0773
0.0015
0.0028
1.5490
1.2009
0.0956
0.1282
0.1040
0.1071
0.3395
5.3363
3.8622
0.6026
0.7242
0.9911
0.0997
0.3769
•
4
2
2
.
3
1
5
1
13
4
21
16
11
6
3
23.5
33.3
25.0
18.8
33.3
25.0
25.0
25.0
50.0
36.2
57.1
52.4
54.5
75.0
df
33
44
42
50
50
50
50
50
10
23
6
21
22
24
25
26
51
96
45
76
83
93
100
104
252
605
212
408
564
608
608
609
.... Ahnuo tho knot*— -
>...v.._. mLruvt? Lins miui — — ™. "••«
Pa ire > DQO
RSD DQO n %
4.2%
15.0%
6.2%
4.4%
3.1%
2.7%
5.4%
1.7%
8.8%
6.5%
3.1%
5.0%
4.3%
3.2%
3.7%
3.1%
18.8%
11.4%
14.5%
9.3%
8.2%
6.2%
5.6%
4.3%
46.5%
87.2%
53.0%
43.7%
43.4%
41.6%
33.9%
25.3%
10% 2
10% 7
5% 13
5% 9
5% 9
5% 2
5% 4
5%
3
4
4
3
3
4
4
15
25
26
31
31
31
28
21
6.1
15.9
31.0
18.0
18.0
4.0
8.0
30.0
17.4
19.0
13.6
12.5
16.0
15.4
28.8
26.0
56.5
40.8
37.3
33.3
8.0
20.2
* AS = Audit samples; PD = Preparation duplicates; FD = Field duplicates; S/H = Sampling class/horizon routine
samples.
b Standard deviation and RSD data in reporting units and percent, respectively, for mineral soil samples below and
above the knot point, 10.0 mg S/kg for extractable sulfate and 1.0 mg S/L for the isotherms; a dot signifies a lack of
data occupying that range.
100
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116
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Figures 3-63 through 3-78 are plots of
the audit sample data in relation to the DQOs
and of the routine sample data in relation to
the QA samples. Supplemental information
relating to the delta and proportion values is
presented in Appendix C, and the identification
of inordinate data values is presented in
Appendix D.
Total Carbon, Nitrogen, and Sulfur
Table 3-10, Figures 3-79 through 3-84
The analytical within-batch precision
DQOs were satisfied for total carbon, nitrogen,
and sulfur except for N_TOT data above the
knot (see Table 3-10). The preparation dupli-
cates and field duplicates also met the analyti-
cal DQOs for the lower tier but not the upper
tier. A general pattern of increasing standard
deviation with increased sources of con-
founded error was maintained.
Figures 3-79 through 3-84 are plots of
the audit sample data in relation to the DQOs
and of the routine sample data in relation to
the QA samples. Supplemental information
relating to the delta and proportion values is
presented in Appendix C, and the identification
of inordinate data values is presented in
Appendix D.
Table 3-10. Achievement of Data Quality Objectives for Analytical Wlthln-Batch Precision of Total Carbon,
Nitrogen, and Sulfur
Data
set"
AS
PD
FD
S/H
Parameter
C TOT
N TOT
SJTOT
C TOT
N TOT
SJTOT
C TOT
N TOT
S_TOT
C TOT
N TOT
S TOT
df
6
8
48
7
20
22
22
72
99
106
434
609
Pairs>DQO
SD DQO n %
0.0194 0.05
0.0023 0.01
0.0045 0.01
0.0552
0.0200
0.0067
0.0335
0.0172
0.0116
0.1191
0.0373
0.0376
2
2
6
2
4
17
4
4.2
28.6
30.0
9.1
18.2
23.3
4.0
Al~«._ »U~ 1,-nt"
df
44
42
19
5
82
32
1
503
170
Pairs>DQO
RSD DQO n %
8.5%
13.3%
20.9%
12.4%
40.8%
23.5%
79.2%
85.4%
69.6%
15% 2
10% 11
10%
6
3
27
13
1
4.5
26.2
31.6
60.0
32.9
41.9
100.
AS = Audit samples; PD = Preparation duplicates; FD = Field duplicates; S/H = Sampling class/horizon routine
samples.
Standard deviation and RSD data in reporting units and percent, respectively, for mineral soil samples below and
above the knot point, 0.33 weight percent for carbon and 0.10 weight percent for nitrogen and sulfur; a dot signifies
a lack of data occupying that range.
117
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Accuracy (Interlaboratory
Differences)
The following description of interlabora-
tory differences focuses on: (1) the significant
differences among the analytical laboratories
by audit sample horizon type, (2) the relative
differences and rank of increasing difference
among the laboratories using pooled data for
the audit samples, and (3) the relative differ-
ences among the audit samples using pooled
data from all of the laboratories.
Significant Differences Among
Laboratories
Table 3-11 shows the laboratories which
were significantly lower (at the 0.05 and 0.01
significance levels) than the other laboratories
using Scheffe's pair-wise multiple comparison
test for the analytical parameters.
For the A horizon audit sample, labora-
tory differences were highly significant for 19
parameters and significant for 11 parameters.
For the physical parameters, Laboratory 2
showed the greatest number of significant
differences. For the sulfate parameters,
Laboratory 1 showed the greatest number of
significant differences.
For the Bs horizon audit sample, labora-
tory differences were highly significant for four
parameters and significant for eight param-
eters. All of these cases involved differences
between Laboratory 1 and Laboratory 2.
For the Bw horizon audit sample, labora-
tory differences were highly significant for
seven parameters and significant for nine
parameters. A majority of the cases involved
Laboratory 2.
For the C horizon audit sample, labora-
tory differences were highly significant for only
two parameters and significant for five param-
eters. An additional five parameters showed
significant differences. A majority of the cases
involved Laboratory 3.
Overall, the laboratories were less con-
sistent with their analysis of the physical
parameters. In terms of sample type, the
laboratories were less consistent for the A
audit sample, followed by the Bw, Bs, and C
samples, respectively.
Relative Differences and Ranking of
Laboratories
Table 3-12 shows the relative difference
as percent and the rank of increasing relative
difference for each of the laboratories pooled
for the A, Bw, and C audit samples. The table
also shows the mean differences for all labo-
ratories combined for each audit sample type.
For the physical parameters, SP_SUR
showed the highest interlaboratory differences
followed by VCOS, while CLAY showed the
lowest differences. Laboratory 2 showed the
highest differences overall for the 12 param-
eters in this group. For soil pH, the laboratory
differences were consistently very low.
For the CEC parameters, Laboratory 1
was consistently lower than the other labora-
tories. For the exchangeable acidity param-
eters, Laboratory 2 was consistently lower
than the others. For the iron and aluminum in
the pyrophosphate and acid oxalate extracts,
Laboratory 1 was consistently lower than the
other laboratories. For iron and alimimum in
citrate dithionite, Laboratory 2 was consis-
tently lower than the others.
For the extractable sulfate parameters,
Laboratory 2 showed the lowest differences.
For the sulfate isotherm parameters, all labor-
atories showed low relative differences. For
the elemental analysis of carbon, nitrogen, and
sulfur, the laboratories were more consistent
for C_TOT, followed by N_TOT and S_TOT,
respectively.
For the 43 parameters used in deter-
mining laboratory differences, the rankings
showed that Laboratory 1 had the lowest
differences over all parameters, with 19 first-
place rankings (43 percent) and 9 third-place
rankings (21 percent).
Mean Differences Among the Audit
Samples
The laboratories showed the lowest
differences overall on the Bs audit sample for
the physical parameters, pH, CEC, acidity, and
iron and aluminum. The laboratories showed
the highest differences overall for the C audit
124
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Table 3-11. Significant Interlaboratory Difference*
Audit horizon6 —
Parameter3 A Bs Bw
SP SUR
SAND
COS
MS
FS
VFS
SILT
COSI
FSI
CLAY
PH H20
PH 002M
PH_01M
CA CL
MG_CL
CA OAC
MG_OAC
CEC CL
CEC~OAC
AC KCL
AL_KCL
CA CL2
MG CL2
K CL2
NA CL2
AL_CL2
AL PYP
FE AO
AL~AO
FE'CD
AL_CD
S04 H20
S04 P04
S04~2
S04~4
S04~8
S04 16
S04JJ2
C TOT
N TOT
S TOT
2 < 1.3 **
1.3 < 2 **
1 < 2,3 ** 1 < 2
3 < 2**
2 < 3,1 **
2 < 1.3 **
1,2 < 3
1,2 < 3 2 < 1
1,2 < 3 ** 2 < 1 **
2 < 3 2 < 1 **
3 < 1
1,2 < 3 **
2 < 1
1.2 < 3 **
2 < 1 < 3 ** 2 < 1 **
1 < 3
1 < 3,2 **
3,1 < 2 1 < 2
3 < 1 ** 2 < 1
2,3 < 1 2 < 1
2,3 < 1 **
1.3 < 2 1 < 2
1,3 < 2 **
1,2 < 3 **
1 < 2,3 ** 1 < 2 **
3 < 1.2 **
1 < 2
1 < 2**
1 < 2**
1 < 2 ** 1 < 2
1 < 2 **
2 < 1
3 < 2
1,3 < 2
2 < 1,3
2 < 3 **
1 < 2,3 **
3,1 < 2 **
2 < 1,3
2 < 1,3
2 < 1 **
3 < 1,2 **
1 < 3 1 < 3
2 < 3 1 < 3
2 < 3
2 < 3
2 < 1 **
1,2 < 3 **
2 < 1,3
2 < 3
2 < 3
3 < 2
2 < 3
1,2 < 3 **
2,3 < 1 **
* No significant differences were reported for MOIST, VCOS, K_CL. NA_CL, K_OAC, NA_OAC, AC_BACL, FE_CL2, FE_PYP,
and S04_0.
b A double asterisk denotes a highly significant difference at the 0.01 significance level; differences not evaluated for
the Oa horizon audit sample.
125
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Table 3-12. Relative Difference and Rank by Laboratory and Mean Laboratory Difference by Audit Sample Type
Parameter*
MOIST
SP SUR
SAND
VCOS
COS
MS
FS
VFS
SILT
COSI
FSI
CLAY
PH H2O
PH~002M
PH~01M
CA CL
MGlCL
CA OAC
MGlOAC
CEC CL
CEC OAC
AC KCL
AC'BACL
ALjKCL
CA CL2
MG~CL2
FE PYP
AL'PYP
FE AO
AL~AO
FE~CD
AL^CD
SO4 H2O
SO4~PO4
S04~0
SO4 2
S04~4
SO4 8
SO4~16
SO4JJ2
C TOT
N~TOT
S~TOT
Difference (%)
Laboratory
1 2 3
3.0
10.5
4.3
9.5
2.9
2.5
7.3
4.9
8.6
8.2
9.0
0.9
0.9
0.9
0.5
3.5
4.8
1.7
6.5
7.8
4.6
10.8
0.5
9.3
8.2
12.7
6.5
1.6
5.7
4.8
8.8
11.9
6.5
4.6
3.5
5.0
1.9
3.5
4.1
4.0
1.3
3.6
6.8
0.6
25.0
5.3
12.8
6.1
1.4
4.1
10.8
10.7
11.0
10.4
1.1
0.2
1.0
0.9
13.8
1.8
18.2
5.7
18.5
16.7
2.2
4.9
5.4
12.3
3.6
2.3
6.8
12.0
14.0
6.4
2.1
1.0
3.6
4.8
5.1
2.8
4.6
4.7
3.5
0.8
4.0
9.2
2.8
17.9
1.5
17.4
8.7
3.1
3.2
8.6
3.1
3.9
3.6
1.3
1.1
2.1
1.7
12.4
7.0
22.4
1.0
30.1
14.6
13.0
5.7
3.9
8.5
10.9
5.3
7.4
7.8
11.0
17.0
11.5
7.9
2.2
2.0
0.7
2.1
1.6
1.3
1.6
1.6
7.4
8.3
Rank
— Laboratory —
1 2 3
3
1
2
1
1
2
3
1
2
2
2
1
2
1
1
1
2
1
3
1
1
2
1
3
1
3
3
1
1
1
2
3
2
3
2
2
1
2
2
3
2
1
1
1
3
3
2
2
1
2
3
3
3
3
2
1
2
2
3
1
2
2
2
3
1
2
2
3
1
1
2
3
3
1
1
1
2
3
3
3
3
3
2
1
2
3
2
2
1
3
3
3
1
2
1
1
1
3
3
3
3
2
3
3
1
3
2
3
3
1
2
2
2
3
2
2
3
2
3
1
1
1
2
1
1
1
3
3
2
Difference (%)
.*....._•..., - *• ""!•* O««««.|A
A
2.3
18.0
3.7
12.6
1.7
1.5
5.7
5.2
8.7
8.5
8.9
0.6
0.5
1.1
1.0
9.5
3.9
12.0
3.6
15.7
11.2
5.5
3.0
5.8
13.1
8.7
4.1
5.2
6.7
10.7
11.2
7.5
5.0
3.0
3.4
4.2
2.0
3.6
3.4
2.8
1.0
4.2
4.3
Bs Bw
0.4
2.1
0.2
7.1
4.6
0.3
5.4
3.0
0.3
0.9
2.9
12.3
0.3
1.3
0.9
18.0
10.1
5.5
1.8
6.8
4.2
2.0
0.3
2.9
5.8
8.5
0.1
2.6
8.1
1.0
5.6
6.4
2.2
23.2
5.9
2.8
0.6
3.0
3.9
2.4
8.9
8.3
8.1
1.3
17.2
11.5
5.9
8.6
3.0
7.5
27.4
4.8
4.4
6.0
2.6
0.6
0.7
0.7
10.0
5.0
13.8
15.4
31.8
15.2
20.7
4.1
8.0
5.1
8.8
5.7
4.3
9.6
8.6
6.7
9.5
3.6
4.3
3.3
2.2
6.0
1.4
4.1
4.3
3.8
5.7
4.8
C
2.8
27.0
1.3
20.1
9.3
3.1
2.5
6.5
31.8
34.3
23.8
100.0
1.6
2.6
1.6
14.8
17.0
39.3
10.7
28.6
25.1
60.9
46.8
25.0
5.8
15.8
13.0
18.2
35.3
12.7
25.3
19.5
14.1
17.4
6.3
0.9
0.3
3.4
3.1
3.0
3.5
48.4
42.9
Concentrations were too low to estimate interlaboratory differences for K_CL, NA_CL, K_OAC, NA_OAC. K CL2, NA CL2,
FE_CL2, and AL_CL2.
126
-------
sample. The laboratories performed well on
all audit samples for the sulfate isotherm
parameters.
Over all the audit samples, the labora-
tories showed the greatest differences for
SP_SUR, VCOS, CEC_CL, CEC OAC, FE_CD,
and S04_PO4.
Representativeness
All pedons sampled were within the
range of morphological characteristics outlined
in their respective sampling classes (Coffey et
al., 1987), hence, the DQO for representative-
ness of the field sampling was satisfied.
The homogenization and subsampling
procedures at the preparation laboratories
produced representative analytical soil samples
of known and accepted quality (Haren and Van
Remortel, 1987). More information on this
characteristic of the data can be found in the
precision discussions of this report, where
assessments of the preparation duplicates are
made.
Histograms of the range and frequency
distribution of the routine samples, field dupli-
cates, preparation duplicates, and natural audit
samples for each of the parameters are pre-
sented in Appendix F. The field duplicates and
preparation duplicates generally were represen-
tative of the range and frequency distribution
of analyte concentrations for the routine sam-
ples. The only exceptions were the SP SUR,
COSI, FE_CL2, AL_CL2, and S_TOT parameters
(see Table 3-13). A more rigorous selection
method for the preparation duplicates, relative
to that of the DDRP Northeastern Soil Survey,
was responsible for good representativeness
in the PD data set. The audit samples gener-
ally were representative of the range of data
from the routine samples.
Completeness
Soil sampling protocols specified the
sampling of all of the designated pedons. A
total of 110 pedons were sampled of the 114
pedons initially selected, resulting in 96.5
percent completeness (Coffey et al., 1987).
Although this does not fully satisfy the DQO
for sampling completeness, sufficient pedons
were sampled to enable estimates and con-
clusions to be drawn from the data.
As specified in the protocols, each batch
of samples sent to a analytical laboratory
contained one preparation duplicate pair. The
Table 3-13. Summary of Significant Differences In the Distribution of the Field and Preparation Duplicates Relative
to the Routine Samples
Parameter
SP_SUR
COSI
FE_CL2
AL_CL2
S_TOT
Data set"
RS
FD
RS
FD
RS
FD
PD
RS
FD
RS
FD
n
703
102
703
102
747
106
26
747
106
747
106
Mean
34.33
35.26
9.88
10.83
0.01
0.01
0.00
0.05
0.04
0.02
0.02
P05*
9.48
8.70
3.50
3.60
0.00
0.00
0.00
0.00
0.00
0.00
0.00
P50*
30.93
34.71
8.70
9.85
0.00
0.00
0.00
0.01
0.01
0.01
0.01
P95*
74.47
67.55
19.52
20.62
0.02
0.02
0.01
0.15
0.13
0.07
0.06
KS-statc
0.151
0.147
0.574
0.584
0.194
0.270
* RS = routine samples, FD = field duplicates, PD = preparation duplicates.
* p05, p50, and p95 are the 5th, 50th (median), and 95th percentiles by data set.
c Kolmogorov-Smirnov test; statistics are significant at the 0.05 level for the critical value: FD_RS
PD RS = 0.271.
0.141,
127
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requested soil analyses and sample process-
ing tasks were performed on 100 percent of
the bulk samples and clods received by the
preparation laboratories (Haren and Van
Remortel, 1987).
The number of AO and JJ flags (denoting
missing data or insufficient sample for analy-
sis, respectively) assigned to the 748 routine
samples was used to assess analytical com-
pleteness of the verified data base. There
was only one missing sample in the data base
and all of the analyses were performed on the
remaining 747 samples; hence, the analytical
laboratories achieved a 99.9 percent complete-
ness level (see Appendix B).
Five levels of confidence, ranging from 0
to 4, were used to segregate and classify data
in the validated data base. A level of con-
fidence of 2 or less, i.e.. less than two major
flags or less than one major and two minor
flags assigned per sample, was used to
assess completeness in the validated data
base (see Appendix B). The DQO for analytical
completeness of 90 percent or higher was
satisfied for all of the parameters. The CEC
parameters were the only analytes to fall
below a completeness level of 95 percent for
the validated data base.
Comparability
The entire verified data base was used
for the assessment of data quality for both
the Northeastern and SBRP reports because
the indiscriminate use or non-use of flagged
data was felt to be inappropriate for the
purposes of quality assessment. The flags
were applied in order to caution the data user
that certain data points are suspect and may
not be suitable for a particular type of data
analysis. Data with levels of confidence of 0,
1, and 2 in the validated data base were used
only for the assessment of analytical
completeness.
