&EPA   NHATS COMPARABILITY STUDY
       GC/ECO

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-J
o
                                                EPA Contract No. 68-DO-0174
                                                                  June  1992
                            NHATS COMPARABILITY  STUDY
                          Exposure Evaluation Division
                    Office of Pollution Prevention and Toxics
                      U.  S.  Environmental Protection Agency
                                401 M Street, S.W.
                               Washington,  DC 20460
                                      77 West Jacks     "':L 10..
                                      Chicago, IL 60oo - ..an ' Uih Fl°or

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                    DISCLAIMER


This document has  been reviewed and approved
for  publication by  the Office  of  Pollution
Prevention  and Toxics and  the  Office  of
Prevention,  Pesticides, and Toxic Substances,
U.S. Environmental  Protection Agency.  The use
of trade names or commercial products does not
constitute Agency  endorsement  or recommenda-
tion for use.

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                        AUTHORS  AND  CONTRIBUTORS

     This  Comparability Study is one  part  of the National  Human
Adipose Tissue  Survey (NHATS) .   The study was conducted   through
the  cooperative efforts of EPA  and contract  support staff.   EPA
participation was from the Exposure Evaluation Division within the
Office  of Pollution  Prevention and  Toxics  (OPPT) .   Within  the
Exposure  Evaluation Division, the  Design  and Development  Branch
and  the Field  Studies Branch were responsible for the  conduct  of
the  study and  the preparation  of the  final report.    Contract
support  to  OPPT  included  Westat, Battelle,  Midwest  Research
Institute  (MRI),  and  the Institute of Rural  Environmental  Health
at Colorado State University (CSU).

Westatr  Inc.

     Performed  data entry  and  data verification,  performed  the
statistical analysis,  and  wrote  the final report on the  compara-
bility study.

Key personnel included:
           John Rogers               Adam Chu

Institute of Rural Environmental  Healthr CSU

     Performed the chemical analyses on the  samples  using  both the
GC/ECD and HRGC/MS methods.

Key personnel included:
           John Tessari              Sharon Chaffey
           Michael Aaronson
                               111

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Midwest Research Institute

     Collected  and  stored  the  tissue  samples,  prepared  the
composite samples  for  analysis, and  contributed portions of  the
Quality Assurance  Project Plan  (QAPjP) .

Key personnel included:
          John Stanley              John Hosenfeld
          Jack Balsinger

Battelle Columbus  Division

     Processed the patient  summary  reports (PSRs), developed  the
composite design, and  contributed the sections for the  composite
design in the QAPjP.

Key personnel included:
          Greg Mack                Lesly Arnold
          Tamara Collins
Exosure Evaluation  Division
     Conducted  the  National  Human Adipose  Tissue  Survey on  an
annual  basis,   managed  tasks  of  contractors,  and  reviewed  and
edited reports .

Key personnel included:
          Cindy Stroup             Mary Frankenberry
          Phil Robinson            John Schwemberger
          Joe Breen                Janet Remmers
                                IV

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                         TABLE OF CONTENTS


                                                               Page

EXECUTIVE SUMMARY	xv

1    INTRODUCTION 	 1

2    SUMMARY AND CONCLUSIONS 	 5

3    SAMPLE COLLECTION, PREPARATION, AND ANALYSIS PROCEDURES ... 9

     3.1    Collection and Storage of the  Human Adipose Tissue
            Specimens	10

     3.2    Sample  Design  for the Comparability Study	11
     3 .3    Sample  Preparation	13
            3.3.1   Preparing  the composite  samples and
                    batches	13
            3.3.2   Method blanks samples 	 15
            3.3.3   Quality control  samples 	 15

     3 .4    Chemical Analysis Methods	19
            3.4.1   GC/ECD analysis  procedures 	 20
            3.4.2   HRGC/MS analysis procedures 	 21
     3 .5    Data File Preparation	24

4    DESCRIPTION OF  THE DATA	25

     4 .1    Organization of the  Data	25
     4 .2    Preliminary Review of the Data	27

5    OVERVIEW OF THE STATISTICAL ANALYSIS	33

     5 .1    Correction for Blanks	33

     5.2    Outliers and the  Use of Remarks	35
     5.3    A Model for the Data	36
     5.4    Basis for Analyzing  the Log Transformed
            Concentrations	40

6    ANALYSIS OF DETECTION LIMITS AND PERCENT RECOVERY 	 45
     6.1    Comparison Detection Limits	45
     6.2    Calculating Recovery	49
            6.2.1   Calculating  recovery using  spiked
                    multisplit samples 	 49
            6.2.2   Calculating  recovery using  quality  control
                    samples	52
            6.2.3   Comparison of HRGC/MS and GC/ECD recovery... 54

     6.3    Comparison of  Percent Detected	55

7    RELATIONSHIP BETWEEN THE GC/ECD AND HRGC/MS METHODS 	 61

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                   TABLE OF CONTENTS (continued)

                                                               Page
     7.1    Modeling the Relationship Between the GC/ECD and
            HRGC/MS Methods	61
     7.2    Comparison of HRGC/MS and GC/ECD Measurements	69
     7 .3    Comparisons for Each Compound	75
     7 .4    Plots of HRGC/MS versus  GC/ECD  Measurements	81
8    COMPARISON  OF  GC/ECD  AND HRGC/MS MEASUREMENTS ACROSS
     YEARS	101
     8 .1    Plots of Measurements Over Time	101
     8.2    Assessment of the Results	Ill
9    PCB MEASUREMENTS	115
     9.1    Comparison of PCB Reporting and Measurement
            Procedures	115
     9.2    PCB Recovery Using the HRGC/MS  and GC/ECD Methods... 116
     9.3    Comparison of HRGC/MS and GC/ECD Paired
            Measurements	118
10   ANALYSIS OF PRECISION AND  COMPONENTS  OF VARIANCE 	 125
     10.1   Standard Deviation Versus Mean	125
            10.1.1  Spiked  multisplit samples 	 127
            10.1.2  Paired  samples 	 128
            10.1.3  Results 	 129
     10 .2   Components of Variance	132
            10.2.1  Components  of variance  for  the  HRGC/MS
                    measurements 	 134
            10.2.2  Components  of variance  for  the  GC/ECD
                    measurements 	 142
     10.3   Summary and Comparison of HRGC/MS and GC/ECD
            Precision	146
11   REFERENCES	151
APPENDIX A:  SUMMARY DATA  TABLES	153
APPENDIX B:  CONVERSION FROM STANDARD DEVIATION OF LOG
     TRANSFORMED DATA TO COEFFICIENT OF VARIATION	181
APPENDIX C:  RECOVERY FROM MULTISPLIT SAMPLES  	 185
APPENDIX D:  DESCRIPTION OF THE  ANALYTICAL PROCEDURES 	 191
     D. 1    Summary of the MOG-GC/ECD Procedure	193
     D .2    Summary of the HRGC/MS Procedure	197
                                 VI

