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

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                                              .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

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                               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

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                            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

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                            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

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                            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

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                            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

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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)

-------
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

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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

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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

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     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

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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


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= Field duplicates; S/H = Sampling class/horizon
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b Standard deviation data
reported in weight
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                                            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
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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
  above the knot point of 0.33 weight percent.
                                             87

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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
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                                              100

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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


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2
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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

-------
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

-------
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
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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.
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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

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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

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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
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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.
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     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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                                      References
American Chemical Society.  1983. Principles
     of Environmental  Analysis.   American
     Chemical Society Committee on Environ-
     mental  Improvement.    Anal.  Chem.
     55(14) :2210-2218.

Arnold,  S.  F.   1981.   The Theory of  Linear
     Models and Multivariate  Analysis.   J.
     Wiley and Sons, New York.  475 pp.

Aronoff, S.  1984.  An Approach to Optimized
     Labeling of Image Classes.  Photogram-
     metric Engineering and Remote Sensing
     50(6):719-727.

Bartz, J. K., S.  K. Drouse, K. A. Cappo, M. L.
     Papp,  G.  A. Raab,  L. J.  Blume, M.  A.
     Stapanian, F. C. Garner, and D. S. Coffey.
     1987.  Direct/Delayed Response Project:
     Quality Assurance Plan for Soil Sampling,
     Preparation, and Analysis.  U.S. Environ-
     mental Protection Agency,  Las Vegas,
     Nevada. EPA/600/8-87/021.

Belsley, D. A., E. Kuh, and  R. E.  Welsch.  1980.
     Regression  Diagnostics.  J. Wiley and
     Sons,  New York. 352 pp.

Byers, G. E., R. D. Van Remortel, C. J. Palmer,
     M. L. Papp, W. H. Cole, J. E. Teberg,  M.
     D. Best, M. J. Miah, A. D. Tansey, and J.
     K. Bartz. 1988.  Direct/Delayed Response
     Project: Quality Assurance Report  for
     Physical and Chemical Analyses of Soils
     from  the Northeastern  United States.
     U.S.  Environmental  Protection Agency,
     Las Vegas,  Nevada.  EPA/600/X-88/136.

Cappo,  K. A., L.  J. Blume, G. A. Raab, J.  K.
     Bartz,  and J. L. Engels.  1987.  Analytical
     Methods Manual for the  Direct/Delayed
     Response  Project.   U.S.  Environmental
     Protection  Agency, Las Vegas, Nevada.
     EPA/600/8-87/020.
Chen, C. W., S. A. Gherini, J. D. Dean, R. J. M.
     Hudson,  and  R. A.  Goldstein.   1984.
     Development  and  Calibration  of  the
     Integrated Lake-Watershed Acidification
     Study Model. In. Modeling of Total Acid
     Precipitation Impacts, J. L. Schnoor (Ed.),
     pp. 175-203.     Butterworth  Publishers,
     Boston, Massachusetts.

Coffey,  D. S.,  J.J. Lee, J.  K.  Bartz, R. D. Van
     Remortel, M. L. Papp, and G.  R. Holdren.
     1987.   Direct/Delayed Response Project:
     Field Operations and Quality Assurance
     Report for Soil Sampling and Preparation
     in the  Southern Blue Ridge Province  of
     the United States,  Volume I: Sampling.
     U.S. Environmental  Protection  Agency,
     Las Vegas, Nevada. EPA/600/4-87/041.

Conover, W. J.  1980.  Practical Nonparametric
     Statistics, Second Edition.  J. Wiley and
     Sons, New York. 493 pp.

Cosby,  B. J., R. F. Wright, G. M. Hornberger,
     and  J.  N. Galloway.   1984.   Model  of
     Acidification of  Groundwater in Catch-
     ments.  Internal project report submitted
     to EPA.  North Carolina State University,
     Raleigh,  North Carolina.

Haren, M. F., and R.  D. Van Remortel.  1987.
     Direct/Delayed  Response Project:   Field
     Operations and Quality Assurance Report
     for Soil Sampling and Preparation in the
     Southern Blue  Ridge  Province of the
     United States,  Volume II: Preparation.
     U.S. Environmental  Protection  Agency,
     Las Vegas, Nevada. EPA/600/4-87/041.

Long, G. L, and J. D. Winefordner.  1980.  Limit
     of Detection:    A  Closer  Look at the
     IUPAC Definition. Anal. Chem. 55(7):712.
                                            136

-------
SAS Institute, Inc.  1985.  SAS User's Guide:
     Statistics. Version 5. SAS Institute, Inc.,
     Gary, North  Carolina.  956 pp.

SAS Institute, Inc.  1986.   SAS System  for
     Linear Models.  SAS Institute, Inc., Gary,
     North Carolina.  210 pp.

Schmoyer, D. D., R. S. Turner, and D. A. Wolf.
     1988.  Direct/Delayed  Response Project:
     Interlaboratory Differences in the  North-
     eastern Soil Survey Data.  In press. U.S.
     Environmental  Protection  Agency, Las
     Vegas, Nevada.

Schnoor, J. L, W. D. Palmer,  Jr.. and  G. E.
     Glass.  1984.  Modeling Impacts of Acid
     Precipitation for Northeastern Minnesota.
     In  Modeling of Total  Acid Precipitation
     Impacts, J.  L. Schnoor (ed.), pp. 155-173.
     Butterworth   Publishers,   Boston,
     Massachusetts.

Steel, R. G. D., and  J. H. Torrie.  1960.  Prin-
     ciples  and   Procedures  of  Statistics.
     McGraw-Hill  Book Company, New York.
     481 pp.
Taylor, J. K.  I987. Quality Assurance of Chem-
     ical  Measurements.   Lewis Publishers,
     Chelsea, Michigan.  328 pp.

Turner, R. S., J. C. Goyert, C. C. Brandt, K. L
     Dunaway,  D.  D. Schmoyer,  and J.  A.
     Watts.  1987.  Direct/Delayed Response
     Project: Guide to Using and Interpreting
     the Data Base.  Environmental Sciences
     Division Publication No. 2871.  Oak Ridge
     National   Laboratory,   Oak    Ridge,
     Tennessee.

U.S. Department of Agriculture/Soil Conserva-
     tion Service.  1984. Soil Survey Labora-
     tory Methods and Procedures for Collect-
     ing Soil Samples.  Soil Survey Investiga-
     tions Report No.  1.   U.S. Government
     Printing Office, Washington, D.C.

U.S. Environmental  Protection Agency.  1985.
     Direct/Delayed Response Project: Long-
     term Response  of Surface Waters  to
     Acidic  Deposition,  Factors  Affecting
     Response and a Plan for Classifying that
     Response  on a Regional Scale.  U.S.
     Environmental   Protection   Agency,
     Corvallis, Oregon.
                                            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





























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SAMPI 1.
10

















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VALOL



















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                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

-------
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