Analytical data from an interlaboratory
comparison study were recently received by
EMSL-LV staff. The study is using data from
the DDRP audit samples to compare analytical
methods used in the two surveys to methods
currently in use at 22 selected soil charac-
terization laboratories throughout the United
States and Canada. The results will be
summarized in an upcoming report (Palmer et
al., in preparation).
Comparison of Analytical and
Preparation Methods
Because of significant differences in
methods among private laboratories, the
preliminary audit sample data provided by
three independent referee laboratories prior to
the initiation of the DDRP surveys could not be
utilized to evaluate the quality of routine data.
Sufficient audit sample data were available
from the DDRP contract laboratory analyses,
however, to provide an estimate of the audit
sample composition. These data were used in
the assessment of comparability, precision,
and interlaboratory differences.
Initial difficulties were encountered in
developing and evaluating the analytical meth-
ods prior to initiation of the DDRP surveys.
As a result, there are certain instances where
the methods actually used by the contract
laboratories differ from those specified in the
DDRP Analytical Methods Manual or in the
individual laboratory solicitations. Approval for
methods amendments was given only when it
was determined by the QA staff that these
changes would not significantly affect the
analytical results, e.g., changing from a 0.20-
micron filter to a 0.45-micron filter. Methods
amendments were recorded in an operations
log book by QA staff but did not always result
in an official EPA contract modification.
During the Northeastern survey, analytical
methods for two parameters were changed
sufficiently to warrant reanalysis of any pre-
viously analyzed samples. The laboratories
were contracted to reanalyze all of their sam-
ples for AL_KCL by using a method which
employed a different acidification procedure.
Two of the laboratories also were contracted
to adjust the soihsolution ratio for PH_002M
and to reanalyze all of the samples; the third
laboratory already had been using the
amended ratio. Hence, reanalyses have cor-
rected all data significantly affected by meth-
ods amendments which occurred as the survey
progressed (Byers et al., 1988).
Identical soil preparation methods were
used in preparing soil samples for the two
surveys. The protocols were revised for clarity
in the SBRP survey but the methods remained
128
-------
comparable. The procedure for selecting a
preparation duplicate for each batch was
refined for SBRP, resulting in better representa-
tiveness of the preparation duplicates.
Comparison of Field Sampling
Methods
As a result of information gathered from
the Northeastern survey exit meeting, the field
sampling protocols were revised to include
clarifications of sampling procedures and
contamination control for the SBRP survey. It
was discovered that the field duplicates in the
Northeastern survey were sampled by two
different methods, i.e., some crews placed
alternate portions of soil from the same hori-
zon into separate bags (the correct method)
while other crews collected twice the normal
amount of sample, performed a simple homo-
genization, and split the sample. The former
method is meant to determine sampling varia-
bility, hence, the data from samples derived by
this method are expected to be more variable
than the data derived by the latter method.
Because of the inconsistent application of the
method, the variances of the Northeastern field
duplicates tend to fluctuate among pedons.
Field duplicates for the SBRP sites were sam-
pled using the correct method. Nevertheless,
overall within-batch variability was expected to
be greater in the SBRP than in the North-
eastern survey because of the additional
sampling variability error contained in the field
duplicates that were sampled using the correct
protocol. This does not mean that the routine
data between region is not comparable, as a
similar methodology was used for routine soil
sampling in each survey. It does suggest,
however, that measurement error in the North-
eastern survey may have been underestimated.
The field sampling audit team did not report
any deviations from the sampling protocols
that would compromise the integrity of the
routine data.
Comparison of Audit Sample
Distribution
Although the SBRP was a less extensive
survey in terms of the total number of samples
collected, two pairs of natural audit samples
were placed in each batch in contrast to one
pair per batch in the Northeastern survey. This
accounts for the similar total number of audit
samples (104 versus 112, respectively), even
though the number of batches in each survey
varied widely. The soil for each audit horizon
type in both regions came from the same bulk
audit sample, hence, data for each subsample
can be compared between regions for any
given parameter. Significant differences could
then be attributed to differing amounts of
measurement error, e.g., differential laboratory
bias. Since there were four analytical labora-
tories in the Northeastern survey and only
three of those four in the SBRP survey, Labo-
ratory 4 cannot be regionally compared.
Laboratory 3 did not analyze the A or C audit
samples in the Northeastern survey or the Bs
audit horizon in the SBRP survey, hence, com-
parisons for this laboratory can be made only
with data from the Bw and Oa audit horizons.
129
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Section 4
Conclusions and Recommendations
Data Verification
Verification of Data Packages
A number of improvements can be made
in the verification procedure. Principal among
them is the development of a computerized
data entry and verification system that will
calculate all of the final data values and pro-
duce a list of flags and data entry errors. This
will provide a much faster turnaround time for
submission of data packages and completion
of the data review phase and confirmation/
reanalysis requests. All raw data needed to
calculate final values could be entered and a
calculation program could be run. This would
facilitate the rapid identification of entry errors
and ultimately reduce the amount of reanalysis
needed. A link between the laboratories and
the quality assurance staff should be estab-
lished that will enable the transfer of prelimi-
nary and final data. The verification program
should be designed to evaluate the quality
control checks and other contractual require-
ments, thereby inducing the laboratories to
assume much of the responsibility for identi-
fication and correction of errant data.
Evaluation of the blind audit samples
should also be made part of the verification
system. However, this portion of the system
would be accessible only to the quality assur-
ance staff. This evaluation would be used in
conjunction with the quality control and sum-
mary checks to determine the acceptance of
batches from the laboratories.
Internal Consistency
The internal consistency checks provided
a meaningful check of routine data for each
analytical parameter. Errors were discovered
that might have otherwise gone unnoticed.
The checks were performed during the final
weeks of data verification for the SBRP survey.
Outliers determined by the internal consistency
computer program were checked only for
transcription errors. The program generated
outliers consisting of approximately 1 percent
of the total number of data values for each
parameter. Of these outliers, approximately 10
percent, i.e., 0.1 percent of the total data
values, were found to be in error. A few
parameters did contain a relatively large num-
ber of outliers which, after correction, improved
the quality of data. There were some param-
eters that did not correlate well with any of
the other parameters. Although the highest
and lowest one percent of these values for
these parameters were reviewed, a better
procedure for checking these values should be
developed.
The internal consistency checks could
provide useful information during the earlier
stages of verification. By using these data to
influence requests for reanalysis, the checks
could become an integral part of the verifica-
tion process. Difficulties with methodology
and data reporting become apparent when
significant numbers of outliers are found for
specific batches.
If it can be determined that parameter
correlations are comparable among regions,
then single batches of data from new regions
can be incorporated into an overall data file
and reviewed to distinguish outliers. However,
if the correlations do not compare across
regions, a statistically significant population of
data must be collected from the region of
concern before suspect data points from
individual batches can be viewed as outliers.
130
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A method should be developed that will
identify outlying, but confirmed, data points
which tend to distort the data quality assess-
ment. In addition to the present internal
consistency checks, a suitable statistical
analysis should be selected to identify which
data points are having a disproportionate
influence on the overall data. These data
points and any associated routine data could
be highlighted with a special flag for the
benefit of data users.
Data Quality Objectives
Detectability
Considerable effort was expended during
the course of this survey to evaluate and
improve the detectability of various param-
eters. In particular, significant improvement
was obtained for the exchangeable cations. It
is recommended that attention be given to
improving detectability in future surveys.
Additional methods research is essential to
this effort.
Data quality objectives for detectability
were not set at the start of the survey with
regard to system detection limits. Although
instrument detection limits are an integral part
of the detectability issue, the actual detection
limit that can be applied to the final data set
is the system detection limit and not the
instrument detection limit. It is recommended
that both types of limits be addressed in the
data quality objectives for future surveys.
It has been noted that "soil blanks" were
not used in this survey, hence, it was difficult
to calculate system detection limits or to
identify when contamination may have
occurred. Although some consideration has
been given to the development of a soil blank
sample, it is recommended that low concentra-
tion audit samples, entered into the system
during the sampling phase, be utilized as
substitutes for blanks. These samples would
serve not only to identify contamination prob-
lems and allow for the calculation of detection
limits, but could also be used to estimate
system precision and accuracy as well as
provide additional quality control benefits.
Precision
It was necessary to investigate why the
laboratories had difficulty in satisfying certain
precision objectives, e.g., the objectives might
be unreasonably restrictive, the laboratories
had problems with the methods, or there were
sample preparation problems. The dispropor-
tionate effect of inordinate data points on the
estimates were also assessed. The precision
results show that the analytical precision
objectives for certain parameters were not
satisfied, including the particle size param-
eters, potassium and cation exchange capacity
in ammonium chloride, potassium in ammoni-
um acetate, the extractable cations in calcium
chloride, iron in acid oxalate, phosphate-
extractable sulfate, the sulfate-zero isotherm,
and total nitrogen.
Table 4-1 is a summary of the overall
achievement of the data quality objectives for
analytical within-batch precision. A proportion,
or precision index, was determined for each
group of parameters by pooling and weighting
the standard deviations across the parameters
within the group for the Sower and upper tiers
and dividing by their respective --'-••? quality
objectives. These values were tht-i summed
and divided by the total degrees of freedom
for the group. A precision index exceeding 1.0
(denoted by an asterisk in the table) indicates
that the precision estimate for 'his parameter
group did not meet the "overall objective" when
viewed from the perspective of the entire
concentration range. This approach helped to
identify which parameter groups should under-
go further quality assurance emphasis in order
to redefine the objectives for future surveys or
to reassess the analytical procedures for the
affected parameters.
The lack of a two tiered data quality
objective for the particte s:?e parameters might
explain the relatively high variability, although
the objective could be w - '".at restrictive as
well. Also, the nature c •'• • ''metric''sieve/
pipet method, such an " 'ure -'fects,
may have caused variab ;>'•/ • : Dart-; 'e size
percentages. Inter!abor;s*c , fences for
these parameters were ;ftiat>vc , nigh. It is
recommended that additional methods details
be provided in order tc lower ihe variability
among laboratories.
131
-------
Table 4-1. Precision Indices Baaed on Pooled Wlthln-Batch Precision Estimates for Parameter Groups Across
Concentration Ranges
Parameter group (parameters included)
Precision index
Particle size analysis (SAND, SILT, and CLAY)
Soil pH (PH : H2O, 002M, and 01M)
Exchangeable cations (CA_, MG_, K_, and NA_: CL)
(CA_, MG_, K_, and NA_: OAC)
Cation exchange capacity (CEC CL and CEC_OAC)
Exchangeable acidity (AC KCL, AC_BACL, and AL KCL)
Extractable cations (CA_,~MG_, «_. MG_, FE^ and~AL_: CL2)
Extractable iron and aluminum (FE_ and AL_: PYP, AO, and CD)
Extractable sulfate (SO4_: H20 and PO4)
Sulfate isotherms (SO4_: 0, 2, 4, 8, 16, and 32)
Elemental analysis (C_TOT, N_TOT, and S_TOT)
1.93*
0.24
0.77
0.55
0.83
0.50
1.79*
0.58
1.09*
0.80
0.72
In the future, two-tiered precision objec-
tives should be defined for the extractable
cations in calcium chloride. For potassium in
ammonium chloride, one audit sample pair had
high variability which expanded the imprecision
for data above the knot. For cation exchange
capacity, only a small amount of the data was
below the knot and one-third of these data
had high variability.
For phosphate-extractable sulfate, the
laboratories exceeded the 10 percent objective
by a considerable degree both below and
above the knot, although this was not the
case for water-extractable sulfate. This sug-
gests that there were no problems with biolog-
ical degradation over time, sample preparation,
or sample extraction. However, there may
have been a difficulty with the ion chromatog-
raphy instrumentation, where inadequate
separation of the sulfate and phosphate peaks
may have occurred for the higher sulfate
concentrations in the phosphate extraction. In
addition, column loading could occur due to
high phosphate concentrations in the extract.
For total nitrogen, one laboratory exhibited
significant interlaboratory differences which
might explain variability in the data.
As expected, increasing sources of
confounded data collection error led to in-
creased standard deviations in the precision
estimates. Of the 64 cases where the preci-
sion estimates below and above the knot for
the preparation duplicates were compared to
the analytical data quality objectives, only 18
cases exceeded the objective. This indicates
that the preparation laboratories performed
relatively well in subsampling the bulk soil
samples. In many cases, the error estimates
for the preparation duplicates were less than
that for the audit samples. As a result, error
in the preparation of the natural audit samples
by QA staff often may exceed error in the
preparation of the routine and duplicate sam-
ples by the preparation laboratories. For the
field duplicates, 39 of the 64 cases exceeded
the analytical objectives. Specific data quality
objectives should be defined for system-wide
measurement in future surveys, using data
from the field duplicates. Also, as anticipated,
the sampling class/horizon groups showed the
highest levels of error due to population
variability.
In summary, the preparation duplicates
generally had slightly higher precision than the
audit samples. This suggests that the quality
assurance staff may have had more difficulty
in homogenizing the 500-kilogram bulk audit
samples as compared to the 5.5-kilogram
routine samples homogenized at the prepara-
tion laboratories. This has implications for
preparation of any future audit samples, but
also reflects well on the subsampling proce-
dure followed by the preparation laboratories.
In addition, the relatively lower precision for
the field duplicates suggests that the com-
ponent of error from soil sampling is a large
portion of the overall data collection error.
Accuracy (Interlaboratory
Differences)
The approach taken for this report was
to assess interlaboratory differences for the 51
132
-------
analytical parameters by comparing mean
values among the individual laboratories and
mean values for the laboratories combined
across audit samples. The lack of acceptable
or "true" analytical values for the soil param-
eters prohibited the assessment of accuracy,
hence, interlaboratory differences are used to
describe the relative systematic error. Three
basic comparisons were made: (1) the use of
a statistical test to directly compare labora-
tories, (2) pooling of audit sample data (A, Bw,
and C horizons) for each laboratory to com-
pare and rank overall laboratory performance,
and (3) pooling data from each laboratory for
each audit sample (A, Bs, Bw, and C horizons)
to compare laboratory performance by sample
type.
For the 624 possible statistical configura-
tions of the laboratories by parameter and by
audit horizon type, there were 97 cases (52, 12,
24, and 9 cases for the A, Bs, Bw, and C
horizons, respectively) where one laboratory
was significantly different at the 0.05 signifi-
cance level. Considering the number of pairs
of audit samples analyzed by the laboratories
(36, 40, and 24 pairs for Laboratories 1, 2, and
3, respectively), the number of significant
differences attributed to each laboratory were
similar (i.e., 36, 45, and 16). In this respect,
each of the laboratories performed similarly
when compared to the other two laboratories.
Of the 97 significant differences identi-
fied, 41 (42 percent) were highly significant at
the 99 percent confidence level. A majority of
those (25 of 41) were in the A horizon data.
The data users should be aware of this when
assessing data for specific parameters. Table
4-2 shows the mean laboratory difference for
each laboratory and audit sample type for
each parameter group or subgroup for 44 of
the 51 parameters analyzed.
The lowest interlaboratory differences
occurred in the soil pH parameter group. The
laboratories showed the highest differences
overall in the CEC parameters. The differences
were also relatively high for the cations in
calcium chloride and the cations in ammonium
acetate. The mean interlaboratory differences
across all parameters for Laboratories 1, 2,
and 3 were 5.2, 6.8, and 8.2 percent,
respectively.
The laboratories showed the highest
differences on the C audit samples. The mean
laboratory differences for the A, Bs, Bw, and C
audit samples were 7.1, 5.5, 9.0, and 22.5
percent, respectively. The C audit sample
generally had the lowest concentrations for
most of the parameters. The lowest difference
was in the Bw horizon for pH and the highest
difference was in the C audit sample for
exchangeable acidity. Overall, the laboratories
Table 4-2. Summary of Interlaboratory Differences by Laboratory and by Audit Sample Type
Parameters
Specific surface
Sand & Silt fractions
Sand, Silt, Clay
Soil pH
Cations in NH4CI
Cations in NH4OAc
CEC
Exchangeable acidity
Cations in CaCU
Extractable Fe & Al
Extractable sultate
Sulfate isotherms
Total C, N, S
Interlaboratory difference (percent)
Laboratory Sample type —
L1 L2 L3 A Bs Bw
10.5
7.0
4.6
0.8
4.2
4.1
6.2
5.7
10.5
66
5.6
37
3.9
25.0
8.9
5.7
0.7
7.8
12.0
17.6
3.6
8.0
7.3
2.3
4.3
4.7
17.9
6.0
2.0
1.6
9.7
11.3
22.4
9.4
9.7
10.0
5.1
1.6
5.8
18.0
7.0
4.3
0.9
6.7
7.8
13.5
4.3
10.9
7.5
4.0
3.2
4.3
2.1
3.0
4.3
0.8
14.1
3.7
5.5
1.2
7.2
5.5
12.7
3.1
8.1
17.2
7.9
6.3
0.7
7.5
14.6
23.5
12.4
7.0
7.4
4.0
3.6
4.8
27.0
18.7
44.4
1.9
15.9
250
279
54.0
108
16.3
158
2.8
31.6
Number of parameters included
133
-------
showed the highest differences for the specific
surface and cation exchange capacity
parameters.
No single laboratory was consistently
superior to the others for all parameters or
parameter groups. Each laboratory appears to
have individual strengths in specific analytical
methods, which is probably a reflection of the
combination of experience, instrumentation,
and laboratory management and practice
within each laboratory. This resulted in a
patchwork of differences on a parameter
group basis, although a general trend existed
for the overall ranking of the laboratories.
In order to limit the interlaboratory differ-
ences in future surveys, it is recommended
that the DDRP staff consider the possibility of
choosing laboratories to perform analyses on
a parameter basis for those parameter groups
that revealed inherently high differences. One
laboratory would analyze all soil samples for
a given parameter or parameter group. This
approach would require an advanced quality
assurance program as well as a program for
establishing acceptable data values to monitor
laboratory performance.
It is recommended that a more stringent
laboratory selection procedure be adopted in
the pre-evaluation process for the selection of
contract laboratories. Since one of the major
goals of any quality assurance program is
minimizing random and systematic errors,
selection of the best possible laboratories is
of primary importance. As shown, laboratories
differ substantially in their overall performance
for certain parameters.
It is recommended that an additional
type of soil audit sample be incorporated into
the quality assurance program to better moni-
tor the analytical results of the laboratories.
A quality control audit sample would have
known and acceptable analyte concentrations,
which the laboratory would be required to
duplicate within limits designated on a batch-
by-batch basis. The sample would not be
blind to the laboratory. If the analytical
results were not within designated intervals,
the batch would be reanalyzed in order to
bring the audit sample within tolerance specifi-
cations. This would ensure that each labora-
tory could meet a rigid standard for each
batch of samples analyzed. Batch error and
laboratory difference would be reduced.
Therefore, it is further recommended that the
laboratories be required to report the analytical
results of the analyses on a batch-by-batch
basis to the QA staff immediately after the
analysis of each batch.