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                   TABLE OF CONTENTS  (continued)

                                                               Page
APPENDIX E:  DISCUSSION OF THE VARIANCE COMPONENTS	203
                                vii

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                           LIST OF TABLES


                                                               Page

Table 1.     Number of Samples for HRGC/MS and GC/ECD Chemical
            Analysis by Sample Type and Batch	14

Table 2.     Spiking Levels for the Multisplit Composite
            Samples	16

Table 3.     Concentrations of Target Compounds in GC/ECD
            Porcine Fat QC Samples,  in ug/g wet weight	18

Table 4.     Spiking Levels of Surrogate Compounds in HRGC/MS
            Samples	22

Table 5.     Model for the GC/ECD and HRGC/MS Measurements
            with an Explanation of Each Term	41

Table 6.     Detection Limits  (ug/g)  Using the HRGC/MS and
            GC/ECD Methods, for all Compounds Reported on the
            GC/ECD Forms 	  46

Table 7.     Average Recovery, with 95% Confidence Intervals,
            for the GC/ECD Measurements on Spiked Multisplit
            and Quality Control Samples 	  53

Table 8.     Average Recovery, with 95% Confidence Intervals,
            for the HRGC/MS Measurements on Spiked Multisplit
            and Quality Control Samples 	  56

Table 9.     Number of GC/ECD and HRGC/MS Measurements on
            Paired Composite Samples and Percent Detected, by
            Data Qualifier and Analytical Method	57

Table 10.   Correlation Between Log Transformed HRGC/MS and
            GC/ECD Measurements 	  71

Table 11.   Summary of the Statistical Tests for Batch
            Effects and Nonconstant Recovery 	  72

Table 12.   Geometric Mean Ratio of GC/ECD to HRGC/MS
            Measurements 	  73

Table 13.   Summary of PCB Recovery Measurements	120

Table 14.   Coded HRGC/MS versus Coded GC/ECD PCB
            Measurements in Paired Samples 	  123

Table 15.   Comparison of HRGC/MS and GC/ECD Measurements in
            Multisplit Samples 	  124
                                viii

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                     LIST OF TABLES  (continued)
Table 16.



Table 17.



Table 18.



Table 19.


Table 20.


Table A-l.


Table A-2.


Table A-3.


Table A-4.


Table A-5.


Table A-6.


Table A-7.


Table A-8.


Table A-9.


Table A-10.


Table A-ll.
                                                   Page
Variance Components for Log Transformed
Measurements for HRGC/MS Fraction 1 Surrogate
Compounds	136

Variance Components for Log Transformed
Measurements for HRGC/MS Fraction 2 Surrogate
Compounds	139

Variance Components for Log Transformed
Measurements for Fraction 1 Compounds in HRGC/MS
Spiked Dichloromethane Samples 	  141

Variance Components for Log Transformed Aldrin
Measurements in GC/ECD Samples 	  144

Summary of Variance Components for GC/ECD and
HRGC/MS Measurements 	  147

Summary of GC/MS Measurements on Method Blanks,
Nominal Concentration in ug/g	156

Summary of GC/MS Measurements on Dichloromethane
Spike Samples, as Percent Recovery	158
Summary of GC/MS Measurements on Paired Composite
Samples, ug/g	
160
Summary of GC/MS Measurements on the High Level
Spiked Multisplit Composite Samples, ug/g	162

Summary of GC/MS Measurements on the Low Level
Spiked Multisplit Composite Samples, ug/g	164

Summary of GC/MS Measurements on the Mid Level
Spiked Multisplit Composite Samples, ug/g	166

Summary of GC/MS Measurements on Surrogate QA
Compounds in all Samples, Percent Recovery	168
Summary of GC/ECD Measurements on Method Blanks,
ug/g	
169
Summary of GC/ECD Measurements on the Porcine Fat
Samples in Batches 1, 2, and 3, ug/g	170

Summary of GC/ECD Measurements on the Porcine Fat
Samples in Batches 4 through 10, ug/g	171

Summary of GC/ECD Measurements on the paired
Composite Samples, ug/g	172
                                 IX

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                     LIST  OF  TABLES  (continued)


                                                               Page
Table A-12.  Summary  of GC/ECD Measurements of Aldrin Recovery
            in All Samples Except Extracts, ug/g	173

Table A-13.  Summary  of GC/ECD Measurements on the High Level
            Spiked Multisplit Composite Samples, ug/g	174

Table A-14.  Summary  of GC/ECD Measurements on the Low Level
            Spiked Multisplit Composite Samples, ug/g	175

Table A-15.  Summary  of GC/ECD Measurements on the Mid Level
            Spiked Multisplit Composite Samples, ug/g	176

Table A-16.  Summary  of GC/ECD Measurements on Extracts of the
            Unspiked Samples Associated with the High Level
            Spiked Multisplit Composite Samples, ug/g	177

Table A-17.  Summary  of GC/ECD Measurements on Extracts of the
            Unspiked Samples Associated with the Low Level
            Spiked Multisplit Composite Samples, ug/g	178

Table A-18.  Summary  of GC/ECD Measurements on Extracts of the
            Unspiked Samples Associated with the Mid Level
            Spiked Multisplit Composite Samples, ug/g	179