It was noted that both the precision and
the difference estimates were high in some
cases. Soil analytical methods, especially
those which extract or exchange soil constitu-
ents rather than those which determine the
total amount present in the soil, are uniquely
composed of two main sources of error,
extraction error and instrumental error. The
former is assumed to be the main cause of
differences among the laboratories and is a
major reason to maintain interlaboratory ana-
lytical survey programs. Without distinguishing
between extraction and instrumental error,
however, it is not known whether one or both
errors are present and where to focus efforts
to reduce systematic bias. It is recommended
that liquid audit samples be incorporated into
the quality assurance program and be used to
differentiate between systematic bias resulting
from extraction or instrument sources.
It is recommended that the analytical
procedures for the specific parameters men-
tioned above be reviewed, tested, and modified
where appropriate. Since quality is a con-
tinuum, the need for data quality dictates the
objective chosen. This objective may or may
not be attainable with the current technology.
The analytical procedures within the methods
should be examined for their ability to accom-
plish the analysis at the level of detail speci-
fied. If the critical value for relative inter-
laboratory difference is set at 5 percent, then
the results of this survey suggest that only the
methods used for the soil pH and sulfate
isotherm parameters remain as viable meth-
ods. If a level of 10 percent is chosen, several
parameter groups still remain a problem (see
Table 4-2).
It is recommended that the issue of
properly assessing laboratory bias be ad-
dressed since the current approach using inter-
laboratory differences has limited utility. The
use of external laboratories to analyze the
audit samples according to established DDRP
methods would generate accepted "true"
values for comparison among laboratories.
134
-------
It is recommended that data quality
objectives for interlaboratory bias be estab-
lished. If only one laboratory exhibited a high
but known difference, correction factors could
conceivably be applied to the data set. In this
case, however, each of the four laboratories
exhibited high differences for certain param-
eters. Serious consideration should be given
to either (1) modifying the method or clarifying
a procedure within the method, or (2) replacing
the method altogether. A case in point for the
former is cation exchange capacity and for the
latter is the specific surface determination.
Representa tiveness
All pedons sampled were representative
of their respective sampling classes. The
preparation laboratories prepared analytical
samples of known and accepted quality.
In evaluating representativeness of the
quality assurance samples, some trends can
be noted. First, the field duplicates and prepa-
ration duplicates are representative of the
range and frequency distribution of the routine
samples for most parameters. Second, the
natural audit samples generally are represen-
tative of the concentration range of the routine
samples.
It is recommended that the soil sampling
and preparation protocols specify a method for
representative selection of field duplicates and
preparation duplicates. The selection protocol
should be reiterated to the field and laboratory
personnel during the pre-sampling training
sessions. The quality assurance field auditor
should ensure that a sufficient amount of soil
is collected for each bulk sample during the
sampling effort to allow a preparation dupli-
cate to be subsampled.
Completeness
Sampling of the specified pedons had a
completeness level of 96.5 percent. Process-
ing was accomplished for 100 percent of the
pedon samples received by the preparation
laboratories. The analytical completeness level
exceeded 99 percent for all parameters.
Sufficient data were generated to make con-
clusions for each parameter in the data bases.
Comparability
Sampling, preparation, and analytical
methods and protocols for the DDRP Southern
Blue Ridge Province Soil Survey were com-
parable and nearly identical to those used for
the DDRP Northeastern Soil Survey. As de-
scribed in Section 3, the data user is cautioned
that some of the field duplicate data may not
be comparable for certain applications. It is
recommended that the statistical approach
undertaken for this report be applied to the
Northeastern survey data bases and a com-
parable report be generated for the benefit of
DDRP data users.
135
-------
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Arnold, S. F. 1981. The Theory of Linear
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Stapanian, F. C. Garner, and D. S. Coffey.
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Hudson, and R. A. Goldstein. 1984.
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Integrated Lake-Watershed Acidification
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Precipitation Impacts, J. L. Schnoor (Ed.),
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Remortel, M. L. Papp, and G. R. Holdren.
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the United States, Volume I: Sampling.
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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
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Raleigh, North Carolina.
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Southern Blue Ridge Province of the
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Dunaway, D. D. Schmoyer, and J. A.
Watts. 1987. Direct/Delayed Response
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137
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Appendix A
Verification Flags Used in the
Southern Blue Ridge Province Soil Survey
Following is a list of the data qualifiers, i.e., flags, that were applied to specific data in the
DDRP Southern Blue Ridge Province Soil Survey data bases. Data users can examine which flags
may be relevant to them for the purposes of a specific analysis. Acronyms and descriptions of the
flags are presented in alpha-numeric order.
Table A-1. Flaga Used In the DDRP Southern Blue Ridge Province Soil Survey
Reagent/Calibration Blanks
B3 R-blank > 2x CRDL (reagent blank flag)
B4 Blank value is negative
B5 R-blank > CRDL
B6 C-blank > CRDL
B7 C-blank > 1.05 x R-blank
B8" SP_SUR: calibration blank > 1 milligram
W1 R-blank > 0.5 x sample value (sample < 2 x R-blank)
W2" pH measurements: R-blank <6 or >7
Quality Control Check Samples
Q1 QCCS was above contractual criteria
Q2 QCCS was below contractual criteria
Q3 Insufficient number of QCCS measured
Q4 Theoretical DL-QCCS > 3 x CRDL
Q5" Measured DL-QCCS was not within 20% of theoretical value
Q6" Measured & theoretical DL-QCCS is negative
Duplicates
F1 Field duplicate precision > 10% RSD and both routine and duplicate values > 10 x CRDL
F2 Field duplicate pH precision > 10% RSD
F3 Field duplicate particle size precision > 10% RSD and both routine and duplicate values > 5.0 wt%
P1 Preparation duplicate precision > 10% RSD and both routine and duplicate sample concentrations > 10 x CRDL
P2 Preparation duplicate pH precision > 10% RSD
P3 Preparation duplicate particle size precision > 10% RSD and both routine and duplicate values > 5.0 wt %
A1 Audit duplicate > 10% RSD and both audit pair concentrations > 10 x CRDL
A2 Audit sample pH precision > 10% RSD
A3 Audit sample particle size precision > 10% RSD and both routine and duplicate values > 5.0 wt%
D1 Analytical duplicate precision > 10% RSD and both the routine and duplicate samples > 10 x CRDL
D2 Analytical duplicate pH precision > 10% RSD
D3 Analytical duplicate particle size precision > 10% RSD and both routine and duplicate values > 5.0 wt%
Matrix Spike
S1 Percent recovery of matrix spike was above (>115%) contractual criteria
S2 Percent recovery of matrix spike was below (<85%) contractual criteria
(continued)
138
-------
Table A-1. Continued
Instrument Detection Limit
L1 IDL > CRDL
Sulfur Relation Determination
KO Elemental parameter out of range; CJTOT > 60% and S_TOT > 0.5%
K1 Organic soil and SO4_H20 > 1.05 x SO4_PO4 (only if both are > 5 mg/kg): both values flagged
K2 Mineral soil and SO4JH2O > 1.05 x SO4 PO4 (only if both are > 5 mg/kg): both values flagged
K5 Organic: SO4_H2O or SO4_PO4 not in range 0- to 100-mg/kg; Mineral: SO4_PO4 or SO4JH2O not in range
0- to 250-mg/kg
K6 Organic sample doesn't meet following criteria: SO4 0 0-20(mg S/L), SO4 2 2: 22, SO4 4 a 24, SO4_8 z. 28,
SO4J6 a 36, SO4_32 & 52 or Sample doesn't fall within following relationship: SO4_0~< SO4_2 < SO4 4 <
S04 8 < SO4 16 < SO4_32 (organic and mineral)
K7 Ratio of S04_H20:S04_0 flagged when ratio <5 or >25
Iron/Aluminum Determination
M1 Flag if AL_KCL < AL_CL2
Miscellaneous
AO* Value missing
XO6 Invalid but confirmed data based on QA/QC data review
X\"'b Invalidbul confirmed data; potential gross contamination of sample or parameter
X2* Invalid but confirmed data; potential sample switch
X3" Possible contamination due to either sampling technique, e.g., bucket augering, or soil ammendments, e.g.,
herbicides, liming, manure, etc.
X4* Outliers due to internal consistency check; data checked only for transcription errors
Laboratory Tags
CC Soil retrieved from disqualified analytical laboratory
JJ Insufficient soil for analysis/reanalysis
RR Reanalyzed
UU Unnecessary for parameter
XX No sample (initiated at preparation laboratory)
" New flag - not on list of flags distributed 7/87.
b Sample flag - parameter flagged only for affected samples.
139
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Appendix B
Data Verification Worksheets and Tables
Data verification was accomplished using the DDRP Quality Assurance Plan (Bartzet al., 1987)
as a guideline. Figures B-1 and B-2 from that document serve as examples of some of the
prominent quality assurance worksheets used during data verification. Also provided is the Data
Verification Template in Figures B-3 through B-13. The template was used to guide the quality
assurance staff through the often complex procedures used to verify the data. The template was
developed by the Soils Quality Assurance Section of Lockheed Engineering and Sciences Company
in Las Vegas, Nevada.
The latter portion of Appendix B includes data from some of the primary verification activities.
Tables B-1, B-2, and B-3 provide information on the quality control check sample compliance, the
internal consistency checks, and the analytical completeness assessment.
140
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DATE RECEIVED
UDRP SOU. SURVEY
FORM 300
Data Confirmation/Reanalysis Request Form
Contractor Analytical Laboratory Laboratory Supervisor
Ihe following suspect data values require: Confirmation (See I)
Reanalysis (See II)
PARAMIILR
DORP
H)RM NO.
SAMPI 1.
10
1
SOSPFCI
ORIGINAL
VALOL
RICONFIRHLD
NEW
VALOL
Fxplanatlon
CONIRACT
ANALYTICAL
LABORATORY
[
LLHSCO
I. Confirmation Request Did ANY values change: Yes _ No
If yes, reason (note auove in explanation column)
(A) Reporting trror (C) Original reported value did not change
(B) Calculation Error ([)) Data Previously Omitted
(E) Other -- Explain
Whether values arc changed or not. submit supporting RAW DATA.
Additional Comments Regarding Confirmation:
II Reanalysis Requested Due to-*
QA (External) Data
t)C (Internal) Data Indicated Below.
IDI > CRDL
Matrix Spire Recovery Outside Criteria
Replicate Precision (X PSD) Outside Criteria, Insufficient Number of Replicates
Blank > CROL (Reagent; Calibration)
OCCS Outside Criteria (DL, Low, High)
1C Resolution Value Below kO%
Air Dry Sample Weight Ouside Criteria
Total Sample Volume. Aliquot Volume, or Dilution Volume Outside Criteria
Standard Relationships Out of Range
Additional Comments Regarding Reanalysls.
All appropriate data forms including QC data forms must be submitted in support of reanalysis.
•Date form completed" must reflect date of reanalysis.
FOR LIHSCO USE ONLY. PRt.-VERIF ICAT10N
POST-VERIFICATION
NUMBER OF VALUES SUBMITTED
_ NUMBER OF VALUES CHANGED
ObOOC
Figure B-1. DDRP form 500 (data conflrmatlon/reanalysls request).
141
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I. OUTSTANDING ISSUES - CONTRACTOR ANALYTICAL LABORATORY
The following items that are identified as missing should be resubmitted
and problems should be resolved before verification is completed:
A. General (forms 102-108)
1. Required forms have been submitted.
2. Laboratory name, batch ID, preparation laboratory name,
laboratory manager's signature, date form completed, and date
batch received are included on all forms.
3. Correct data qualifiers (tags) were used as needed (see Table 1).
B. Data examination (forms 103-108)
1. Check that audit pairs are within established control criteria.
2. Estimate %RSD for all paired QA samples for each parameter, and
record in Table 3.
3. Check the internal consistency of the data.
a. Form 103a: pH, H20 > 0.002 > 0.01.
b. Form 103b: sand + silt + clay = 100 + 0.2.
c. Form 104d: CEC NH4OAc > CEC NH4C1.
d. Form 106: Ext. Sulfate, H20 < P04.
e. Form 106: Exch. Acidity, Bad 2 > KC1 .
f. Form 107: Sulfate Isotherms are 0 < 2 < 4 < 8 < 16 < 32.
Adsorption solution is within 5% of the theoretical
value.
g. Form 104c: Extraction ratio is 1:2 for mineral samples and
1:10 or 1:25 for organic samples.
h. Forms 103b and 108: For particle size analysis and specific
surface, organic samples are reported as a U.
C. General (forms 109-116)
1. Required forms have been submitted.
2. Laboratory name, batch ID, and laboratory manager's signature are
included on all forms.
D. Data examination (forms 109-116)
1. Forms 109a-c: Detection Limits
a. Check that instrumental detection limits (IDL) and
associated dates of determination are tabulated. IDL
should be updated monthly for each parameter.
b. IDL should be less than or equal to the contract-required
detection limit (CRDL) for each parameter.
2. Form HOa-c: Matrix Spikes
a. Identify samples used for spiking.
b. Check that percent recovery for matrix spikes is
reported for each parameter required.
Figure B-2. Data completeness checklist (1 of 3).
142
-------
c. Check that percent recovery is calculated correctly
(recalculate at least three per page).
d. Check that percent recovery is 100 +_ 152 for each parameter;
if it is not, then spiking must be repeated on two different
samples.
e. Verify that the level of spike is 10 times the CRDL or equal
to the endogenous level, whichever is greater.
f. Check that the sample used for Total S, N, and C is not
an organic sample for each batch.
3. Form llla-i: Replicates
a. Replicate precision results are reported for each parameter.
For pH and specific surface, triplicates are determined.
b. Correct equation is used to calculate %RSD (degrees of
freedom equal n-1).
c. %RSDs are 0-10% (except on fractionated sand and silt).
4. Forms 112a-h: Blanks and QCCS
a. Calibration blanks, reagent blanks, and detection limit (DL)
QCCS are reported where required.
b. Calibration and reagent blanks should be less than or equal
to the CRDL.
c. Form 112g: K-factors are reported correctly.
d. Form 112h: Three high EGME blanks are reported correctly.
e. DL QCCS theoretical values are approximately 2 to 3
times the CRDL, and the measured values are within
20% of the theoretical value.
f. QCCS true values are approximately in the midrange of
the reported sample values or of the calibration curve.
g. Initial, continuing, and final QCCS values are within
upper and lower control limits.
5. Form 113: Ion Chromatography
a. 1C resolution test results are reported.
b. Resolution value exceeds 60%.
c. Peaks are clean on chromatogram(s).
d. At least one chromatogram is provided for each day of
operation for each instrument.
6. Form 114: Standard Additions
a. Standard additions are performed and results are reported
when matrix spike results do not meet contractual requirements.
7. Forms 115a-e: Air Dry Sample Weights
a. The air-dried soil weight is reported for each parameter,
except for particle-size, analysis (oven dried) and specific
surface (?2 05 wt. = oven dried).
Figure B-2. Continued (2 of 3).
143
-------
b. Weights are reported correctly (see Table 2).
c. Form 115a: One sample is determined in triplicate for
moisture and specific surface.
d. Duplicates are reported correctly.
8. Forms 116a-h: Dilution Factors
a. Total sample volume, aliquot volume, total dilution volume,
dilution concentrations, and dilution blanks are recorded
for each sample.
E. Forms 200: Blank-corrected data
1. Required forms 204-208 have been submitted.
2. Laboratory name, batch ID, preparation laboratory name, manager's
signature, and date batch received are included on all forms.
3. Correct number of samples were analyzed, and the results for
each parameter are tabulated.
F. Forms 300: Raw Data
1. Required forms 303b-308 have been submitted.
2. Laboratory name, batch ID, preparation laboratory name, laboratory
manager's signature, and date batch received are included on all
forms.
3. Correct number of samples were analyzed, and the results for
each parameter are tabulated.
G. Reporting units are correct on the following forms (see Table 4):
1. 103-108
2. 109: Detection Limits
3. 110: Matrix Spikes
4. Ill: Replicates
5. 112: Blanks and QCCS
6. 115: Air Dry Sample Weights
7. 116: Dilution Factors/Concentration
8. 200: Blank-Corrected Data
9. 300: Raw Data
Figure B-2. Continued (3 of 3).
144
-------
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Table B-1. Occurrences of Less-Than-Complete Compliance for Measurement of Quality Control Check Sample*
Parameter
SP_SUR
VCOS
COS
MS
FS
VFS
SILT
COSI
FSI
MG_CL
K CL
NA_CL
MG OAC
K_O~AC
CEC CL
AC KCL
AC'BACL
ALjKCL
MG CL2
NA~CL2
ALJ2L2
AL_PYP
FE CD
AL~CD
SO4 H20
SO4 PO4
SO4_0
SO4 2
SO4 4
SO4 8
SO4 16
SO4I32
C TOT
N_TOT
S_TOT
Laboratory
2
3
1
2
3
1
2
3
1
2
3
1
3
2
3
2
2
3
2
1
2
1
1
1
1
2
2
2
1
1
2
2
1
2
2
2
2
2
2
3
2
3
2
2
2
3
2
3
3
n
29
27
47
45
42
47
45
42
47
45
42
47
42
45
42
45
45
42
45
54
56
54
54
54
54
55
54
52
52
54
58
58
54
57
58
58
55
55
54
42
52
42
51
53
52
41
55
41
41
Percent compliance
72.4
77.8
26.1
33.4
38.1
29.8
73.3
83.3
57.4
93.3
95.2
53.2
95.3
86.7
97.6
95.6
62.2
90.5
71.1
98.1
98.2
79.6
94.4
94.4
94.4
98.2
88.9
98.1
98.1
85.2
98.2
98.3
98.2
98.2
87.9
96.6
96.4
92.7
96.3
97.6
92.3
97.6
98.0
94.3
92.3
65.9
96.4
68.3
82.9
156
-------
Table B-2. Internal Consistency Checks Performed for the Southern Blue Ridge Province Analytical Verified Data
Base
Parameter
Correlations*
Data set6
1st check
2nd check
MOIST
SP SUR
SAND
VCOS
COS
FS
VFS
SILT
COSI
CLAY
PH H20
PH 002M
PH_01M
CA CL
MG CL
K CL
NA_CL
CEC CL
AC KCL
AClBACL
CA CL2
MG CL2
K CL2
NA CL2
FE CL2
AL_CL2
FE PYP
AL PYP
FE AO
AL_AO
S04 H2O
S04 0
SO4 2
SO4 4
SO4 8
SO4 16
SO4_32
N TOT
S_TOT
+/-
+/-
+I-, FSI
+/-, COS
+/-, MS
+/-, SAND
+/-, SAND
+/-, SAND
+/-. SILT
+1-
PH 01 M
PH H20
PH_002M
CA OAC
MG OAC
K OAC
NAJDAC
CEC OAC
AL KCL
C_TOT
+/-
+/-
+/-
+/-
+/-
+/-
FE AO
AL CD
FE CD
AL_CD
SO4 P04
SO4 H2O
SO4 H2O
SO4 H2O
SO4 H2O
SO4 H20
S04_H20
C TOT
N_TOT
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
0-M
O-M
O-M
O-M
O-M
O-M
A
O-M
X
X
.95
.62
.74
.67
X
.96
.80
X
.95
.98
.98
.94
.82
.92
.70
.99
.81
.92
X
X
X
X
X
X
.91
.89
.76
.86
.50
.93 .88
.97 .86
.96 .82
.96 .76
.89 .62
.89 .07
.95
.34 .76
X
X
.95
.62
.74
.67
X
.96
.80
X
.96
.98
.98
.96
.83
.92
.90
.89
.83
.92
X
X
X
X
X
X
.90
.89
.78
.76
.50
.94 .80
.97 .68
.96 .51
.96 .29
.90 .07
.89 .08
.95
.36 .66
" At times, a variable is used for more than one correlation; x = r^ not applicable; +/- = outlier check on the highest
and lowest 1% of the values.
b Routine samples used in correlation: A = all samples; O = organic samples only; M = mineral samples only.