Table B-l.  Coefficient of Variation for the Untransformed
            Data for Selected Values of s, the Standard
            Deviation of  the Log Transformed Data	184

Table C-l.  Recovery for  Spiked Compounds in Multisplit
            Samples  Analyzed Using the HRGC/MS Method	187

Table C-2.  Recovery for  Spiked Compounds in Multisplit
            Samples  Analyzed Using the GC/ECD Method	188

Table C-3.  Recovery for  Spiked Compounds in Multisplit
            Samples  Analyzed Using the GC/ECD Method, with
            Unspiked Concentrations Measured in Extracts 	 189

Table E-l.  Variance Components and Sources of Error 	 207
                                 x

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                          LIST OF FIGURES


                                                               Page

Figure  1.    Processing steps for the sample preparation and
             analysis of composite samples	17

Figure  2.    Subsets of the data with comparable measurements
             by sample type and type of compound	26

Figure  3.    Histogram of GC/ECD measurements of p,p'-DDE on
             paired composite adipose tissue samples	31

Figure  4.    Histogram of HRGC/MS measurements of p,p'-DDE on
             paired composite adipose tissue samples	31

Figure  5.    Analyses to be performed on subgroups of data
             defined by sample type and type of compound	34

Figure  6.    Average HRGC/MS and GC/ECD detection limits for
             primary compounds	48

Figure  7.    Percent of paired samples with detected
             concentrations using the HRGC/MS and GC/ECD
             methods	58

Figure  8     Average ratio of the GC/ECD and HRGC/MS
             measurements for primary compounds, with 95%
             confidence intervals	74

Figure  9.    HRGC/MS versus GC/ECD concentration measurements
             for p,p'-DDT in paired composite human adipose
             tissue samples	83

Figure  10.   HRGC/MS versus GC/ECD concentration measurements
             for p,p'-DDE in paired composite human adipose
             tissue samples	84

Figure  11.   HRGC/MS versus GC/ECD concentration measurements
             for beta-BHC in paired composite human adipose
             tissue samples	85

Figure  12.   HRGC/MS versus GC/ECD concentration measurements
             for dieldrin in paired composite human adipose
             tissue samples	86

Figure  13.   HRGC/MS versus GC/ECD concentration measurements
             for heptachlor epoxide in paired composite human
             adipose tissue samples	87

Figure  14 .   HRGC/MS versus GC/ECD concentration measurements
             for oxychlordane in paired composite human
             adipose tissue samples	88


                                xi

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                    LIST OF  FIGURES(continued)
                                                               Page
Figure 15.   HRGC/MS versus GC/ECD concentration measurements
             for trans-nonachlor in paired composite human
             adipose tissue samples	89

Figure 16.   HRGC/MS versus GC/ECD concentration measurements
             for uncorrected hexachlorobenzene in paired
             composite human adipose tissue samples	90

Figure 17.   HRGC/MS versus GC/ECD concentration measurements
             for corrected hexachlorobenzene for recovery in
             paired composite human adipose tissue samples	91

Figure 18.   Transformed HRGC/MS versus GC/ECD p,p'-DDT
             measurements used for statistical tests	92

Figure 19.   Transformed HRGC/MS versus GC/ECD p,p'-DDE
             measurements used for statistical tests	93

Figure 20.   Transformed HRGC/MS versus GC/ECD beta-BHC
             measurements used for statistical tests	94

Figure 21.   Transformed HRGC/MS versus GC/ECD dieldrin
             measurements used for statistical tests	95

Figure 22.   Transformed HRGC/MS versus GC/ECD heptachlor
             epoxide measurements used for statistical
             tests	96

Figure 23.   Transformed HRGC/MS versus GC/ECD oxychlordane
             measurements used for statistical tests	97

Figure 24.   Transformed HRGC/MS versus GC/ECD trans-
             nonachlor measurements used for statistical
             tests	98

Figure 25.   Transformed HRGC/MS versus GC/ECD uncorrected
             hexachlorobenzene measurements used for
             statistical tests	99

Figure 26.   Transformed HRGC/MS versus GC/ECD corrected
             hexachlorobenzene for recovery measurements used
             for statistical tests	100

Figure 27.   Weighted average GC/ECD, HRGC/MS, and HRGC/MS
             adjusted p,p'-DDT concentrations for design
             samples from 1970 through 1984	103

Figure 28.   Weighted average GC/ECD, HRGC/MS, and HRGC/MS
             adjusted p,p'-DDE concentrations for design
             samples from 1970 through 1984	104
                                XII

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                     LIST OF FIGURES(continued)
                                                               Page
Figure 29.   Weighted average GC/ECD, HRGC/MS, and HRGC/MS
             adjusted beta-BHC concentrations for design
             samples from 1970 through 1984	105

Figure 30.   Weighted average GC/ECD, HRGC/MS, and HRGC/MS
             adjusted dieldrin concentrations for design
             samples from 1970 through 1984	106

Figure 31.   Weighted average GC/ECD, HRGC/MS, and HRGC/MS
             adjusted heptachlor epoxide concentrations for
             design samples from 1970 through 1984	107

Figure 32.   Weighted average GC/ECD, HRGC/MS, and HRGC/MS
             adjusted oxychlordane concentrations for design
             samples from 1970 through 1984	108

Figure 33.   Weighted average GC/ECD, HRGC/MS, and HRGC/MS
             adjusted trans-nonachlor concentrations for
             design samples from 1970 through 1984	109

Figure 34.   Weighted average GC/ECD, HRGC/MS, and HRGC/MS
             adjusted corrected hexachlorobenzene
             concentrations for design samples from 1970
             through 1984	110
Figure 35,
Figure 36,


Figure 37,



Figure 38.




Figure 39.
Weighted average GC/ECD, HRGC/MS, and HRGC/MS
adjusted corrected hexachlorobenzene
concentrations for design samples from 1970
through 1984, with two outliers removed from the
calculation of the 1982 HRGC/MS and HRGC/MS
adjusted average	
Histogram of HRGC/MS surrogate compound
recoveries in lipid samples	
Histogram of HRGC/MS PCB measurements with
shading to indicate coded GC/ECD concentration
in paired samples	
                                                                 112
119
121
Slope of the linear relationship between the log
of the standard deviation and the log of the
mean for GC/ECD measurements on multisplit
spiked and paired samples	
130
Slope of the linear relationship between the log
of the standard deviation and the log of the
mean for HRGC/MS measurements on multisplit
spiked and paired samples	
                                                                 131
                                xiii

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LIST OF FIGURES(continued)





                                           Eage
            xiv

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                       EXECUTIVE  SUMMARY


     The National Human Adipose  Tissue  Survey  (NHATS)  is  a  chemi-
cal  monitoring program operated by EPA  from  1970 through  1992.
The  objectives of the NHATS program were:  (1)  to  detect and quan-
tify the  concentrations  and  prevalences of  selected toxic  sub-
stances in the adipose tissue  of the  general U.S.  population;  (2)
to measure trends in these concentrations over  time;  (3) to  assess
the  effects  of regulatory  actions;  and  (4)  to provide  baseline
body burden data for chemicals  of interest to EPA.

     The data for NHATS were generated by collecting  and analyzing
adipose  tissue  specimens for selected  toxic substances.    The
adipose tissue specimens  were  obtained  from  cooperating hospitals
and medical examiners across the  continental United States.

     Initially, the NHATS program focused on  organochlorine  pesti-
cides  and polychlorinated  biphenyls  (PCBs)   using  the   Modified
Mills Olney Gaither  (MOG)  protocol and packed column  gas chromato-
graph/electron  capture detection  (GC/ECD)  method.   However,  in
1982  the  program  was expanded  to  include  a  wider range  of
lipophilic compounds.  In 1982 and 1984, the  samples  were  analyzed
using a method based on  a high  resolution gas chromatograph/mass
spectrometer (HRGC/MS).

     A comparability  study  was initiated to compare the  measure-
ments from the two  analytical methods for similar adipose  tissue
samples.   The determination  of data  comparability  between  the
methods is essential  to  allow valid assessments of the data  when
combining results  for GC/ECD  and  HRGC/MS for  trend analysis  and
baseline estimates.  The  specific  objectives of the  Comparability
Study were to:  1)  characterize the detection  levels,  recovery,  and
precision of the  measurements,  and to  compare these for the  two
methods;  2)  assess whether  there  are  significant  differences
between the measurements  from  the two analytical  methods; and 3)
                                xv

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describe the relationship between  the  HRGC/MS and GC/ECD measure-
ments and  assess  its usefulness for converting measurements from
one method to the other.

     The study  objectives  were met by  analyzing paired composite
samples  using  both  the  HRGC/MS and GC/ECD methods.   Individual
FY84 specimens were  combined to create larger composite samples to
reduce analysis costs and to provide adequate sample quantities to
meet  the  sensitivity  requirements  for both  HRGC/MS  and  GC/ECD
analyses.   The composite  samples were  grouped into  batches for
processing and  analysis.   Each sample was  split to create paired
samples, one for  GC/ECD  analysis and the other for HRGC/MS anal-
ysis.  Method blanks, quality control,  and multisplit samples were
analyzed, in addition to the paired samples, to assess the quality
of the data and to  estimate  the precision  and recovery of the two
methods.

     The HRGC/MS method can detect a wider range of compounds than
the  GC/ECD,  which is limited  because  of its  specificity  to com-
pounds with high  electron capture  cross-sections.   Of the 20 com-
pounds measured by both methods, only the following nine compounds
were positively  identified  and quantified by  both  the GC/ECD and
HRGC/MS  methods:  p,p'-DDT;  p,p'-DDE;  beta-BHC;  dieldrin,  hepta-
chlor epoxide;  oxychlordane; trans-nonachlor;  hexachlorobenzene;
and  PCBs.1  These are the same  compounds that have been routinely
detected in previous GC/ECD efforts.  These compounds are referred
to as the  primary compounds  for the comparability study.  Because
the  results  for  dieldrin were limited,  the comparison of the two
analytical  methods   is  based  primarily on the measurements of:
p,p'-DDT;  p,p'-DDE; beta-BHC;  heptachlor epoxide;  oxychlordane;
trans-nonachlor; hexachlorobenzene;  and PCBs.

     The GC/ECD method is  more sensitive than the  HRGC/MS method
in measuring all  primary compounds except PCBs.   Because the de-
1Total PCBs are counted as one compound here, however,  for the HRGC/MS method,
the concentrations of individual PCB homologs are reported.
                                xvi

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tection  limits vary  among samples, the  average detection  limits
for  each compound were  used for comparison.   The average  GC/ECD
detection limits are  consistently below  0.01  ug/g,  compared  to the
average  HRGC/MS detection limits  which  range  from  0.01 to  0.35
ug/g  for  different  compounds.2   However,  for  PCBs,  the  GC/ECD
method is much less  sensitive than the HRGC/MS  method,  with aver-
age detection  levels of 0.43 ug/g and 0.01 ug/g respectively.3

     Method  recovery was  determined from the analysis  of samples
spiked with  known  amounts of target analytes.   The recovery esti-
mates vary,  depending on the compound.  The GC/ECD recoveries for
spiked lipid material range from 52% to 109%.   The HRGC/MS  recov-
ery estimates  are  less precise and  range  from  26% to  62%, with the
exception  of beta-BHC with an estimated  recovery  of 99%.    These
general  results are  consistent  with  the ratio  of the GC/ECD  to
HRGC/MS  measurements  in the paired samples.   The GC/ECD recovery
estimates are consistently greater than the HRGC/MS  estimates for
all compounds  except beta-BHC for which the estimates  are similar.