157
-------
Table B-3. Completeness of Soil Analysis Using Data for Routine Samples from the Verified and Validated Data
Bases
\/ai-ifiA>-l \/altH*»tA^
Parameter
MOIST
SP SUR
SAND, SILT, CLAY
VCOS, COS, MS, VFS
FS, FSI
COSI
PH H2O
PH 002M
PH_01M
CA CL
MG CL
K Cl
NA_CL
CA OAC
MG~OAC
K OAC
NA_OAC
CEC CL
CEC OAC
AC KCL
AC~BACL
ALJ
-------
Appendix C
Table of Statistics for Step Function Precision Estimates
This table provides statistical information that supplements the precision results and
discussion found in Section 3. Included are data relating to the development of delta values,
including the standard deviations and proportions. These data can be used to assess the quality
of the routine sample data set on the basis of the quality of the QA sample data sets. The table
is sorted by parameter and subsorted by data set.
Table C-1. Table of Statistics for Step Function Precision Estimates
Parameter Data set Delta Window
MOIST AS 0.2715 0.0-1.0
1.0-2.0
2.0-3.0
3.0-5.0
5.0-inf
PD 0.1503 0.0-1.0
1.0-2.0
2.0-3.0
3.0-5.0
5.0-inf
FD 0.2782 0.0-1.0
1.0-2.0
2.0-3.0
3.0-5.0
5.0-inf
S/H 1.0249 0.0-1.0
1.0-2.0
2.0-3.0
3.0-5.0
5.0-inf
df
6
17
25
2
4
8
12
2
15
34
37
14
4
15
291
210
82
11
Mean
0.15
1.79
2.36
3.42
0.78
1.55
2.48
6.24
0.70
1.59
2.46
3.53
5.88
0.74
1.59
2.40
3.73
6.14
Within-
batch SO
0.0105
0.1153
0.1058
1.3655
0.0871
0.1515
0.1566
o!l276
0.0673
0.1867
0.2659
0.3959
2.3188
0.5516
0.6811
1.1486
1.9585
2.1173
Pairs >DQO Between-
n % batch SD
0.0082
0.1782
0.1287
0.4091
Plil
0.036
0.482
0.334
0.128
0.020
(continued)
159
-------
Tabl* C-1. Continued
Parameter Data set
SP_SUR AS
PD
FD
S/H
SAND AS
PD
FD
S/H
VCOS AS
PD
FD
S/H
Delta
2.8038
2.4631
4.3893
15.3351
3.2861
1.6508
2.1400
13.0018
0.6918
0.8507
0.9102
3.1052
Window
0.0-20.0
20.0-35.0
35.0-50.0
50.0-60.0
60.0-inf
0.0-20.0
20.0-35.0
35.0-50.0
50.0-60.0
60.0-inf
0.0-20.0
20.0-35.0
35.0-50.0
50.0-60.0
60.0-inf
0.0-20.0
20.0-35.0
35.0-50.0
50.0-60.0
60.0-inf
0.0-25.0
25.0-40.0
40.0-50.0
50.0-65.0
65.0-inf
0.0-25.0
25.0-40.0
40.0-50.0
50.0-65.0
65.0-inf
0.0-25.0
25.0-40.0
40.0-50.0
50.0-65.0
65.0-inf
0.0-25.0
25.0-40.0
40.0-50.0
50.0-65.0
65.0-inf
0.0-2.0
2.0-5.0
5.0-7.0
7.0-10.0
10.0-inf
0.0-2.0
2.0-5.0
5.0-7.0
7.0-10.0
10.0-inf
0.0-2.0
2.0-5.0
5.0-7.0
7.0-10.0
10.0-inf
0.0-2.0
2.0-5.0
5.0-7.0
7.0-10.0
10.0-inf
df
10
15
25
t
t
7
6
8
3
2
22
33
25
14
8
63
307
177
52
9
4
3
1
25
17
1
1
8
10
6
6
16
23
36
21
5
40
133
315
115
26
12
1
5
6
6
10
3
5
2
27
44
16
5
9
44
361
105
84
14
Mean
8.54
26.30
41.61
m
12^51
26.62
41.28
55.16
76.26
12.55
28.02
40.56
53.89
75.84
15.70
29.07
42.74
54.30
86.97
24.34
30.70
46.50
56.87
88.96
14.25
39.75
46.46
59.21
79.15
17.33
31.70
44.80
57.57
76.17
9.56
34.74
44.61
57.32
71.95
0.97
2.70
6.65
8.47
10.90
0.66
3.25
5.82
8.44
12.35
1.06
3.32
5.88
8.66
13.83
1.07
3.51
6.35
8.31
14.11
Within-
batch SD
1.5527
2.9952
2.9879
2^6175
1.8572
1.8821
7.9749
2.0807
2.3343
4.1471
3.8869
10.4138
4.9491
7.8331
13.1748
19.5647
23.7949
19.7507
0.4809
2.8213
11.5966
0.6598
1.1548
0.4950
0.2121
1.1150
2.6087
0.3162
1.2517
1.2618
3.8792
1.8054
1.4229
9.2390
14.5129
11.0756
14.0928
11.9727
0.2033
0.6618
0.9192
0.6025
2.0469
0.2291
0.5599
1.6376
1.3784
1.6125
0.2228
0.7352
1.0109
1.6420
2.7438
1.0098
2.1911
4.1229
6.1835
8.1345
Pairs >DQO
n %
2 66.7
1 100.0
3 12.0
5 29.4
1 12.5
2 20.0
3 50.0
4 25.0
11 47.8
11 30.1
6 27.9
Between-
batch SD
1.1464
5.0913
3.6237
.
E(J1
0.118
0.500
0.283
0.083
0.016
0.2562
3.7041
2.1122
1.3601
PJl
0.010
0.068
0.218
0.509
0.195
0.2943
0.4318
.
0.9660
0.7550
Elil
0.090
0.580
0.170
0.135
0.026
(continued)
160
-------
Table C-1. Continued
Parameter Data set
COS AS
PD
FD
S/H
MS AS
PD
FD
S/H
FS AS
PD
FD
S/H
Delta
1.1928
1.0244
0.8614
4.8611
0.4506
0.4251
0.8863
5.4795
1.5968
0.6694
0.8903
6.4465
Window
0.0-6.0
6.0-10.0
10.0-15.0
15.0-inf
0.0-6.0
6.0-10.0
10.0-15.0
15.0-inf
0.0-6.0
6.0-10.0
10.0-15.0
15.0-inf
0.0-6.0
6.0-10.0
10.0-15.0
15.0-inf
0.0-7.0
7.0-15.0
15.0-20.0
20.0-inf
0.0-7.0
7.0-15.0
15.0-20.0
20.0-inf
0.0-7.0
7.0-15.0
15.0-20.0
20.0-inf
0.0-7.0
7.0-15.0
15.0-20.0
20.0-inf
0.0-5.0
5.0-10.0
10.0-15.0
15.0-25.0
25.0-inf
0.0-5.0
5.0-10.0
10.0-15.0
15.0-25.0
25.0-inf
0.0-5.0
5.0-10.0
10.0-15.0
15.0-25.0
25.0-inf
0.0-5.0
5.0-10.0
10.0-15.0
15.0-25.0
25.0-inf
df
33
1
4
12
5
11
6
4
42
29
21
10
126
185
250
47
7
26
17
4
12
7
3
32
46
14
10
49
468
84
7
7
.
32
11
4
6
11
5
6
19
24
38
15
4
19
160
400
25
Mean
3.30
9.85
11.66
19.53
3.46
7.62
11.91
21.77
3.25
8.11
12.34
19.59
3.87
7.94
11.47
17.87
3.51
8.27
28'04
5.34
10.89
16.48
23.50
3.82
10.60
17.29
24.08
5.11
11.56
16.87
23.52
3.91
t
22.83
31.51
t
6.52
13.31
18.91
28.12
3.17
7.51
12.37
19.89
28.37
0.94
8.49
12.34
18.70
27.56
Within-
batch SD
0.2926
0.2121
2.2122
2.3310
0.2530
2.2702
0.4787
1.0989
0.3901
1.0590
0.9607
0.8823
2.3924
4.1560
5.7904
9.9343
0.1000
0.4731
1.2580
0.1369
0.4026
0.6745
0.9000
0.1772
1.0184
0.6285
1.0644
3.2163
5.4426
6.9530
7.3224
0.1225
15760
2.0641
.
0.3317
0.6922
0.6882
0.5916
0.1155
0.5319
1.1724
0.8227
0.7443
0.2510
4.7832
5.4761
6.9114
7.5823
Pairs >DQO Between-
n % batch SD
0.3154
.
1.4447
1.6977
pm
0.217
0.306
0.403
0.074
0.1600
0.3725
.
1.3142
fill
0.094
0.748
0.138
0.020
0.3297
t
.
1.6669
1.9776
P(il
0.007
0.041
0.254
0.647
0.051
(continued)
161
-------
Table C-1. Continued
Parameter Data set
VFS AS
PD
FD
S/H
SILT AS
PD
FD
S/H
COSI AS
PD
FD
S/H
Delta
0.9815
0.7067
0.9105
5.2680
3.0484
1.9100
1.5611
10.2079
1.0200
0.9736
1.1769
5.0119
Window
0.0-8.0
8.0-14.0
14.0-20.0
20.0-inf
0.0-8.0
8.0-14.0
14.0-20.0
20.0-inf
0.0-8.0
8.0-14.0
14.0-20.0
20.0-inf
0.0-8.0
8.0-14.0
14.0-20.0
20.0-inf
0.0-12.0
12.0-25.0
25.0-35.0
35.0-45.0
45.0-inf
0.0-12.0
12.0-25.0
25.0-35.0
35.0-45.0
45.0-inf
0.0-12.0
12.0-25.0
25.0-35.0
35.0-45.0
45.0-inf
0.0-12.0
12.0-25.0
25.0-35.0
35.0-45.0
45.0-inf
0.0-6.0
6.0-15.0
15.0-inf
0.0-6.0
6.0-15.0
15.0-inf
0.0-6.0
6.0-15.0
15.0-inf
0.0-6.0
6.0-15.0
15.0-inf
df
8
13
11
18
5
14
4
3
24
36
30
12
20
444
114
30
7
19
16
1
7
2
12
7
5
6
39
26
22
9
1
234
293
71
9
6
35
9
5
19
2
22
61
19
20
577
11
Mean
6.94
10.28
18.19
21.61
5.93
11.19
16.66
22.37
5.96
10.80
16.25
25.57
5.50
11.12
16.51
21.84
5.44
17.31
26.71
38.20
65.45
8.95
19.03
30.14
39.43
e'ss
19.56
30.24
39.62
52.17
8.32
20.97
28.39
37.57
55.33
4.07
10.15
31.13
4.81
9.29
16.77
4.46
10.35
19.76
4.83
9.89
17.66
Within-
batch SD
0.8846
0.9009
1.2454
1.1533
0.2588
0.6170
1.2171
0.4435
0.4985
0.7190
1.5143
1.5215
2.7120
4.9620
6.1405
7.8953
1.0021
1.2367
1.4472
15.2735
2.0296
0.3808
2.1064
2.0032
1.0193
0^5679
1.6110
1.4116
2.1105
1.1385
3.1113
9.7470
10.6545
10.1350
11.4662
1.0042
0.8494
7.2708
3.3092
0.8851
0.7018
0.4880
1.1712
2.4179
4.3080
5.0333
5.2882
Pairs >DQO
n %
2 28.6
6 31.6
2 12.5
1 100.0
2 28.6
2 16.7
3 42.9
2 40.0
1 15.4
8 20.5
11 42.3
8 35.6
2 22.2
Between-
batch SD
0.2696
1.0197
0.6924
0.9327
Piil
0.043
0.705
0.192
0.060
2.0491
0.9544
0.5231
4.1057
P(i\
0.007
0.379
0.476
0.121
0.017
2.0745
1.3269
2.4468
£01
0.038
0.936
0.026
(continued)
162
-------
Table C-1. Continued
Parameter
FSI
CLAY
PHJH20
PH_002M
PH_01M
CA_CL
Data set
AS
PD
FD
S/H
AS
PD
FD
S/H
AS
PD
FD
S/H
AS
PD
FD
S/H
AS
PD
FD
S/H
AS
PD
FD
S/H
Delta
0.8048
1.2626
1.2922
7.2695
1.3815
0.7144
1.4309
6.5929
0.0349
0.0350
0.1009
0.3331
0.0361
0.0253
0.0917
0.3433
0.0354
0.0307
0.0846
0.3516
0.0309
0.0329
0.1608
0.8124
Window
0.0-10.0
10.0-20.0
20.0-30.0
30.0-inf
0.0-10.0
10.0-20.0
20.0-30.0
30.0-inf
0.0-10.0
10.0-20.0
20.0-30.0
30.0-inf
0.0-10.0
10.0-20.0
20.0-30.0
30.0-inf
0.0-10.0
10.0-25.0
25.0-inf
0.0-10.0
10.0-25.0
25.0-inf
0.0-10.0
10.0-25.0
25.0-inf
0.0-10.0
10.0-25.0
25.0-inf
4.0-inf
4.0-inf
4.0-inf
4.0-inf
3.5-inf
3.5-inf
3.5-inf
3.5-inf
3.0-inf
3.0-inf
3.0-inf
3.0-inf
0.0-0.2
0.2-1.0
1.0-4.0
4.0-inf
0.0-0.2
0.2-1.0
1.0-4.0
4.0-inf
0.0-0.2
0.2-1.0
1.0-4.0
4.0-inf
0.0-0.2
0.2-1.0
1.0-4.0
4.0-inf
df
18
25
1
6
7
11
8
12
55
27
8
37
439
124
8
21
26
5
16
5
17
66
19
83
455
70
50
26
104
609
50
26
104
609
50
26
104
609
9
41
,
17
4
5
56
32
11
4
224
303
66
16
Mean
3.09
14.14
27.65
32.41
7.61
15.89
24.06
6.36
14.74
24.19
37.25
8.24
15.58
23.48
38.42
3.44
17.95
5.67
15.98
33.72
5.70
17.45
36.20
7.14
17.15
33.71
4.73
5.17
5.03
5.08
4.38
4.59
4.50
4.55
4.19
4.43
4.33
4.39
0.14
0.29
o!io
0.52
2.36
0'09
0.52
1.79
4.37
0.10
0.47
1.89
6.27
Within-
batch SD
1.2137
0.8212
0.6364
0.5708
0.2087
1.2412
1.6750
0.4770
1.0247
2.3842
2.2773
5.2270
7.1082
8.1474
11.2703
0.5189
1.5467
0.6107
0.7278
0.7563
0.8438
1.4499
2.0403
3.5364
6.5828
10.4791
0.0349
0.0350
0.1009
0.3331
0.0361
0.0253
0.0917
0.3433
0.0354
0.0307
0.0846
0.3516
0.0250
0.0355
o!o314
0.0179
0.1039
0'0308
0.1320
0.3443
1.5367
0.1179
0.6925
2.0407
6.7793
Pairs
n
1
6
3
1
2
17
5
8
1
5
1
4
1
3
4
9
11
1
1
>DQO
%
4.8
23.1
18.8
20.0
11.8
25.4
26.3
7.7
2.0
4.8
2.0
3.8
11.1
7.3
23.5
15^8
35.5
9.1
25.0
Between-
batch SD
0.5142
1.2703
17588
P(i)
0.067
0.708
0.208
0.017
0.6286
1.1777
t
P(i)
0.142
0.744
0.114
0.0871
Pfjl
1.000
0.0849
P(i)
1.000
0.0587
P(i)
1.000
0.0311
0.0489
_
fill
0.380
0.479
0.110
0.031
(continued)
163
-------
Table C-1. Continued
Parameter Data set
MG_CL AS
PD
FD
S/H
K_CL AS
PD
FD
S/H
NA_CL AS
PD
FD
S/H
CAJDAC AS
PD
FD
S/H
Delta
0.0083
0.0234
0.0668
0.2547
0.0116
0.0110
0.0237
0.1004
0.0116
0.0094
0.0154
0.0312
0.0261
0.0724
0.1543
0.8353
Window
0.0-0.2
0.2-0.5
0.5-inf
0.0-0.2
0.2-0.5
0.5-inf
0.0-0.2
0.2-0.5
0.5-inf
0.0-0.2
0.2-0.5
0.5-inf
0.0-0.2
0.2-0.4
0.4-inf
0.0-0.2
0.2-0.4
0.4-inf
0.0-0.2
0.2-0.4
0.4-inf
0.0-0.2
0.2-0.4
0.4-inf
0.0-0.05
0.05-0.07
0.07-0.2
0.2-inf
0.0-0.05
0.05-0.07
0.07-0.2
0.2-inf
0.0-0.05
0.05-0.07
0.07-0.2
0.2-inf
0.0-0.05
0.05-0.07
0.07-0.2
0.2-inf
0.0-0.2
0.2-0.5
0.5-1.5
1.5-inf
0.0-0.2
0.2-0.5
0.5-1.5
1.5-inf
0.0-0.2
0.2-0.5
0.5-1.5
1.5-inf
0.0-0.2
0.2-0.5
0.5-1.5
1.5-inf
df
29
21
_
13
7
6
59
28
17
279
268
62
23
25
1
19
5
2
80
21
3
476
132
1
38
6
4
19
3
2
.
90
8
3
1
523
74
12
17
33
_
15
3
4
4
52
19
21
9
218
158
190
43
Mean
0.07
0.22
.
0'08
0.30
0.74
0.08
0.31
0.77
0.13
0.32
0.65
0.04
0.25
0.57
0.09
0.28
0.56
0.09
0.28
0.53
0.12
0.27
0.47
0.02
0.06
0.08
.
0.03
0.06
0.10
0'03
0.05
0.09
0.20
0.03
0.06
0.10
0.14
0.27
o!o9
0.29
0.84
2.45
0.09
0.33
0.86
3.06
0.11
0.32
0.82
3.87
Within-
batch SD
0.0073
0.0094
o!oi40
0.0099
0.1189
0.0250
0.0372
0.3683
0.1147
0.3489
0.4968
0.0102
0.0109
0.4610
0.0093
0.0170
0.0212
0.0185
0.0398
0.2341
0.0817
0.1651
0.2758
0.0104
0.0112
0.0545
0.0085
0.0165
0.0060
0.0115
0.0351
0.0519
0.0219
0.0248
0.0580
0.1214
0.0220
0.0322
p
.
o!o214
0.1298
0.0472
0.2309
0.0270
0.0658
0.1087
1.2389
0.1259
0.3672 »
1.2118
4.3309
Pairs
n
_
i
1
7
5
1
1
10
5
1
1
3
5
1
2
2
8
_
i
1
1
12
7
6
3
>DQO
%
7.7
16.7
11.9
17.9
5.9
100.0
12.5
23.8
33.3
2.6
75.0
5.6
12.5
66.7
11.8
24.2
6.7
33.3
25.0
23.1
36.8
28.6
33.3
Between-
batch SD
0.0113
0.0166
_
P(il
0.469
0.424
0.107
0.0065
0.0173
PJJ1
0.780
0.218
0.003
0.0099
0.0039
0.0034
.
Oil
0.855
0.121
0.024
0.0392
0.0490
_
Elil
0.373
0.250
0.300
0.077
(continued)
164
-------
Table C-1. Continued
Parameter
MG_OAC
K_OAC
NA_OAC
Data set
AS
PD
FD
S/H
AS
PD
FD
S/H
AS
PD
FD
S/H
Delta
0.0106
0.0152
0.0367
0.2650
0.0219
0.0163
0.0342
0.0880
0.0109
0.0058
0.0122
0.0326
Window
0.0-0.1
0.1-0.2
0.2-0.6
0.6-1.0
1.0-inf
0.0-0.1
0.1-0.2
0.2-0.6
0.6-1.0
1.0-inf
0.0-0.1
0.1-0.2
0.2-0.6
0.6-1.0
1.0-inf
0.0-0.1
0.1-0.2
0.2-0.6
0.6-1.0
1.0-inf
0.0-0.1
0.1-0.2
0.2-inf
0.0-0.1
0.1-0.2
0.2-inf
0.0-0.1
0.1-0.2
0.2-inf
0.0-0.1
0.1-0.2
0.2-inf
0.0-0.0
0.0-0.1
0.1-inf
0.0-0.0
0.0-0.1
0.1-inf
0.0-0.0
0.0-0.1
0.1-0.2
0.2-inf
0.0-0.0
0.0-0.1
0.1-inf
df
24
1
25
.
f
9
3
10
3
1
43
15
32
11
3
54
177
355
22
1
24
26
10
8
7
47
32
25
165
321
123
44
4
.
21
2
2
89
12
i
515
83
10
Mean
0.05
0.20
0.24
0.05
0.16
0.35
0.80
1.13
0.06
0.16
0.35
0.75
1.22
0.07
0.15
0.35
0.78
1.67
0.04
0^26
0.06
0.12
0.33
0.06
0.14
0.30
0.07
0.14
0.27
0.03
0.07
0.02
0.06
0.13
0.03
0.06
_
o!26
0.03
0.06
0.15
Within-
batch SD
0.0064
0.0014
0.0162
o!o072
0.0071
0.0131
0.1185
0.0742
0.0172
0.0343
0.0317
0.1296
0.6794
0.0463
0.1341
0.3481
0.6105
0.6873
0.0087
o!()410
0.0083
0.0089
0.0468
0.0114
0.0224
0.0973
0.0398
0.0885
0.1567
0.0070
0.0342
.
0!0047
0.0104
0.0184
0.0115
0.0167
0.0233
0.0234
0.0641
0.2127
Pairs
n
1
t
1
2
4
4
1
1
3
1
2
5
7
1
1
1
_
>DQO
%
4.0
33.3
4.7
26.7
12.5
9.1
33.3
115
14.3
4.3
14.7
29.2
25.0
_
1.1
8.3
Between-
batch SD
0.0123
0.0181
t
Elil
0.114
0.283
0.559
0.041
0.003
0.0105
.
0.0269
Elil
0.289
0.512
0.199
0.0089
0.0148
_
PJl
0.838
0.139
0.018
(continued)
165
-------
Table C-1. Continued
Parameter Data set
CEC_CL AS
PD
FD
S/H
CEC_OAC AS
PD
FD
S/H
AC_KCL AS
PD
FD
S/H
AC_BACL AS
PD
FD
S/H
Delta
0.6535
0.8022
1.0434
3.8552
1.4975
1.0692
2.1533
6.2257
0.2289
0.2523
0.4554
1.5754
2.3814
1.7449
2.6042
7.8735
Window
0.0-2.5
2.5-8.0
8.0-15.0
15.0-inf
0.0-2.5
2.5-8.0
8.0-15.0
15.0-inf
0.0-2.5
2.5-8.0
8.0-15.0
15.0-inf
0.0-2.5
2.5-8.0
8.0-15.0
15.0-inf
0.0-2.5
2.5-8.0
8.0-16.0
16.0-inf
0.0-2.5
2.5-8.0
8.0-16.0
16.0-inf
0.0-2.5
2.5-8.0
8.0-16.0
16.0-inf
0.0-2.5
2.5-8.0
8.0-16.0
16.0-inf
0.0-2.5
2.5-4.5
4.5-inf
0.0-2.5
2.5-4.5
4.5-inf
0.0-2.5
2.5-4.5
4.5-inf
0.0-2.5
2.5-4.5
4.5-inf
0.0-2.5
2.5-10.0
10.0-30.0
30.0-inf
0.0-2.5
2.5-10.0
10.0-30.0
30.0-inf
0.0-2.5
2.5-10.0
10.0-30.0
30.0-inf
0.0-2.5
2.5-10.0
10.0-30.0
30.0-inf
df
6
26
18
1
16
6
3
6
60
33
5
1
343
242
23
6
16
28
7
13
6
2
28
43
• 31
1
78
375
155
12
35
3
17
4
5
63
32
9
402
153
54
6
33
11
15
9
2
3
46
47
8
1
256
341
11
Mean
1.14
6.63
9.59
2.02
5.60
11.35
19.11
2.06
5.53
11.18
19.17
1.65
5.84
10.04
16.76
1.43
14.36
20.58
,
5.15
11.58
28.38
1.87
6.04
12.21
23.10
1.39
5.68
11.25
23.32
0.94
3.49
5.02
1.11
3.45
6.40
1.27
3.36
6.57
1.54
3.31
5.89
0.99
18^34
34.94
6'44
13.56
47.64
2.06
6.80
16.15
34.52
1.11
6.87
16.53
45.36
Within-
batch SD
0.4028
0.4715
0.9277
.
0.1676
0.7372
0.7286
2.5525
0.3745
0.6343
1.4862
2.7369
0.2475
2.9598
4.9852
6.3937
0.1333
17216
0.9934
0^5376
0.5799
2.5168
0.1115
1.3210
2.1032
2.7704
0.1838
2.7096
4.8001
11.6441
0.1106
0.4592
0.5019
0.0635
0.7769
0.2512
0.3770
0.6298
0.5756
1.0775
1.8788
4.5821
0.5059
2.4070
2.0840
0!9713
2.2901
3.6708
0.3227
1.2158
3.3417
13.1190
0.1768
4.9756
10.1933
9.1530
Pairs
n
2
4
2
3
1
2
2
20
7
3
4
1
2
1
7
10
8
1
1
4
3
1
3
2
4
3
11
10
1
>DQO
%
33.3
15.4
11.1
.
18.8
16.7
66.7
33.3
33.3
21.2
60.0
25.0
3.6
28.6
7.7
25.0
23.3
25.8
2.9
25.0
6.3
9.4
11.1
50.0
e!i
267
33.3
23.9
21.3
12.5
Between-
batch SD
0.4537
0.6417
1.3158
M
0.007
0.569
0.384
0.040
0.4756
.
1.7984
1.5125
Piil
0.004
0.142
0.599
0.255
0.2669
0.4492
0.1167
pfu
0.671
0.242
0.087
0.7581
2^3378
2.0669
P(Q
0.004
0.432
0.543
0.020
(continued)
166
-------
Table C-1. Continued
Parameter
AL_KCL
CA_CL2
MG_CL2
K_CL2
Data set
AS
PD
FD
S/H
AS
PD
FD
S/H
AS
PD
FD
S/H
AS
PD
FD
S/H
Delta
0.1644
0.2874
0.3124
1.5319
0.0819
0.0244
0.0332
0.1923
0.0085
0.0095
0.0179
0.0586
0.0046
0.0024
0.0130
0.0188
Window
0.0-2.5
2.5-5.0
5.0-inf
0.0-2.5
2.5-5.0
5.0-inf
0.0-2.5
2.5-5.0
5.0-inf
0.0-2.5
2.5-5.0
5.0-inf
0.0-0.5
0.5-1.0
1.0-inf
0.0-0.5
0.5-1.0
1.0-inf
0.0-0.5
0.5-1.0
1.0-inf
0.0-0.5
0.5-1.0
1.0-inf
0.0-0.05
0.05-0.1
0.1-0.2
0.2-inf
0.0-0.05
0.05-0.1
0.1-0.2
0.2-inf
0.0-0.05
0.05-0.1
0.1-0.2
0.2-inf
0.0-0.05
0.05-0.1
0.1-0.2
0.2-inf
0.0-0.05
0.05-inf
0.0-0.05
0.05-inf
0.0-0.05
0.05-inf
0.0-0.05
0.05-inf
df
14
36
17
6
3
73
24
6
412
163
34
25
25
13
13
49
53
2
345
264
24
2
24
8
5
10
3
36
25
36
7
64
266
277
2
27
23
22
4
94
10
535
74
Mean
0.95
3.25
oise
3.40
8.16
1.25
3.29
6.90
1.28
3.17
6.01
0.38
0.58
oise
0.62
0.37
0.60
1.46
0.43
0.58
0.03
0.09
0.13
0'02
0.07
0.15
0.24
0.03
0.07
0.14
0.30
0.04
0.07
0.13
0.28
0.02
0.07
0.01
0.07
0.02
0.10
0.02
0.06
Within-
batch SD
0.1231
0.2754
o!l362
0.6320
0.5739
0.2498
0.4369
0.5138
1.0008
2.2599
4.7630
0.1188
0.0365
o!fl177
0.0326
0!0335
0.0329
1.4871
0.1308
0.2681
•
0.0035
0.0075
0.0108
o!ooi7
0.0083
0.0129
0.0071
0.0032
0.0164
0.0206
0.1882
0.0283
0.0508
0.0742
0.0827
0.0045
0.0052
0.0020
0.0054
0.0045
0.0759
0.0167
0.0339
Pairs
n
2
,
1
3
3
15
9
t
4
4
24
14
2
8
1
4
_
3
2
3
14
12
11
3
13
3
12
1
54
7
>DQO
%
5.6
16.7
4.1
12.5
60.0
36.0
.
30.8
30.8
.
50.0
25.9
100.0
33.3
50.0
16.7
_
37.5
40.0
30.0
38.9
48.0
30.6
42.9
48.1
13.0
54.5
25.0
57.4
70.0
Between-
batch SD
0.1110
0.3911
f
P(il
0.688
0.256
0.055
0.0706
0.0452
.
Elil
0.552
0.448
0.0050
0.0110
0.0194
P&
0.125
0.424
0.444
0.007
0.0026
0.0114
P(i)
0.881
0.119
(continued)
167
-------
Table C-1. Continued
Parameter
NA_CL2
FE_CL2
AL_CL2
FE_PYP
Data set
AS
PD
FD
S/H
AS
PD
FD
S/H
AS
PD
FD
S/H
AS
PD
FD
S/H
Delta
0.0032
0.0027
0.0076
0.0318
0.0037
0.0009
0.0025
0.0052
0.0043
0.0073
0.0113
0.0282
0.0317
0.0295
0.0605
0.2970
Window
0.0-0.02
0.02-0.05
0.05-inf
0.0-0.02
0.02-0.05
0.05-inf
0.0-0.02
0.02-0.05
0.05-inf
0.0-0.02
0.02-0.05
0.05-inf
0.0-0.02
0.02-0.05
0.05-inf
0.0-0.02
0.02-0.05
0.05-inf
0.0-0.02
0.02-0.05
0.05-inf
0.0-0.02
0.02-0.05
0.05-inf
0.0-0.05
0.05-inf
0.0-0.05
0.05-inf
0.0-0.05
0.05-inf
0.0-0.05
0.05-inf
0.0-0.2
0.2-0.33
0.33-0.7
0.7-inf
0.0-0.2
0.2-0.33
0.33-0.7
0.7-inf
0.0-0.2
0.2-0.33
0.33-0.7
0.7-inf
0.0-0.2
0.2-0.33
0.33-0.7
0.7-inf
df
23
27
21
3
2
73
29
2
382
202
25
30
9
3
9
42
1
1
531
12
14
35
18
6
66
21
545
57
6
37
7
10
7
6
3
30
21
36
17
149
126
254
80
Mean
0.01
0.03
0.02
0.03
0.08
0.02
0.03
0.15
0.02
0.03
0.11
0.02
0.04
0.06
0.00
0.00
0.03
0.22
0.00
0.03
0.01
0.11
0.01
0.08
0.01
0.08
0.02
0.08
0.04
0.62
0.85
0.11
0.26
0.49
1.33
0.10
0.27
0.52
1.31
0.10
0.27
0.51
1.03
Within-
batch SD
0.0014
0.0065
o!o025
0.0029
0.0038
0.0051
0.0092
0.0316
0.0104
0.0294
0.3646
0.0037
0.0050
0.0042
0.0009
0.0025
0.0021
0.2524
0.0033
0.0864
0.0036
0.0104
0.0042
0.0351
0.0061
0.0574
0.0229
0.0752
0.0063
o!o452
0.0383
0.0104
0.0203
0.0445
0.0343
0.0322
0.0511
0.0425
0.1860
0.0962
0.1878
0.3331
0.7451
Pairs
n
9
8
11'
1
49
21
2
8
3
1
4
23
1
12
12
11
3
55
8
2
2
2
6
3
3
>DQO
%
39.1
29.6
52^4
33.3
67.1
72.4
100.0
26.7
33.3
33.3
44.4
53.5
100.0
85.7
34.3
61.1
50.0
84.6
36.4
SA
33.3
6.7
28.6
8.1
18.8
Betweerv
batch SD
0.0023
0.0055
P(J1
0.627
0.330
0.043
0.0047
0.0050
0.0051
PJU
0.851
0.020
0.0028
0.0278
Edl
0.882
0.098
0.0083
o!o613
0.0964
P(il
0.252
0.209
0.408
0.131
(continued)
168
-------
Table C-1. Continued
Parameter Data set
AL_PYP AS
PD
FD
S/H
FE_AO AS
PD
FD
S/H
AL_AO AS
PD
FD
S/H
Delta
0.0318
0.0514
0.0701
0.2233
0.0607
0.1115
0.0636
0.3413
0.0239
0.0762
0.0523
0.2510
Window
0.0-0.2
0.2-0.33
0.33-0.7
0.7-inf
0.0-0.2
0.2-0.33
0.33-0.7
0.7-inf
0.0-0.2
0.2-0.33
0.33-0.7
0.7-inf
0.0-0.2
0.2-0.33
0.33-0.7
0.7-inf
0.0-0.2
0.2-0.33
0.33-0.6
0.6-inf
0.0-0.2
0.2-0.33
0.33-0.6
0.6-inf
0.0-0.2
0.2-0.33
0.33-0.6
0.6-inf
0.0-0.2
0.2-0.33
0.33-0.6
0.6-inf
0.0-0.2
0.2-0.33
0.33-0.7
0.7-inf
0.0-0.2
0.2-0.33
0.33-0.7
0.7-inf
0.0-0.2
0.2-0.33
0.33-0.7
0.7-inf
0.0-0.2
0.2-0.33
0.33-0.7
0.7-inf
df
6
31
13
10
7
6
3
38
24
29
13
193
100
243
73
6
26
18
9
6
4
7
40
22
21
21
181
115
156
157
6
1
26
17
12
5
6
3
48
21
21
14
187
143
211
68
Mean
0.06
.
0.57
0.79
0.12
0.26
0.47
1.06
0.12
0.27
0.50
1.07
0.13
0.25
0.43
0.88
0.08
o!41
0.97
0.13
0.28
0.46
1.02
0.12
0.26
0.44
1.13
0.15
0.27
0.47
0.86
0.06
0.30
0.44
0.95
0.14
0.25
0.54
1.03
0.13
0.25
0.50
1.00
0.14
0.24
0.47
0.93
Within-
batch SD
0.0063
o!o417
0.0701
0.0171
0.0290
0.0842
0.0713
0.0279
0.0451
0.0875
0.1668
0.0972
0.1352
0.2771
0.5254
0.0657
.
0.0193
0.0970
0.0377
0.0313
0.0501
0.3281
0.0230
0.0446
0.1053
0.0858
0.1301
0.2257
0.3491
0.6845
0.0088
0.0184
0.0208
0.0905
0.0120
0.0268
0.1803
0.0489
0.0153
0.0604
0.0524
0.1451
0.0722
0.1658
0.3497
0.6602
Pairs
n
2
2
1
1
3
4
2
4
3
t
2
2
2
2
6
8
2
3
1
1
6
3
4
>DQO
%
6.5
15.4
14.3
16.7
7.9
17.4
6.7
30.8
50.0
11.1
22.2
28.6
5.0
27.3
40.0
9.1
17.6
16.7
2.1
28.6
14.3
28.6
Between-
batch SD
0.0126
.