     For all compounds except dieldrin  and  PCBs, a  strong  linear
relationship existed between the HRGC/MS and GC/ECD measurements
in the  paired  samples,  with the  ratio  of the  HRGC/MS to  GC/ECD
measurements being  roughly constant.    For   some  compounds,  the
ratio of the HRGC/MS  to GC/ECD measurements varied among analysis
batches  and depended  on the concentration  in  the samples.   The
ratio of the  average GC/ECD measurement to  the average HRGC/MS
measurement  in the paired samples  ranged from  1.25  to 3.88  for
different compounds.   For p,p'-DDT, p,p'-DDE, heptachlor epoxide,
oxychlordane,  trans-nonachlor, and hexachlorobenzene,  the  ratios
are statistically significantly greater than 1.0.

     For the statistical  analysis,  PCBs  were analyzed  separately
because  the  GC/ECD  concentrations in  the  paired  samples  were
2The measurements are reported in micrograms per gram lipid weight, unless
otherwise noted.
3The BCD method provides data on total PCBs while the MS method is capable of
providing data on individual PCB compounds.

                               xvii

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reported  on  an  interval  scale4 rather than  as a  concentration,
which limited the  ability to compare the HRGC/MS and  GC/ECD mea-
surements.  The  results  for the PCB measurements were similar to
other primary compounds in  that the  GC/ECD measurements tended to
be higher than the  corresponding HRGC/MS measurements.  The ratio
of the GC/ECD and HRGC/MS measurements could  not be  determined due
to the interval nature of  the GC/ECD  data.

     The  precision with  which a  concentration  can  be  measured
depends  on the  analysis  method  and the  concentration in  the
sample,  with the standard deviation of the  measurements increasing
as the concentration  increases.   For the primary compounds other
than dieldrin the  GC/ECD  measurements were more precise  than the
HRGC/MS measurements.   For these compounds, the following rules of
thumb can be used to describe the precision:  I) the  coefficient of
variation of the HRGC/MS  measurements was three times  larger than
for the  GC/ECD measurements;  and 2) 95%  of  GC/ECD measurements
were within  22%  of the actual  concentrations and  95% of HRGC/MS
measurements were  within  63% of the actual concentrations  in the
samples.

     The final objective  of the Comparability Study was to assess
whether the  relationship  between  the HRGC/MS  and GC/ECD  measure-
ments  in the  FY84  composite  samples  is  useful  for  comparing
average concentrations measured by different  methods in different
years.  To make  this  assessment, the ratios  of the  average GC/ECD
to  HRGC/MS measurements  were  used to  adjust  the 1982  HRGC/MS
measurements for comparison to the trends  in  the GC/ECD  measure-
ments in  neighboring  years.  Given  the likely prediction errors,
the adjusted HRGC/MS  averages from  1982 were  consistent  with the
trends in the  GC/ECD  averages prior  to  1982  and in 1983.   Use of
this adjustment  method assumes that the relationship  between the
GC/ECD and HRGC/MS measurements are unaffected by  changes  in the
Concentrations were reported as being in one of the intervals 0 to  .33 ug/g,
.33 to 1 ug/g, 1 to 3 ug/g, or greater than 3 ug/g.
                               XVI11

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procedures over  time,  the laboratory used,  or the concentration

levels in the samples.


     An alternate procedure  for  comparing the HRGC/MS and GC/ECD

data  is  to correct all  measurements  for recovery.   Because the

recovery depends  on the sample matrix, this  would require  recovery

measurements on lipid samples for each year of data.  An  adequate

number of  samples for estimation  of  recovery  is  recommended to

limit  the  possible  error in  the estimated  recoveries  and the

corresponding  averages.   When  adequate  recovery  information is

available,  correcting the HRGC/MS data for recovery may be prefer-

able  to  using the  ratios from  FY84 data  because the  recovery

correction  requires making fewer assumptions.


     The conclusions  from the Comparability Study are:


     The  measurements  from the HRGC/MS   and GC/ECD  methods
     cannot  be  compared  without  accounting  for  differences
     in  recovery,  precision,  and  detection  limits.

     •    The GC/ECD method has generally more precise estimates,
          higher  recovery,  and  lower  detection limits  than the
          HRGC/MS method  when measuring the primary compounds.

          The  GC/ECD  method  was more precise than  the  HRGC/MS
          method.  The GC/ECD method has  higher recovery than the
          HRGC/MS method for all  compounds except beta-BHC for
          which the  recovery estimates  are similar.   The GC/ECD
          method  has  lower  detection  limits  than  the  HRGC/MS
          method  for  all  compounds except  PCBs.

     •    The  HRGC/MS measurements  are  lower  than  the  GC/ECD
          measurements for all primary compounds.  The differences
          are statistically significant for some compounds.

     •    Compared to the GC/ECD  method, the HRGC/MS method can be
          used to study a wider range of  target compounds and was
          chosen  in recent years in  order to  expand  the  list of
          chemicals monitored by  NHATS.
                               xix

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For   comparison  of  measurements   across  methods  and
years,  the  measurements  can  be  adjusted  using  either
the  ratio of  the  6C/ECD to  HRGC/MS  measurements  or  by
correcting  for   recovery.    A  correction  for  recovery
requires  making  fewer  assumptions.

•    Using the  ratios  of  the  average GC/ECD  and HRGC/MS
     measurements  in FY84 samples  to  convert from  one  method
     to  the  other  proved reasonable  for  the  1982   data.
     However, this  conversion  method  has  limitations.  The
     relationship  between .the GC/ECD and HRGC/MS  measurements
     may change with time,  the laboratory used,  or the con-
     centration levels  in the samples.

•    A combination of theoretical arguments and data analysis
     suggests that the  comparability of the measurements from
     the two  methods can  be improved by correcting  for  recov-
     ery within each sample year and  laboratory.   Additional
     analysis beyond the scope of this  study  is required  to
     evaluate the best method for making  a recovery correc-
     tion and how it compares  to  use of  a ratio  correction
     based on the  Comparability Study.
                           xx

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                             INTRODUCTION
     The National Human Adipose Tissue Survey  (NHATS)  has been the
main operative  program of the  National  Human Monitoring Program
 (NHMP).   NHMP is a chemical monitoring program designed  to fulfill
the human and environmental monitoring mandates of both the Toxic
Substances  Control  Act   (TSCA)  and  the  Federal  Insecticide,
Fungicide  and  Rodenticide  Act (FIFRA),   as  amended.   NHATS  was
first established by the U. S. Public Health  Service  in 1967,  and
was transferred to the U.  S. Environmental Protection  Agency (EPA)
in 1970.  NHATS was conducted by EPA through 1992.  The  Agency now
is in the process  of developing alternate human tissue  monitoring
activities  as   part  of the National  Human  Exposure Assessment
Survey  (NHEXAS).