0.0590
0.0609
PJjl
0.324
0.172
0.387
0.117
0.0425
t
0.0494
0.2413
pfil
0.306
0.193
0.253
0.248
0.0115
o!o707
0.0936
Eiil
0.323
0.232
0.337
0.108
(continued)
169
-------
Table C-1. Continued
Parameter Data set Delta
FE_CD AS 0.1120
PD 0.1086
FD 0.2724
S/H 1.1583
AL_CD AS 0.0551
PD 0.0179
FD 0.0459
S/H 0.2127
SO4JH2O AS 0.9452
PD 0.9319
FD 1.9537
S/H 5.8528
Window
0.0-0.33
0.33-1.4
1.4-3.0
3.0-inf
0.0-0.33
0.33-1.4
1.4-3.0
3.0-inf
0.0-0.33
0.33-1.4
1.4-3.0
3.0-inf
0.0-0.33
0.33-1.4
1.4-3.0
3.0-inf
0.0-0.2
0.2-0.33
0.33-0.6
0.6-inf
0.0-0.2
0.2-0.33
0.33-0.6
0.6-inf
0.0-0.2
0.2-0.33
0.33-0.6
0.6-inf
0.0-0.2
0.2-0.33
0.33-0.6
0.6-inf
0.0-5.0
5.0-10.0
10.0-15.0
15.0-inf
0.0-5.0
5.0-10.0
10.0-15.0
15.0-inf
0.0-5.0
5.0-10.0
10.0-15.0
15.0-inf
0.0-5.0
5.0-10.0
10.0-15.0
15.0-inf
df
6
12
32
i
12
10
3
4
29
51
20
1
65
454
89
6
27
17
4
12
6
4
15
28
39
22
52
142
311
104
7
10
33
6
10
4
6
20
33
26
25
44
313
175
77
Mean
0.19
1.02
1.87
0.26
1.08
2.11
5.02
0.27
0.89
2.28
5.24
0.21
1.07
1.98
4.24
0.05
o!so
0.76
0.16
0.27
0.46
0.88
0.14
0.27
0.47
0.81
0.16
0.27
0.45
0.76
3.37
8.56
28.24
2.53
7.77
11.75
18.32
2.87
7.61
13.04
19.90
2.83
7.82
12.61
18.15
Within-
batch SD
0.0319
0.2884
0.0838
0^0127
0.0500
0.1059
0.1752
0.0237
0.0465
0.2443
0.6112
0.0283
0.5859
1.0252
2.3534
0.0066
<
0.0649
0.0540
0.0065
0.0114
0.0159
0.0403
0.0124
0.0209
0.0431
0.1113
0.0997
0.1302
0.2099
0.4099
0.9036
0.8830
1.1965
0.7120
0.7470
1.0011
1.5926
0.6361
1.8995
1.3794
4.1298
3.2913
5.5638
6.1462
7.8160
Pairs
n
1
3
.
1
6
2
1
1
4
4
1
3
2
1
2
1
2
3
10
10
5
>DQO
%
16.7
25.0
3.4
11.5
10.5
3.7
3.6
10.3
18.2
14.3
30.0
6.1
16.7
20.0
25.0
33.3
15.0
31.3
35.7
20.8
Between-
batch SD
0.0595
0.0996
0.2683
Ptfl
0.007
0.119
0.728
0.145
0.0137
0.0591
0.0728
Pffl
0.097
0.242
0.498
0.164
0.9405
1.3778
2^2976
Efll
0.081
0.498
0.284
0.137
(continued)
170
-------
Table C-1. Continued
Parameter
S04_PO4
SO4_0
SO4_2
Data set Delta
AS 6.8012
PD 4.2747
FD 9.5746
S/H 55.803
AS 0.1167
PD 0.0504
FD 0.1395
S/H 0.6741
AS 0.1377
PD 0.0685
FD 0.1710
S/H 0.9001
Window
0.0-10.0
10.0-50.0
50.0-100
100-inf
0.0-10.0
10.0-50.0
50.0-100
100-inf
0.0-10.0
10.0-50.0
50.0-100
100-inf
10.0-10.0
10.0-50.0
50.0-100
100-inf
0.0-0.3
0.3-1.0
1.0-2.0
2.0-inf
0.0-0.3
0.3-1.0
1.0-2.0
2.0-inf
0.0-0.3
0.3-1.0
1.0-2.0
2.0-inf
0.0-0.3
0.3-1.0
1.0-2.0
2.0-inf
0.0-1.0
1.0-2.0
2.0-3.0
3.0-inf
0.0-1.0
1.0-2.0
2.0-3.0
3.0-inf
0.0-1.0
1.0-2.0
2.0-3.0
3.0-inf
0.0-1.0
1.0-2.0
2.0-3.0
3.0-inf
df
6
10
27
7
3
12
5
6
8
45
25
26
4
241
179
185
8
10
32
5
15
4
2
17
42
30
15
50
347
172
40
15
35
5
15
4
2
28
24
34
18
201
180
161
67
Mean
5.52
27.08
75.99
109.2
7.16
31.72
74.71
164.8
7.38
31.11
73.59
198.2
7.02
31.84
69.27
144.9
0.62
1.30
3.91
0.13
0.74
1.29
2.61
0.14
0.64
1.33
2.59
0.17
0.67
1.47
2.46
2.65
5.05
0.28
1.46
2.52
4.23
0.46
1.44
2.37
3.94
0.75
1.47
2.55
3.64
Within-
batch SD
2.2402
4.7974
13.0012
3.5376
0.9141
5.8440
2.0469
4.5053
1.2009
6.2803
9.9318
14.2334
3.8622
24.7319
53.6656
104.1
o!o921
0.1383
0.2206
0.1290
0.0391
0.0446
0.0696
0.0397
0.1104
0.1676
0.3689
0.2372
0.6382
0.6733
1.4729
_
o!l052
0.2156
0.0177
0.0748
0.0554
0.2203
0.1282
0.1050
0.2191
0.3361
0.7242
0.9655
0.8431
1.3443
Pairs
n
2
4
3
1
4
4
12
11
2
2
3
10
2
3
3
18
16
10
3
6
3
1
16
9
11
11
>DQO
%
33.3
40.0
11.1
33.3
33.3
50.0
26.7
44.0
7.7
25.0
30.0
31.3
40.0
20.0
17.6
43.9
51.6
66.7
2o'o
17.1
20.0
50.0
57.1
36.0
33.3
61.1
Between-
batch SD
1.8574
4.9756
5.1376
6.4870
Elil
0.014
0.404
0.293
0.289
o!o703
0.0939
0.3829
Elil
0.084
0.555
0.290
0.071
o'l612
0.4348
P(0
0.313
0.292
0.279
0.117
(continued)
171
-------
Table C-1. Continued
Parameter Data set
S04_4 AS
PD
FD
S/H
S04_8 AS
PD
FD
S/H
S04J6 AS
PD
FD
S/H
Delta
0.1648
0.1129
0.2478
1.2066
0.2675
0.1347
0.3179
1.9926
1.1733
0.3402
0.5620
3.2410
Window
0.0-1.0
1.0-3.0
3.0-5.0
5.0-inf
0.0-1.0
1.0-3.0
3.0-5.0
5.0-inf
0.0-1.0
1.0-3.0
3.0-5.0
5.0-inf
0.0-1.0
1.0-3.0
3.0-5.0
5.0-inf
0.0-1.0
1.0-4.0
4.0-7.0
7.0-inf
0.0-1.0
1.0-4.0
4.0-7.0
7.0-inf
0.0-1.0
1.0-4.0
4.0-7.0
7.0-inf
0.0-1.0
1.0-4.0
4.0-7.0
7.0-inf
0.0-1.0
1.0-8.0
8.0-14.0
14.0-inf
0.0-1.0
1.0-8.0
8.0-14.0
14.0-inf
0.0-1.0
1.0-8.0
8.0-14.0
14.0-inf
0.0-1.0
1.0-8.0
8.0-14.0
14.0-inf
df
m
24
26
4
15
5
2
21
35
34
14
45
335
194
35
.
8
42
2
10
9
5
11
30
35
28
1
282
198
128
.
11
39
1
10
9
6
4
33
43
24
1
260
230
118
Mean
,
4.37
7.06
0.32
2.07
3.98
5.64
0.50
2.10
3.75
5.84
0.81
1.80
4.03
5.33
&56
9.36
0.23
2.57
5.10
8.20
0.54
2.45
5.50
8.44
0.25
2.59
5.54
8.04
1fc41
16.25
0.28
5.32
10.51
15.10
0.72
4.61
11.04
15.91
0.75
5.75
10.88
14.98
Within-
batch SD
o!l592
0.1940
0.0773
0.1232
0.0921
0.1764
0.1040
0.2665
0.2196
0.4053
0.9911
1.1923
1.1210
2.0197
.
0.2892
0.2356
0.0015
0.0678
0.1723
0.2127
0.1071
0.3169
0.2439
0.4314
0.0997
1.9205
1.9462
2.2265
.
1.5700
0.4291
0.0028
0.2701
0.4836
0.2133
0.3395
0.6030
0.4706
0.6561
0.3769
3.3797
3.4585
2.6010
Pairs
n
(
6
3
1
3
11
12
12
7
1
1
1
2
6
18
7
6
1
3
3
1
3
17
4
7
>DQO
%
25.0
11.5
25.0
20.0
52.4
34.3
35.3
50.0
izs
2.4
10.0
22.2
54.5
60.0
20.0
21.4
9^1
7.7
30.0
11.1
75.0
51.5
9.3
29.2
Between-
batch SD
t
0.2572
0.3647
P(i)
0.074
0.529
0.333
0.064
0^2491
0.5789
P(i)
0.003
0.442
0.330
0.225
.
0.6650
0.9774
Plil
0.003
0.404
0.387
0.206
(continued)
172
-------
Table C-1. Continued
Parameter Data set
SO4_32 AS
PD
FD
S/H
C_TOT AS
PD
FD
S/H
N_TOT AS
PD
FD
S/H
Delta
0.4729
0.6459
0.9111
5.2605
0.1132
0.3168
0.4458
1.1403
0.0057
0.0265
0.0264
0.0702
Window
0.0-1.0
1.0-16.0
16.0-25.0
25.0-inf
0.0-1.0
1.0-16.0
16.0-25.0
25.0-inf
0.0-1.0
1.0-16.0
16.0-25.0
25.0-inf
0.0-1.0
1.0-16.0
16.0-25.0
25.0-inf
0.0-0.3
0.3-1.0
1.0-3.0
3.0-5.0
5.0-inf
0.0-0.3
0.3-1.0
1.0-3.0
3.0-5.0
5.0-inf
0.0-0.3
0.3-1.0
1.0-3.0
3.0-5.0
5.0-inf
0.0-0.3
0.3-1.0
1.0-3.0
3.0-5.0
5.0-inf
0.0-0.1
0.1-0.2
0.2-inf
0.0-0.1
0.1-0.2
0.2-inf
0.0-0.1
0.1-0.2
0.2-inf
0.0-0.1
0.1-0.2
0.2-inf
df
6
44
.
7
12
7
28
38
38
135
274
200
6
t
8
34
2
7
12
5
2
22
32
32
11
7
106
247
141
50
65
8
42
20
3
2
72
22
10
434
105
65
Mean
23'35
29.80
.
11.41
21.44
28.67
10^45
21.25
29.06
13^45
19.81
27.77
0.14
169
4.40
5.19
0.22
0.67
1.77
1178
0.20
0.60
1.71
3.96
8.02
0.19
0.63
1.68
4.13
6.56
0.03
0.14
0'04
0.14
0.65
0.04
0.15
0.39
0.04
0.18
0.36
Within-
batch SD
o!4551
0.4953
0^3923
0.5392
0.9347
10103
0.9039
0.8597
_
e!s2i2
5.4906
4.2035
0.0194
o!()469
0.3757
0.2221
0.0552
0.1090
0.8215
_
o!3641
0.0335
0.0809
0.3602
0.4092
2.8314
0.1191
0.4774
1.5394
2.3361
3.5708
0.0023
0.0188
0'0200
0.0492
0.0310
0.0172
0.0302
0.0840
0.0373
0.1068
0.2333
Pairs
n
.
3
1
12
5
4
2
2
5
1
4
10
9
3
5
11
6
3
17
9
4
>DQO
%
t
_
42.9
14.3
42£
13.2
10.5
5.9
28.6
41.7
20.0
18.2
32.3
27.3
27.3
71.4
26.2
soio
100.0
23.3
42.9
40.0
Between-
batch SD
.
1.0632
1.7337
P(il
0^212
0.440
0.349
0.0263
0.0953
0.2507
pm
0.175
0.397
0.245
0.083
0.101
0.0056
0.0165
P(i)
0.706
0.183
0.101
(continued)
173
-------
Table C-1. Continued
Parameter Data set Delta Window
S TOT AS 0.0042 0.0-0.01
0.01-0.04
0.04-0.1
0.1-inf
PD 0.0065 0.0-0.01
0.01-0.04
0.04-0.1
0.1-inf
FD 0.0070 0.0-0.01
0.01-0.04
0.04-0.1
0.1-inf
S/H 0.0203 0.0-0.01
0.01-0.04
0.04-0.1
0.1-inf
df
6
42
8
13
1
41
50
8
1
143
427
39
Mean
0.01
0.02
_
<
0.01
0.02
0.10
.
0.01
0.02
0.06
0.25
0.01
0.02
0.06
Within-
batch SD
0.0029
0.0047
.
f
0.0026
0.0085
0.0007
o!oosi
0.0056
0.0376
0.1980
0.0057
0.0150
0.1397
Pairs
n
2
.
f
2
2
2
1
>DQO
%
4.8
.
15.4
4.0
25.0
100.0
Between-
batch SD
0.0029
0.0033
_
.
Fill
0.246
0.686
0.061
A dot indicates a lack of data within the range of this window.
P(i) Proportion of the routine samples within the ith window.
174
-------
Appendix D
Inordinate Data Points Influencing the Precision Estimates
The Appendix D table provides information on specific data points that have an inordinate
effect on the precision estimates presented in the results and discussion of Section 3. Included
is information for each datum on the sampling class/horizon group, the batch/sample number, and
the reason for its effect on the estimates. Data users interested in the quality of data in specific
batches will find this table particularly helpful. The table is sorted by parameter and subsorted by
data set.
Table D-1. Inordinate Data Points Having a High Degree of Influence on the Precision Estimates for the Data Sets
Parameter
MOIST
SP_SUR
SAND
VCOS
Data set*
S/H
FD
AS
S/H
FD
PD
AS
S/H
FD
PD
AS
FD
AS
Sampling
class/horizon
MSL/
FR /
FR /
OTC/
ACH/
ACL /
MSL/
MSL/
OTC/
ACL /
MSH/
ACH/
MSL /
ACH/
SKV /
Oe
A
Oe
Bs
Bs
A
Bw
A
Bt
Bw
Bs
A
A
Bw
A
Bw
A
Bs
Bw
C
Bw
Bs
Bs
C
C
Batch/sample
20612-06,20
20614-04,12
29601-35
29606-14
29601-40,04
20610-18,34
20609-33,19
20614-23,02
20712-28
29607-09,26
20711-16,29
20704-10,14
20614-23,02
20703-03
20705-30
20704-20
29607-05,23
20608-02,23
20705-21,30
20709-24,37
20608-27
20614-18,31
Reason6
large variability
high value
high value
high value
high value
large variability
high value
high value
large variability
low value
low value
large variability
low value
high value
large variability
large variability
low value
low value
high value
high value
high value
large variability
large variability
low value
high value
(continued)
175
-------
Table D-1. Continued
Parameter
COS
MS
FS
VFS
SILT
COSI
FSI
CLAY
PH_H2O
Data set3
FD
PD
AS
FD
AS
S/H
PD
AS
S/H
FD
AS
FD
PD
AS
FD
PD
AS
S/H
FD
PD
AS
S/H
AS
S/H
FD
AS
Sampling
class/horizon
ACL /
MSH/
ACH/
ACL /
MSL /
OTC/
MSL/
OTC/
OTC/
SKV /
ACL /
FL /
MSH/
ACH/
FL /
MSH/
MSL/
OTC/
ACH/
ACH/
ACL /
ACH/
OTC/
SHL /
OTC/
ACL /
MSL/
A
Bw
BA
Bs
Bs
C
A
A
Bs
C
BA
A
Bt
Bw
A
Bs
Bs
A
Ap
A
A
Bs
Bw
C
A
A
Bw
A
A
Bw
Bt
A
Bw
A
A
Bw
BA
Ap
Bt
Bw
A
A
A
A
A
Bt
A
A
Bs
A
Oe
A
Bs
Batch/sample
29607-09,26
20711-16,29
20710-16,07
20705-21,30
20709-24,37
20614-31
29607-09,26
20703-03
20709-37
29605-36
20614-23,02
20703-03
20705-21,30
20709-24,37
29606-18,26
20703-03
20705-21
20704-01,20
20614-31
29607-09,26
20711-25,09
20711-16,29
20703-03
20709-07
20704-20
20705-06,01
20711-25,09
20711-16,29
20703-03
20709-07
20704-20
20705-06,01
29601-40,04
29607-09,26
20704-10,14
20703-03
20709-07,16
20703-03
20709-07
29601-35
20609-18,24
29601-22,35
Reason*
large variability
large variability
high value
large variability
large variability
high value
low value
low value
low value
low value
large variability
large variability
low value routine
large variability
low value
large variability
large variability
large variability
large variability
high value
low value
low value
high value
low value
high value
low value
low value
high value
high value
low value
high value
low value
negative value
high value
high value
low value
large variability
large variability
high value
high value
large variability
high value
low value
low value
large variability
large variability
low value
low value
negative value
large variability
large variability
low value
high value
(continued)
176
-------
Table D-1. Continued
Parameter
PH_002M
PH_01M
CA_CL
MG_CL
K_CL
NA_CL
CA_OAC
MG_OAC
Data set'
S/H
FD
AS
S/H
FD
AS
S/H
FD
AS
S/H
FD
PD
S/H
FD
AS
S/H
FD
PD
AS
S/H
FD
PD
S/H
FD
PD
Sampling
class/horizon
OTC/
ACL /
MSL /
OTC/
ACL /
MSL/
OTC/
OTC/
OTC/
MSL/
SKV /
FR /
ACC/
SHL /
SKV /
FR /
ACC/
MSH/
SKV /
FR /
MSH/
MSH/
FR /
SKX /
OTC/
OTC/
OTC/
OTC/
MSL/
OTC/
SKV /
FR /
ACH/
MSH/
ACC/
SHL /
OTC/
SKV /
FR /
ACC/
ACH/
A
Oe
A
A
A
Oe
A
A
A
Bt
C
A
A
Oe
A
A
Ap
Oe
A
Oe
Ap
Oe
A
Oe
A
Bs
Oe
Bw
Oe
Bw
A
A
Bs
Bs
A
Bt
C
Bw
A
Ap
A
Oe
A
Bw
Ap
Oe
Ap
A
Oe
Ap
A
Batch/sample
20609-18,24
20612-07
20609-18,24
20701-19
20609-18,24
20709-22,06
20614-04,12
20706-31
20703-03,19
20709-22,06
20614-04,12
20701-28,39
20709-22,06
20614-04,12
20703-19
20703-35
29603-03,40
20614-04,12
20608-12,05
29605-30
20707-33
20703-18
29603-04
20609-18,24
20612-24,10
20709-22.06
20614-04,12
20704-10,14
29605-12.23
20612-24,10
20709-22,06
20614-04,12
20701-28,39
20704-10,14
Reason"
large variability
large variability
low value
high value
large variability
large variability
low value
low value
large variability
large variability
large variability
high value
high value
high value
high value
large variability
large variability
large variability
high value
high value
large variability
large variability
high value
high value
high value
low value
large variability
low value
high value
high value
high value
high value
high value
low value
large variability
large variability
large variability
large variability
high value
large variability
large variability
high value
large variability
high value
high value
large variability
large variability
large variability
high value
large variability
large variability
(continued)
177
-------
Table D-1. Continued
Parameter
K_OAC
NA_OAC
CEC_CL
CEC_OAC
AC_KCL
AC_BACL
Data set8
S/H
FD
PD
AS
S/H
FD
PD
AS
S/H
FD
PD
AS
S/H
FD
PD
AS
S/H
FD
PD
AS
S/H
FD
AS
Sampling
class/horizon
OTC/
OTC/
SKV /
FR /
ACH/
FR /
FR /
FR /
ACL /
FR /
ACH/
OTC/
SKV /
FR /
ACH/
ACC/
FL /
FR /
SKV /
FR /
OTC/
ACH/
FL /
SKX /
ACL /
MSL/
FR /
FR /
ACH/
ACL /
FR /
SKX /
SKV /
FL /
OTL /
FR /
Ap
Ap
A
Oe
A
A
Bs
Bw
A
Bw
Oe
Bw
Oe
A
A
Bs
A
A
A
A
Bt
A
A
A
C
Oe
A
A
C
A
A
A
Bw
A
Oe
A
C
A
BC
A
Bs
Oa
Oe
Oe
Oe
C
Bg
Bt
A
A
Oa
Batch/sample
20612-24,10
20709-22,06
20614-04,12
20704-10,14
20702-01
29603-04
20702-12
20701-17,10
20614-04,12
20704-10.14
20707-11
20701-16,19
20709-22,06
20612-28,08
20704-10,14
20706-40,12
20613-18,08
20613-31
20610-05,14
20614-18,31
20709-22,06
20612-28,08
20708-05,09
20704-10,14
20613-18,08
20614-06
20710-05
20609-18,24
29604-26,14
20612-28,08
20611-37,40
20602-14
29604-15
20613-13
20614-01.29
29604-26,16
20612-28,08
20612-02
20612-12
Reason*
large variability
large variability
high value
large variability
high value
high value
high value
high value
large variability
large variability
large variability
low value
high value
large variability
high value
low value
large variability
large variability
high value *
large variability
large variability
high value
high value
large variability
large variability
large variability
large variability
high value *
high value
high value
high value
low value
high value
large variability
large variability
low value
low value
high value *
high value
high value
low value
high value
large variability
large variability
large variability
large variability
large variability
high value
large variability*
low value
low value
(continued)
178
-------
Table D-1. Continued
Parameter
AL_KCL
CA_CL2
MG_CL2
K_CL2
NA_CL2
FE_CL2
AL_CL2
Data set"
S/H
FD
PD
S/H
FD
PD
AS
S/H
FD
AS
S/H
FD
AS
S/H
FD
AS
S/H
FD
PD
AS
S/H
FD
PD
AS
Sampling
class/horizon
MSL/
SKX /
MSL/
MSH/
SKX /
FR /
ACH/
ACH/
OTC/
ACL /
SHL /
FR /
SKV /
ACC/
ACL /
ACC/
SHL /
SKV /
FR /
ACL /
FR /
OTC/
SKV /
FR /
FR /
ACL /
FR /
FR /
FR /
FR /
FR /
FR /
FR /
SKV /
FL /
FR /
ACH/
FR /
SKV /
ACH/
ACC/
C
A
A
BC
Bw
A
BC
Bw
Ap
Oe
Oe
A
A
Ap
C
E
Ap
Oe
A
A
C
Oe
A
Ap
A
A
Oe
C
Bs
A
Bt
A
Oe
A
Oe
C
Bs
A
A
A
C
A
A
A
Bs
A
Oe
A
A
A
Bt
Bw
Bs
Batch/sample
20609-18,24
20711-02,22
20608-18,36
20612-28,08
20611-27,40
20612-06,20
20709-22,06
20701-28,39
20711-31
20709-22,06
20612-06.20
20711-31
20709-22,06
20612-06,20
20614-04.12
20711-31
29606-14
20706-07
20612-06,20
20614-04,12
20711-31
29606-14
20701-33
20707-11,33
20612-06.20
20709-22.06
20613-18.08
29601-22,35
20612-06,20
20709-22.06
20704-10,14
20706-40,12
20610-15
29601-22,35
Reason6
large variability
large variability
large variability
high value
high value
high value *
high value
large variability
large variability
large variability
large variability
high value
high value
large variability
low value
large variability
large variability
large variability
high value
high value
low value
large variability
large variability
large variability
high value
high value
high value
low value
high value
low value
large variability
large variability
large variability
high value
high value
low value
high value
high value
large variability
large variability
large variability
high value
high value
high value
low value
large variability
large variability
high value
high value
high value
large variability
high value
low value
(continued)
179
-------
TabU D-1. Continued
Parameter
FE_PYP
AL_PYP
FE_AO
AL_AO
FE_CD
AL_CD
S04_H20
SO4_PO4
Data set"
S/H
FD
PD
AS
FD
PD
AS
S/H
FD
PD
S/H
FD
PD
AS
S/H
FD
PD
AS
S/H
FD
PD
AS
S/H
FD
PD
AS
FD
PD
AS
Sampling
class/horizon
FR /
FR /
FR /
ACL /
FR /
MSH/
SHL /
ACH/
ACH/
ACH/
FR /
ACH/
FR /
FR /
ACL /
ACH/
FR /
ACL /
ACH/
ACC/
ACC/
FR /
SHL /
ACL /
SKX /
SKX /
FR /
ACH/
ACL /
ACL /
ACC/
FR /
SKV /
FR /
ACH/
ACH/
ACL /
ACH/
OTC/
ACH/
ACC/
A
C
Bw
A
A
Bw
Bt
A
Bw
BA
Bs
Bs
C
Bw
Bs
A
C
Bw
C
BA
Bw
A
A
Bs
Bw
Bt
Cr
C
Bt
C
Bw
Bw
Bw
Bw
Bw
A
C
Bt
Bw
Oe
A
Oe
Bw
A
Bw
C
Oa
Bw
C
Bw
C
BA
Bt
Bs
A
Batch/sample
29607-09,26
20612-06,20
20602-28,30
20611-24,35
20704-10,14
29607-14,25
20710-16,07
20705-21
20712-28
29604-26,16
29607-14,25
20712-28,31
29601-33,34
20706-20,19
20710-16,07
29607-09,26
20710-16,07
20705-21,30
20611-15.25
20706-20,19
20608-12,05
20612-14,26
20710-05
20704-07,03
29607-09,26
20706-20,19
20706-40,12
20710-05
20709-22,06
20614-04,12
29607-14,25
20610-05,14
20610-15
20711-14
20612-12
20703-28,40
20706-20,19
20704-07,03
20705-14,37
20710-61,07
20706-40,12
20709-24
20706-31
Reason*
large variability
large variability
large variability
high value
large variability
high value
high value
large variability
large variability
low value
low value
low value
high value
large variability
large variability
large variability
large variability
large variability
high value
large variability
large variability
high value
high value
large variability
low value
large variability
large variability
large variability
large variability
high value
high value
large variability
low value
large variability
high value
high value
high value
high value
low value
large variability
high value
high value
large variability
large variability
low value
high value
high value
large variability
high value
high value
high value
low value
high value
high value
high value
(continued)
180
-------
Table D-1. Continued
Parameter
SO4_0
SO4_2
S04_4
S04_8
S04J6
S04_32
C_TOT
Data set*
S/H
FD
PD
AS
S/H
FD
PD
AS
S/H
FD
PD
AS
S/H
AS
S/H
PD
AS
S/H
PD
AS
S/H
FD
PD
AS
Sampling
class/horizon
FR /
ACH/
FR /
MSL/
ACL /
FR /
FR /
MSL/
ACH/
FR /
FR /
FR /
ACH/
ACH/
SKX /
FR /
FR /
ACH/
FR /
OTL /
FR /
FR /
FR /
MSL/
FR /
OTC/
FR /
FR /
MSL /
SKV /
ACH/
MSL /
Oe
Oe
A
BA
A
A
A
Bt
Bs
A
Oe
Oe
A
A
A
Bw
Bw
A
Oe
A
Bw
A
Bw
Bs
C
Oe
A
Bs
Bw
Oe
Bw
A
C
Oe
C
A
C
A
A
A
A
A
Bw
A
Bs
Oa
Batch/sample
20610-35,29
20612-06.20
20612-28,08
20609-33,19
29601-35
20614-31
20612-28,08
20704-10,14
20709-25,15
20612-14,26
20610-14
20613-28,39
20612-28,08
29601-41,37
20701-19
29605-03
29607-08,19
20704-01
20614-23,02
20610-14
20614-18,31
20705-14,37
29605-30,34
20614-18,31
20612-06,20
20609-18,24
20709-22,06
20704-10,14
20614-23,02
20611-16
20707-13
20612-12,25
Reason*
large variability
large variability
large variability
large variability
high value
high value
large variability*
low value
high value
high value
large variability
large variability
large variability
large variability*
large variability
large variability
large variability
low value
large variability
large variability
large variability
large variability*
large variability
high value
low value
large variability
large variability
large variability
high value
large variability
large variability
low value
large variability
large variability
low value
large variability
high value
large variability
large variability
high value
high value
high value
high value
high value
high value
large variability
(continued)
181
-------
Table D-1. Continued
Sampling
Parameter Data set* class/horizon
N TOT S/H FR /
ACH/
ACL /
FR /
FD MSL /
SKV /
FR /
PD MSH /
MSH/
MSL/
ACH/
AS
S TOT S/H ACL /
FR /
SKX /
FD ACL /
FR /
SHL /
SKV/
FR /
PD ACC /
MSL/
ACH/
AS
A
Cr
Cr
Oe
A
A
A
Bw
BC
Bw
A
Bs
A
A
A
Bw
Bt
A
A
A
Oe
Bt
Bw
A
Bs
Bs
Bw
C
Batch/sample
20609-18,24
20709-22,06
20612-06,20
29605-12,23
29606-39,16
20614-23,02
20704-10,14
20707-13
20611-16
20706-04,03
20612-06,20
29606-15,37
20709-22,06
20614-04,12
20706-40,12
20614-23,02
20704-10,14
20701-16
20707-13
20611-15,25
20608-27,38
Reason6
large variability
negative value
negative value
large variability
high value
large variability
high value
high value
negative value
high value
high value
high value
high value
large variability
large variability
large variability
high value
high value
high value
high value
high value
large variability
large variability
high value
high value
high value
large variability
large variability
* AS = audit samples; PD
routine samples.
preparation duplicates; FD •
routine samples.
b An asterisk in this column denotes an organic soil type.
field duplicates; S/H = sampling class/horizon groups of
NOTE: Sampling class codes are as follows: FR = frigid, OTC = calcareous, SKV - skeletal concave, SKX - skeletal
convex, FL = flooded, SHL = low organic shallow, ACH = high organic acid crystalline, MSH » high organic
metasedimentary, ACC = low organic clayey acid crystalline, ACL - other low organic acid crystalline, MSL -
low organic metasedimentary, OIL = other low organic soils.
182
-------
Appendix E
Additional Precision Plots for Moisture, Specific Surface, and
Particle Size Fractions
Following are precision plots for the routine and QA data sets from the MOIST, SP_SUR,
VCOS, COS, MS, FS, VFS, COSI, and FSI parameters. Since these parameters did not"have
specifically established DQOs, it was decided to place the routine data plots in a place separate
from the audit sample / routine sample paired plots found in the results and discussion of the
report. Supplemental information relating to these plots can be found in Section 3 under the
parameter group heading and in Appendices C and D.
183
-------
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Appendix F
Table of General Statistics for the Analytical Parameters
Appendix F consists of a general summary table for data users. Included are data sorted
by laboratory and by audit sample type for mean concentration, standard deviation, and laboratory
difference from the interlaboratory mean. Supplemental information relating to this table is
contained in the discussion of "interlaboratory differences" in the main body of the report.
Table F-1. Table of General Statistics for the Analytical Parameters
Lab
Mean
A
SD
d
Mean
Audit Sample3 -
Bs
SD
d
Mean
Bw
SD
d
Mean
C
SD
d
1
2
3
ALL
1
2
3
ALL
1
2
3
ALL
1
2
3
ALL
1.95
1.87
1.82
1.89
40.3
28.2
44.8
36.9
54.1
59.2
55.6
56.5
0.79
1.00
1.15
0.97
0.13
0.28
0.20
0.22
4.56
9.31
4.98
9.81
4.00
1.18
0.61
3.33
0.22
0.22
0.45
0.33
0.06
-0.01
-0.06
3.40
-8.64
7.90
-2.40
2.70
-0.82
-0.18
0.03
0.18
2.79
2.81
0.31
0.72
Moisture %
-0.01
0.01
2.80 0.56
2.36
2.34
2.28
2.33
Specific Surface m2/g
26.0
27.1
26.6
85.6
85.3
6.59
8.23
7.38
-0.63
0.52
40.1
25.3
41.4
36.3
0.12
0.04
0.15
0.11
1.98
7.28
1.47
8.11
Total Sand (2.0-0.05mm) % dry wt
85.5
1.14
1.36
1.25
0.18
-0.15
24.3 0.44
32.5 3.65
25.8 1.47
27.1 4.10
0.03
0.01
-0.05
3.9
-10.9
5.1
-2.81
5.40
-1.21
Very Coarse Sand (2.0-1.0 mm) % dry wt
10.6
9.15
1.03
2.17
0.77
-0.65
2.77 0.36
2.35 0.47
2.67 0.64
2.62 0.48
0.15
-0.27
0.05
9.80 1.86
Coarse Sand (1.0-0.5mm) % dry wt
0.14
0.15
0.15
0.15
2.21
1.58
3.11
2.45
93.6
97.3
94.7
95.4
3.45
4.47
2.75
3.44
0.01 -0.01
0.00 0.00
0.01 0.00
0.01
0.15
0.40
1.19
1.10
-0.24
-0.87
0.66
1.34 -1.74
2.00 1.90
1.33 -0.67
2.04
0.78 0.01
2.58 1.03
0.88 -0.69
1.69
1
2
3
ALL
3.13
3.14
3.00
3.10
0.34
0.21
0.44
0.33
0.03
0.04
-0.10
20.8
19.0
—
19.8
1.44
2.48
-
2.24
1.00
-0.85
—
4.18
3.42
4.42
4.04
0.28
0.43
0.30
0.52
0.15
-0.61
0.39
13.2
13.3
11.0
12.1
3.04
4.44
1.56
2.95
1.00
1.20
-1.13
(continued)
193
-------
Table F-1. Continued
Lab
1
2
3
ALL
1
2
3
ALL
1
2
3
ALL
1
2
3
ALL
1
2
3
ALL
1
2
3
ALL
1
2
3
ALL
1
2
3
ALL
Mean
8.09
8.34
8.38
8.27
21.6
24.7
24.3
23.5
20.5
22.0
18.8
20.6
28.0
22.7
26.5
25.6
12.9
10.4
11.8
11.6
15.1
12.3
14.7
13.9
17.9
18.1
17.8
17.9
4.50
4.51
4.56
4.52
A
SD
0.62
0.29
0.54
0.50
2.09
0.51
0.67
1.92
1.78
1.05
0.74
1.80
5.26
2.43
0.67
4.15
6.40
4.12
0.49
4.61
1.17
1.82
0.36
1.84
1.42
2.21
0.54
1.61
0.04
0.03
0.04
0.04
d
-0.18
0.08
0.11
-1.92
1.20
0.75
-0.17
1.40
-1.77
2.40
-2.89
0.95
1.30
-1.28
0.17
1.20
-1.61
0.77
-0.07
0.15
-0.12
-0.02
-0.01
0.04
«--»«•» *..»_ .... - Audit Ssmols •*• «••"•
Bs Bw
Mean SD d Mean SD
Medium Sand (0.5-0. 25mm) % dry wt
26.3 0.71 0.10 3.50 0.09
26.1 1.22 -0.08 3.35 0.13
3.70 0.12
26.2 1.00 . 3.51 0.17
Fine Sand (0.25-0. 1mm) % dry wt
20.8 1.51 -1.48 3.57 0.12
23.5 2.05 1.20 4.15 0.13
4.17 0.15
22.3 2.25 . 3.91 0.33
Very Fine Sand (0.1-0.05mm) % dry wt
7.15 0.91 -0.24 10.3 0.12
7.58 1.05 0.20 19.3 2.88
10.9 0.43
7.39 0.99 . 13.0 4.33
Total Silt (0.05-0.002mm) % dry wt
12.9 1.05 0.04 68.5 0.96
12.8 1.45 -0.03 60.0 3.78
66.3 1.25
12.8 1.25 . 65.4 4.22
Coarse Silt (0.05-0. 02mm) % dry wt
8.69 0.66 -0.09 34.6 0.81
8.85 1.24 0.07 31.1 2.95
35.1 0.78
8.78 1.00 . 33.7 2.31
Fine Silt (0.02-0.005mm) % dry wt
4.21 0.88 0.13 33.9 0.34
3.97 0.71 -0.11 28.8 1.46
31.3 0.83
4.08 0.78 . 31.7 2.36
Total Clay (< 0.002mm) % dry wt
1.46 0.76 -0.23 7.25 0.87
1.88 0.88 0.19 7.52 0.29
7.77 0.39
1.69 0.84 . 7.48 0.63
pH in H2O
4.53 0.05 -0.02 5.10 0.04
4.56 0.19 0.01 5.12 0.03
5.18 0.02
4.55 0.14 . 5.13 0.05
d
-0.01
-0.16
0.19
-0.34
0.24
0.27
-2.76
6.30
-2.09
3.10
-5.47
0.90
0.84
-2.59
1.30
2.20
-2.88
-0.43
-0.23
0.05
0.30
-0.03
-0.01
0.05
Mean
29.6
31.0
32.5
31.5
34.3
36.6
37.7
36.7
13.3
11.9
10.8
11.6
6.00
2.30
5.27
4.40
5.40
1.97
5.02
4.07
0.60
0.35
0.27
0.35
0.35
0.42
0.00
0.20
5.21
5.49
5.57
5.48
C
SD
2.90
2.31
1.94
2.29
3.89
4.33
1.94
3.13
1.48
3.06
1.20
2.06
1.27
1.97
1.29
2.11
1.13
2.17
1.21
2.11
0.14
0.26
0.14
0.21
0.07
0.05
0.00
0.21
0.08
0.03
0.07
0.14
d
-1.94
-0.52
0.99
-2.50
-0.12
0.92
1.70
0.32
-0.76
1.60
-2.10
0.87
1.37
-2.09
0.95
0.25
0.00
-0.08
0.15
0.23
-0.20
-0.27
0.01
0.08
(continued)
194
-------
Table F-1. Continued
Lab
1
2
3
ALL
1
2
3
ALL
1
2
3
ALL
1
2
3
All
1
2
3
ALL
1
2
3
ALL
1
2
3
ALL
1
2
3
ALL
Mean
4.23
4.21
4.34
4.25
4.01
3.98
4.09
4.02
0.30
0.25
0.33
0.29
0.23
0.22
0.20
0.21
0.29
0.25
0.24
0.26
0.05
0.04
0.03
0.04
0.27
0.23
0.33
0.27
0.25
0.22
0.23
0.23
A
SD
0.03
0.02
0.09
0.07
0.04
0.04
0.02
0.06
0.04
0.06
0.06
0.06
0.02
0.01
0.01
0.02
0.15
0.02
0.02
0.09
0.03
0.01
0.02
0.02
0.03
0.05
0.05
0.06
0.02
0.02
0.02
0.02
d
-0.02
-0.04
0.09
-0.01
-0.04
0.07
0.01
-0.04
0.04
0.01
0.00
-0.02
0.02
-0.01
-0.02
0.01
0.00
-0.01
0.00
-0.04
0.06
0.01
-0.01
0.00
Mean
4.19
4.08
..