     The data for NHATS were generated by collecting  and analyzing
adipose  tissue  specimens  for  selected   toxic  substances.    The
adipose tissue  specimens were  obtained from cooperating hospitals
and medical examiners within a statistically representative sample
of metropolitan statistical areas  across  the continental United
States.   Cooperating pathologists and medical examiners collected
and sent  adipose tissue specimens  to EPA on  a  continuing  basis
throughout each  fiscal  year.   The pathologists and medical  exam-
iners also  supplied EPA with  a limited  amount of    demographic,
occupational,  and medical  information for each specimen.   This
information  allows  reporting   of  the  residue  levels   by
subpopulations  of  interest,  namely  sex, race,  age group,  and
geographic region.

     Historically,  the NHATS  program  monitored  human  adipose
tissue  for the  presence and levels of 19  organochlorine  pesticides
and polychlorinated biphenyl compounds  (PCBs).  Nine of these 20
compounds have  been regularly  detected  in more than 90% of  the
annual  NHATS specimens  (Robinson,  Mack,  Remmers, Mohadjer, 1990).
The NHATS program has documented a  decrease in the concentrations

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of  several of these  compounds over  time.   NHATS data  showed a
decrease in the percentage of samples with PCB levels above 3 ug/g
wet weight1 starting  in  1978.   This decrease began  2  years after
Congressional  legislation in  1976 banned  production  and  use  of
PCBs.

     The success  in monitoring the  effect of regulation  and the
desire  to  broaden knowledge  of the range  of chemicals  to which
humans  are exposed  led EPA to expand the  list  of  chemicals moni-
tored by NHATS to include a wider range  of lipophilic compounds.
The expansion of  the  chemical  list  led  to a new chemical measure-
ment protocol  in  1982.   From  1970  through 1981 and in  1983,  the
NHATS samples  were  analyzed by the  Modified Mills  Olney Gaither
(MOG)  protocol using  a packed  column  gas  chromatograph with elec-
tron capture detection (GC/ECD) (Sherma  and Beroza  1980) .   In 1982
the samples were analyzed using a method that applies high resolu-
tion gas chromatography/mass  spectrometry (HRGC/MS)  for detection
of  analytes   (Stanley 1985).   In  1984,  samples  were split  and
analyzed using both  methods for the Comparability Study.

     Since a  primary objective of the  NHATS program  has  been  to
estimate baseline levels and trends over time, possible changes in
the measurements  associated  with  changes  in  the analytical tech-
nique are  of  interest.   Therefore,  this Comparability  Study was
initiated  to  compare the measurements  from  the  two  analytical
methods for similar adipose tissue  samples.   Comparability of the
measurements from the two methods  has  focused on eight individual
organochlorine pesticides and one  class  of chemicals  which have
been consistently detected in human adipose tissue  with the GC/ECD
method.   These compounds  are p,p'  DDT,  p,p' DDE, beta-BHC, hepta-
chlor epoxide,  oxychlordane,  trans-nonachlor,  hexachlorobenzene,
dieldrin, and PCBs.   For  the  GC/ECD  method, the hexachlorobenzene
measurements were reported as measured  (referred to as uncorrected
1The measurements are  reported in micrograms per gram lipid weight, unless
otherwise noted.

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hexachlorobenzene) and  corrected  for  recovery based on historical
recovery estimates (referred to as corrected hexachlorobenzene).

     The specific objectives of the Comparability  Study were to:
     •    Describe and compare the detection  level,  recovery,  and
          precision of the measurements using the  HRGC/MS  and
          GC/ECD analysis methods;
     •    Determine if there are significant  statistical differ-
          ences between the HRGC/MS and GC/ECD measurements on
          paired samples; and
     •    Describe the relationship between the HRGC/MS and GC/ECD
          measurements and evaluate its usefulness for  converting
          measurements from one method to the  other.

     These  objectives  have been met by  analyzing split  composite
adipose  tissue samples  collected in  1984 using both  analytical
procedures.   NHATS samples  from  690  individuals  were  composited
into 46  composite  tissue samples.  These  samples  were  split  into
two portions, or paired samples, one for analysis using the GC/ECD
method and  1 for analysis using  the  HRGC/MS  method.  One of the
paired samples for GC/ECD analysis was lost  during preparation,
leaving  45  paired  samples for the Comparability  Study.   Three of
the  45  composite  samples were larger  than  the  others  and are
called multisplit composite samples  Each of  these three composite
samples was split into 10 portions, five portions for GC/ECD  anal-
ysis and five for the HRGC/MS analysis.  Four of the five  portions
for each method  were  spiked with the target  analytes  in  order to
measure  recovery.   Additional quality  control  (QC)  and blank
samples  were prepared to  bring the total number of samples for
each method to  80.    This  report summarizes  the  measurements,
presents the results of the statistical analyses used to determine
the recovery for each  analytical  method,  and  compares  the perfor-
mance of the HRGC/MS  and GC/ECD procedures.

     The Comparability Study was  limited  to  a comparison of the
data from the 1984 paired samples, the multisplit samples, associ-
ated quality control  samples,  and summary data from other years.

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This study did not  attempt  to  analyze trends over time or provide
national or regional estimates.