4.13
4.00
3.92
-,
3.96
0.30
0.22
—
0.25
0.06
0.05
—
0.05
0.02
0.02
—
0.02
0.03
0.02
—
0.02
0.19
0.17
-
0.18
0.05
0.05
—
0.05
_„„„___ „ AiuHtt ^samnlfi^ -.
Bs
SD d Mean
pH in 0.002M CaCI2
0.03 0.06 4.72
0.03 -0.05 4.67
4.78
0.06 . 4.72
pH in 0.01M CaCI2
0.07 0.04 4.61
0.03 -0.03 4.57
4.67
0.07 . 4.62
Ca in NH4CI meq/100g
0.04 0.05 0.30
0.03 -0.04 0.23
0.31
0.05 . 0.28
Mg in NH4CI meq/100g
0.01 0.01 0.05
0.01 -0.01 0.06
0.05
0.01 . 0.05
K in NH4CI meq/100g
0.03 -0.00 0.06
0.00 0.00 0.06
0.05
0.02 . 0.06
Na in NH4CI meq/100g
0.03 0.01 0.02
0.01 -0.01 0.02
0.02
0.02 . 0.02
Ca in NH4OAC meq/100g
0.03 0.01 0.24
0.04 -0.01 0.18
0.29
0.04 . 0.24
Mg in NH4OAC meq/100g
0.02 0.00 0.07
0.01 0.00 0.04
0.06
0.01 . 0.06
Bw
SD
0.03
0.03
0.03
0.05
0.01
0.02
0.03
0.04
0.05
0.02
0.05
0.05
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.03
0.02
0.01
0.02
0.04
0.01
0.05
0.06
0.01
0.00
0.01
0.01
d
-0.00
-0.05
0.06
-0.01
-0.04
0.06
0.02
-0.05
0.03
-0.00
0.00
-0.00
0.00
0.00
-0.01
0.00
0.00
0.00
0.00
-0.06
0.05
0.01
-0.01
0.00
Mean
4.75
4.99
5.17
5.04
4.71
4.79
4.92
4.84
0.11
0.09
0.13
0.11
0.03
0.03
0.04
0.03
0.02
0.02
0.03
0.03
0.01
0.00
0.02
0.01
0.08
0.05
0.13
0.09
0.04
0.03
0.04
0.04
C
SD
0.02
0.06
0.08
0.17
0.02
0.02
0.07
0.10
0.05
0.02
0.04
0.04
0.02
0.01
0.01
0.01
0.00
0.01
0.01
0.01
0.01
0.00
0.02
0.02
0.03
0.03
0.05
0.06
0.00
0.01
0.01
0.01
d
-0.30
-0.05
0.13
-0.14
-0.05
0.08
-0.00
-0.02
0.02
-0.00
-0.01
0.01
-0.00
-0.00
0.00
-0.00
-0.01
0.01
-0.01
-0.05
0.04
0.01
-0.01
0.00
(continued)
195
-------
Table F-1. Continued
Lab
1
2
3
ALL
1
2
3
All
1
2
3
ALL
Mean
0.27
0.26
0.25
0.26
0.05
0.04
0.03
0.04
8.05
7.11
10.9
8.47
A
SD
0.06
0.01
0.01
0.04
0.03
0.01
0.01
0.02
1.08
0.51
1.59
1.90
d
0.01
0.00
-0.01
0.01
0.00
-0.01
-0.42
-1.36
2.43
Mean
0.03
0.03
-
0.03
0.02
0.02
-
0.02
8.24
7.20
—
7.67
Bs
SD
Kin
0.02
0.00
—
0.01
Na in
0.03
0.01
..
0.02
CEC
0.56
0.92
—
0.93
AnHit ^amnlflfl —
d Mean
NH4OAC meq/100g
0.00 0.06
0.00 0.06
0.06
0.06
NH4OAC meq/100g
0.00 0.03
0.00 0.03
0.02
0.02
- NH4CI meq/100g
0.57 4.82
-0.48 4.01
9.01
5.79
Bw
SD
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.82
0.77
0.64
2.26
d
0.00
0.00
0.00
0.00
0.00
-0.01
-0.97
-1.77
3.22
Mean
0.05
0.02
0.03
0.03
0.01
0.00
0.01
0.01
0.77
0.84
1.47
1.14
C
SD
0.00
0.00
0.01
0.01
0.00
0.01
0.01
0.01
0.03
0.58
0.38
0.52
d
0.02
0.01
0.00
0.00
-0.01
0.00
-0.37
-0.31
0.33
CEC - NH4OAC meq/100g
1
2
3
ALL
1
2
3
ALL
18.8
15.5
20.9
18.1
3.19
3.50
3.80
3.47
0.76
1.19
1.61
2.53
0.26
0.33
0.76
0.52
0.73
-2.62
2.80
-0.28
0.02
0.33
23.2
21.3
—
22.2
4.10
3.94
—
4.01
1.21
1.27
—
1.55
KCI
0.40
1.19
_
0.90
1.00 13.8
0.86 9.64
15.6
13.1
Acidity meq/100g
0.09 1.42
-0.07 1.59
2.38
1.74
2.51
0.64
1.45
2.97
0.07
0.08
0.59
0.51
0.66
3.48
-2.50
-0.32
-0.15
0.63
1.34
0.94
1.79
1.43
0.02
0.17
0.52
0.32
0.18
0.12
0.33
0.46
0.05
0.07
0.36
0.33
-0.09
-0.49
0.36
-0.29
-0.15
0.20
BaCI2 Acidity meq/100g
1
2
3
ALL
18.6
19.4
17.6
18.6
1.45
2.45
4.99
3.13
0.00
0.73
-1.05
34.8
35.0
..
34.9
2.14
2.89
..
2.52
-0.13 17.4
0.11 18.3
16.0
17.2
1.29
1.80
0.79
1.53
0.13
1.10
-1.24
0.44
1.69
0.71
0.99
0.07
0.88
0.62
0.81
-0.55
0.70
-0.28
KCI Extractable Al meq/100g
1
2
3
ALL
2.72
3.12
3.07
2.97
0.21
0.29
0.18
0.30
-0.25
0.15
0.10
3.72
3.93
—
3.84
0.62
0.70
..
0.66
-0.12 1.29
0.10 1.50
1.54
1.42
0.09
0.23
0.10
0.18
-0.13
0.08
0.12
0.06
0.14
0.18
0.15
0.00
0.03
0.05
0.06
-0.09
-0.01
0.04
Ca in 0.002M CaCI2 meq/100g
1
2
3
ALL
0.35
0.46
0.35
0.40
0.05
0.06
0.12
0.09
-0.04
0.07
-0.04
0.49
0.55
_,
0.52
0.05
0.04
..
0.06
-0.03 0.61
0.03 0.64
0.55
0.60
0.04
0.01
0.12
0.07
0.01
0.04
-0.05
0.71
0.59
0.59
0.61
0.04
0.39
0.03
0.21
0.11
-0.02
-0.02
(continued)
196
-------
Table F-1. Continued
Lab
1
2
3
All
1
2
3
All
1
2
3
All
1
2
3
ALL
1
2
3
ALL
1
2
3
ALL
1
2
3
ALL
1
2
3
ALL
Mean
0.14
0.12
0.11
0.13
0.07
0.06
0.06
0.06
0.04
0.03
0.03
0.03
0.02
0.02
0.02
0.02
0.10
0.08
0.07
0.08
0.60
0.65
0.66
0.63
0.60
0.63
0.54
0.59
0.39
0.45
0.39
0.41
A
SD
0.03
0.01
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.00
0.00
0.01
0.01
0.01
0.00
0.01
0.03
0.02
0.02
0.03
0.05
0.06
0.08
0.07
0.09
0.12
0.06
0.10
0.05
0.04
0.02
0.05
d
0.02
0.00
-0.02
•
0.01
0.00
-0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.01
-0.01
•
-0.04
0.01
0.03
0.00
0.04
-0.06
-0.02
0.04
-0.02
Mean
0.04
0.03
_
0.04
0.02
0.01
_
0.01
0.02
0.02
—
0.02
0.05
0.04
_
0.04
0.19
0.15
_
0.17
0.58
0.58
_
0.58
0.79
0.75
—
0.77
0.84
0.99
~
0.93
... Audit ^amnln*
B& Bw
SD d Mean SD
Mg in 0.002M CaCI2 meq/100g
0.00 0.00 0.04 0.00
0.00 0.00 0.04 0.00
0.03 0.01
0.01 . 0.04 0.01
K in 0.002M CaCI2 meq/100g
0.01 0.00 0.01 0.00
0.00 0.00 0.01 0.00
0.01 0.00
0.00 . 0.01 0.00
Na in 0.002M CaCI2 meq/100g
0.01 0.00 0.02 0.01
0.00 0.00 0.02 0.00
0.02 0.00
0.01 . 0.02 0.00
Fe in 0.002M CaCI2 meq/100g
0.01 0.01 0.00 0.00
0.01 -0.01 0.00 0.00
0.00 0.00
0.01 . 0.00 0.00
Al in 0.002M CaCI2 meq/100g
0.02 0.02 0.00 0.00
0.04 -0.02 0.00 0.00
0.01 0.00
0.03 . 0.00 0.00
Fe in Pyrophosphate % dry wt
0.04 0.00 0.80 0.06
0.10 0.00 0.84 0.08
0.94 0.12
0.07 . 0.85 0.10
Al in Pyrophosphate % dry wt
0.05 0.02 0.60 0.02
0.13 -0.02 0.54 0.07
0.59 0.06
0.10 . 0.58 0.05
Fe in Acid Oxalate % dry wt
0.23 -0.08 0.99 0.16
0.31 0.07 1.20 0.09
0.91 0.06
0.28 . 1.03 0.16
d
0.00
0.00
-0.01
•
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
•
0.00
0.00
-.00
-0.05
-0.01
0.09
•
0.02
-0.04
0.01
•
-0.04
0.17
-0.11
Mean
0.02
0.01
0.02
0.01
0.01
0.01
0.01
0.01
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.05
0.03
0.04
0.04
0.06
0.04
0.06
0.06
0.11
0.04
0.10
0.08
C
SD
0.00
0.01
0.00
0.01
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.01
0.01
0.00
0.01
0.01
0.01
0.07
0.01
0.07
0.06
d
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
-.00
0.01
-0.01
0.00
0.01
-0.02
0.01
0.03
-0.04
0.02
(continued)
197
-------
Table F-1. Continued
Lab
1
2
3
ALL
Mean
0.40
0.48
0.38
0.42
A
SD
0.06
0.07
0.04
0.07
d
-0.03
0.06
-0.05
Mean
0.95
0.98
—
0.97
Audit S"
Bs
SD d
Al in Acid Oxalate
0.12 -0.01
0.13 0.01
_
0.12
t
Mean
% dry wt
0.87
1.01
0.75
0.88
Fe in Citrate Dithionite % dry
1
2
3
ALL
1.68
1.73
2.23
1.85
0.21
0.15
0.14
0.29
-0.17
-0.12
0.38
0.93
1.05
..
0.99
0.06 -0.06
0.14 0.05
_
0.12
1.77
1.83
2.10
1.88
Bw
SD
0.06
0.04
0.08
0.11
wt
0.55
0.08
0.13
0.38
d
-0.01
0.13
-0.12
-0.11
-0.06
0.22
Mean
0.07
0.05
0.07
0.06
0.17
0.12
0.23
0.19
C
SD
0.01
0.00
0.01
0.01
0.01
0.03
0.04
0.06
d
0.01
-0.01
0.01
-0.01
-0.06
0.05
A! in Citrate Dithionite % dry wt
1
2
3
ALL
0.45
0.51
0.56
0.50
0.03
0.05
0.04
0.06
-0.05
0.01
0.06
0.73
0.83
—
0.78
0.05 -0.05
0.07 0.05
_
0.08
0.60
0.67
0.78
0.67
0.20
0.03
0.09
0.15
-0.07
0.00
0.11
0.05
0.04
0.07
0.05
0.00
0.01
0.01
0.01
0.00
-0.02
0.01
Sulfate in H2O mg S/kg
1
2
3
ALL
31.6
29.9
26.9
29.7
1.20
1.45
2.25
2.44
1.90
0.21
-2.76
8.38
8.01
—
8.18
1.53 0.20
2.16 -0.17
..
1.87
23.8
22.7
21.7
22.9
1.40
0.68
3.98
2.33
0.96
-0.21
-1.23
2.74
2.75
3.65
3.20
0.34
1.68
0.84
1.15
-0.46
-0.45
0.45
Sulfate in PO4 mg S/kg
1
2
3
ALL
73.4
79.1
75.6
76.2
5.30
6.72
3.74
5.99
-2.77
2.90
-0.63
23.1
37.7
—
31.1
3.09 -7.95
25.2 6.60
_
19.8
103
111
115
109
2.42
7.50
3.62
6.75
-5.50
1.95
6.30
4.59
6.96
4.86
5.52
0.12
2.32
2.68
2.42
-0.92
1.44
-0.65
Sulfate 0 mg S/L
1
2
3
ALL
1
2
3
ALL
1
2
3
ALL
4.12
4.47
4.20
4.27
5.36
6.00
5.66
5.69
6.93
7.24
6.96
7.06
0.27
0.56
0.34
0.44
0.29
0.58
0.35
0.51
0.30
0.47
0.26
0.39
-0.16
0.19
-0.07
-0.33
0.31
-0.02
-0.13
0.18
-0.09
1.24
1.10
_
1.17
2.90
2.74
—
2.81
4.35
4.40
..
4.38
0.14 0.08
0.19 -0.06
..
0.18
Sulfate
0.10 0.09
0.25 -0.07
_
0.21
Sulfate
0.27 -0.03
0.30 0.02
.. ..
0.28
2.29
2.14
2.37
2.27
2 mg S/L
3.20
3.12
3.34
3.22
4 mg S/L
4.20
4.00
4.75
4.30
0.21
0.21
0.23
0.22
0.06
0.12
0.27
0.17
0.08
0.16
0.23
0.34
0.02
-0.13
0.10
-0.02
-0.10
0.13
-0.10
-0.30
0.45
0.57
0.56
0.50
0.53
2.56
2.46
2.49
2.49
4.41
4.45
4.45
4.45
0.00
0.16
0.04
0.09
0.07
0.27
0.07
0.16
0.33
0.04
0.17
0.15
0.04
0.03
-0.03
0.07
-0.04
0.00
-0.04
0.01
0.01
(continued)
198
-------
Table F-1. Continued
Lab Mean SD
Bs
Mean SD
Audit Sample" •
Bw
Mean SD
C
Mean SD
Sulfate 8 mg S/L
1
2
3
ALL
9.69
10.6
10.0
10.1
0.41
0.71
0.28
0.65
-0.44
0.48
-0.13
7.49
7.97
0.52
0.31
-0.26
0.21
7.75 0.47
6.44
6.70
6.51
6.53
0.27 -0.09
0.43 0.16
0.32 -0.02
0.33
Sulfate 16 mg S/L
1 15.6 0.84 -0.72
2 17.0 0.98 0.72
3 16.2 1.90 -0.10
ALL 16.3 1.38
1
2
3
ALL
28.5
30.6
30.0
29.7
0.95
1.73
0.49
1.52
-1.21
0.88
0.30
1 4.62 0.20 -0.04
2 4.64 0.17 -0.02
3 4.75 0.52 0.09
ALL 4.66 0.31
13.9
15.1
14.6
27.9
29.3
28.7
4.19
3.49
1.10
0.44
0.99
1.92
1.95
-0.63
0.53
11.3
12.6
12.0
11.9
0.48
0.51
0.33
0.70
Sulfate 32 mg S/L
-0.75
0.63
2.01
22.5
25.0
24.2
23.7
0.66
1.10
0.80
1.35
-0.57
0.72
0.14
-1.19
1.30
0.50
8.60 0.33 0.01
9.02 1.01 0.43
8.30 0.07 -0.29
8.59 0.64
16.9 0.07 -0.13
17.8 2.14 0.80
16.5 0.15 -0.49
17.0 1.28
Total Carbon % dry wt
0.80
0.31
0.38
-0.32
3.81 0.67
1.55
1.41
1.54
1.51
0.04 0.05
0.02 -0.10
0.14 0.03
0.10
Total Nitrogen % dry wt
1
2
3
ALL
0.15
0.15
0.17
0.16
0.02
0.01
0.02
0.02
0.00
0.00
0.01
0.14
0.11
0.04
0.01
0.01
-0.01
0.12 0.03
0.11
0.10
0.12
0.11
0.02
0.01
0.01
0.02
0.00
-0.01
0.01
32.4
34.5
32.2
33.0
0.13
0.14
0.15
0.14
0.02
0.00
0.01
0.01
0.13 -0.58
3.33 1.50
0.29 -0.81
2.07
0.01 -0.01
0.02 0.00
0.04 0.00
0.03
0.00 0.01
0.00 0.00
0.00 0.00
0.01
Total Sulfur % dry wt
1 .023
2 .027
3 .023
ALL
.001
.002
.002
.025
-0.002
0.002
-0.002
.002
.015
.018
_
.002
.001
_
.017
-0.002
0.001
_
.001
.020
.020
.018
•
.001
.000
.002
.019
0.001
0.001
-0.001
.001
.000
.003
.005
.004
.001
.002
.004
-0.004
-0.001
0.001
.002
Mean, standard deviation (SD), and laboratory difference (d) for audit samples; differences were not estimated for
the Oa horizon audit samples.
199
-------
Appendix G
Histograms of Range and Frequency Distributions
Appendix G consists of figures displaying the histograms of four data sets which show the
range and frequency distribution of the routine samples (RS), the field duplicates (FD), the
preparation duplicates (PD), and the natural audit samples (AS). Additional information relating to
these plots can be found under the heading "Representativeness" in Sections 2 and 3 of the report.
Histograms are presented for each of the 51 analytical parameters in the order described in Table
1-1 of Section 1 of the report.
200
-------
CO
O
2
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201
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