     The rest of  this report describes  of  the sample collection,

analysis,  and the  results  and  conclusions of the  Comparability

Study.   The  report is  divided  into  the  following  chapters and

appendices:

     Chapter 2  Summary  and Conclusions;

     Chapter 3  Sample Collection, Preparation, and Analysis
                Procedures;

     Chapter 4  Description of the Data;

     Chapter 5  Overview of the  Statistical Analysis;

     Chapter 6  Analysis of Detection  Limits and Percent Recovery;

     Chapter 7  Relationship Between  the GC/ECD and HRGC/MS
                Methods;

     Chapter 8  Comparison  of GC/ECD  and HRGC/MS Measurements
                Across Years;

     Chapter 9  PCB Measurements;

     Chapter 10 Analysis of Precision  and Components of Variance;

     Chapter 11 References;

     Appendix A Summary  Data Tables;

     Appendix B Conversion  from  Standard Deviation of Log
                Transformed Data to Coefficient of Variation;

     Appendix C Recovery from Multisplit Samples;

     Appendix D Description of the Analytical  Procedures; and

     Appendix E Discussion  of the Variance  Components.


     Reports  that  describe  the NHATS  program,  the  sample collec-

tion procedures,  and the preparation  and analysis  procedures for

the tissue samples are listed in  the  references.

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                  2     SUMMARY  AND  CONCLUSIONS



     The NHATS Comparability Study compared the performance of the

HRGC/MS and GC/ECD analytical methods for measuring pesticides and

PCBs in  human adipose tissue  samples.   The results are  based on

data from  45 paired composite  FY84  NHATS samples  and  associated

multisplit  spiked,  quality control,  and blank  samples, and an

analysis of summary data from other years.  The conclusions,  based

on measurements of p,p'-DDT, p,p'-DDE,  beta-BHC,  dieldrin,  hepta-

chlor epoxide,  oxychlordane, trans-nonachlor,  hexachlorobenzene,

and PCBs, are:

   •     Average detection  limits for the  GC/ECD  method  were
        lower than  for  the HRGC/MS  method,  except  for  PCBs.

        For the  organochlorine pesticides, the average GC/ECD
        detection  limits  are  less than  .01 ug/g.  The corresponding
        HRGC/MS  detection limits range from .01 ug/g to .35 ug/g
        for different compounds.  For PCBs, the average GC/ECD
        detection  limit of  .43 ug/g is much greater than the
        average  for the HRGC/MS method of  .01 ug/g.

   •     Method Recovery  in lipid samples  was higher  for  the
        GC/ECD method  than for  the HRGC/MS  method  for  all
        compounds  except  beta-BHC,   for  which  the recovery
        estimates  were similar.   These general  results are
        consistent with the  ratio of the GC/ECD to HRGC/MS  measure-
        ments  in the paired  samples.  The method recovery depended
        on  the sample matrix.

        The GC/ECD recoveries ranged from 52% to 109%, except  for
        uncorrected hexachlorobenzene, with recovery from 33%  to
        61%.   The HRGC/MS recoveries in the lipid samples ranged
        from 26% to 62%,  except for beta-BHC with a recovery esti-
        mate of  99%.  The ratio of the GC/ECD to the HRGC/MS recov-
        ery ranged from 0.90 to 3.15 in the multisplit samples.

   •     The percentage  of  composite  samples with detected
        concentrations  was  greater  for  the  GC/ECD  method
        than  for  the  HRGC/MS  method.

        The primary compounds were detected in all GC/ECD samples.
        The percent of HRGC/MS samples with detected concentrations
        ranged from 82% to 98% for different compounds,  with the
        exception of dieldrin which was detected in 42%  of  the
        samples tested.   The percentage of samples with detected
        concentrations depends on the method detection limit,  the
        recovery and the  concentration in the samples.

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        In paired  samples,  the  measurements  using  the
        GC/ECD method were  greater  than those  using  the
        HRGC/MS method,  with  the ratio  of  the  6C/ECD  to
        HRGC/MS measurements  being  roughly constant.    For
        some   compounds,  the  ratio  depended on  the  concen-
        tration in  the  samples  or  differed among  batches.

        When compared using geometric means,  the  GC/ECD  measure-
        ments  were greater than the HRGC/MS measurements.   The
        ratio  of the GC/ECD to HRGC/MS measurement  ranged from  1.25
        to 3.88.  The ratio is statistically  significantly  greater
        than 1.0 for p,p'-DDT, p,p'-DDE,  heptachlor epoxide, trans-
        nonachlor, and corrected hexachlorobenzene.

        Differences in the ratio among batches were statistically
        significant for four compounds: p,p'-DDE, beta-BHC,
        oxychlordane, and trans-nonachlor.  Changes in the  ratio of
        the GC/ECD to HRGC/MS measurements  with changing concentra-
        tions  were statistically significant  for  three compounds:
        p,p'-DDT and beta-BHC, and hexachlorobenzene.  For  these
        compounds the ratio of the GC/ECD to  HRGC/MS measurements
        decreases as the concentration in the  samples increases.

        For PCBs,  the  HRGC/MS  measurements were  similar   to
        or lower  than  the  corresponding  GC/ECD measure-
        ments .

        The standard  deviation  of  the  HRGC/MS  and  GC/ECD
        measurements  increases  as  the  concentration  being
        measured increases  such that  the  coefficient  of
        variation of  the  measurements  is   constant.     GC/ECD
        measurements  have  coefficients  of  variation  less
        than   that  for  the  HRGC/MS  measurements.   For  the
        primary compounds  other than  dieldrin  the  GC/ECD
        measurements  were  more  precise  than  the HRGC/MS
        measurements.

        Approximate 95% prediction intervals  for  the GC/ECD and
        HRGC/MS measurements of Fraction 1  compounds2 (all primary
        compounds except dieldrin) are 22%  and 63%  respectively.
        There  are not enough measurements for Fraction 2 compounds
        (including dieldrin) to reliably compare  the measurement
        precision for the HRGC/MS and the GC/ECD  methods for
        Fraction 2 compounds.

        Given   the  likely  prediction errors,  the HRGC/MS
        averages from 1982,  adjusted  by the  GC/ECD  to
        HRGC/MS ratio  found  in  the FY84  samples,  were
        consistent with the  trends  in  the GC/ECD  averages
        in the  years adjacent  to  1982.
2The laboratory analysis procedures  separate  the target  compounds into two
portions, called Fraction 1 and Fraction 2.

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For five compounds, the adjusted HRGC/MS average was close
to the GC/ECD trend.  For one other compound,  the adjusted
HRGC/MS average was close to the GC/ECD trend after
removing two outliers from the HRGC/MS data.   For one
additional compound, the data and the ratio estimates were
too variable to attribute the observed differences in the
GC/ECD and HRGC/MS adjusted averages to the adjustment pro-
cedure.  For one compound,  no comparison was  possible
because there were no data from 1982.

For  comparison of  measurements  across methods  and
years,  the  measurements  can  be  adjusted  using
either  the  ratio  of the  GC/ECD  to  HRGC/MS  measure-
ments  or  by  correcting  for  recovery.

Using the ratios of the average GC/ECD and HRGC/MS measure-
ments in FY84 samples to convert from one method to the
other proved reasonable for the 1982 data.  However,  this
conversion method has limitations.  The relationship
between the GC/ECD and HRGC/MS measurements may change with
time, the laboratory used,  or the concentration levels in
the samples.  When recovery information, either average
recovery or within sample recovery estimates  based on
surrogate compounds, is available, correcting the HRGC/MS
data for recovery may be preferable to using  the ratios
from the Comparability Study.  A combination  of theoretical
arguments and data analysis suggests that the comparability
of the measurements from the two methods may  be improved by
correcting for recovery within each sample year and labora-
tory.  Evaluating the use of recovery estimates to adjust
the data across years was beyond the scope of this study.

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           SAMPLE  COLLECTION,   PREPARATION,  AND  ANALYSIS

                            PROCEDURES
     The  sample and data  processing steps  for  the Comparability

Study,  from  the initial tissue collection  to the final  analysis,

included:

      (I)  Collecting the individual adipose tissue samples;

      (2)  Preparing the sample design for the Comparability Study;

      (3)  Compositing the  individual specimens into composite
          adipose tissue samples;

      (4)  Preparing the quality control and blank samples;

      (5)  Extracting the lipid from the composite adipose tissue
          samples;

      (6)  Dividing each lipid sample into a pair of samples,  one
          to be analyzed using the GC/ECD method and one to be
          analyzed using the HRGC/MS method;

      (7)  Measuring the concentration of target and surrogate
          compounds using  the GC/ECD and HRGC/MS procedures;

      (8)  Entering the concentration measurements into a computer
          file  for statistical analysis; and

      (9)  Analyzing the data to determine the precision and
          recovery of the  GC/ECD and HRGC/MS methods and compare
          the measurements in the paired composite samples.


     Battelle Columbus Division participated  in the sample collec-
tion design  and prepared the sample design for the Comparability
Study.  Midwest Research  Institute (MRI)  collected and stored the

tissue  samples  and  prepared  the composite samples and  lipid

extracts.  The  Institute of Rural Environmental Health at  Colorado

State University  (CSU)  performed the HRGC/MS and GC/ECD  analysis

on the  lipid extract  samples.   Westat  prepared the data  files and

performed the statistical analysis.


     This chapter reviews  the Comparability Study specimen collec-
tion  procedures;  the  preparation  of  the  composite,  blank,  and

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spiked samples; and the analytical procedures for the HRGC/MS and
GC/ECD methods.  The data are summarized  in  chapter 4.  Chapters 5
through 10 present  the  statistical analysis and results.

3.1  Collection  and  Storage  of the  Human  Adipose Tissue
     Specimens

     As part  of the  ongoing  NHATS  program,  individual adipose
tissue specimens are  collected on a yearly basis  from volunteer
hospitals and medical examiners in selected cities throughout the
continental United  States.   Specimens from  the  fiscal  year 1984
collection were used to compare the GC/ECD and HRGC/MS analytical
procedures.

     The cities in the NHATS survey were selected using probabil-
ity  sampling.     Within  the  selected  cities,  organizations
(hospitals and medical examiners)  were  recruited to participate in
the NHATS program.   Each participating  organization was assigned a
quota of individual tissue specimens to be collected over the year
from surgery patients and cadavers.  While the non-random methods
of  selecting  participating  organizations  and of obtaining tissue
specimens within the  hospitals may affect  the interpretation of
national  averages  and comparisons  between different  years,  the
specimen  collection procedures  do  not affect  the  Comparability
Study results.

     The individual tissue specimens were sent to MRI for storage
until  further processing was  performed.    The  composite samples
were prepared in  the  beginning of  1986.   GC/ECD  analyses were
performed in  the first half  of 1986.   HRGC/MS samples in batch 1
were analyzed  in April  1987; the remaining batches were analyzed
at  the end of 1987  and the first half of 1988.
                                10

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3.2  Sample  Design  for  the  Comparability  Study

     The  sample design  specified which  individual FY84  adipose
tissue  specimens  were to be  combined into composite  samples and
the order  of preparation and  analysis  of the  composites,  method
blanks, and quality control  samples.

     The sample design specified four types of samples:
     •    Single-split composite samples,  composite samples which
          were split into two portions,  one  for GC/ECD  analysis
          and one for HRGC/MS analysis;
     •    Multisplit composite samples,  composite  samples which
          were split into 10 portions, five portions  for GC/ECD
          analysis (four of which were spiked)  and five for the
          HRGC/MS analysis (four of which  were spiked);
     •    Quality control samples to monitor  recovery and preci-
          sion; and
     •    Blank samples to identify and correct for possible
          contamination of the samples.

The sample design  specified 43 single-split  composites, 3  multi-
split composites,  10 quality control samples,  and 10 blank  samples
for each method.

     The criteria  for choosing which specimens to composite were
based on demographic  factors:  geographic  region,  age group,  race,
and sex.   Composites were  constructed from  individual  specimens
within the same age group and census division, allowing estimation
of mean concentrations for census  division and age group.   Within
a census division and age group,  specimens were combined in  a way
that permitted estimation  of  differences  between  sex  and race
groups.

     Composite samples were created in order to reduce  the  analyt-
ical costs and to achieve the  desired sensitivity of the  analyti-
cal methods.   The  sensitivity  of  the HRGC/MS  and GC/ECD  methods
depends on  the size  of the  sample being  analyzed.  Individual
                                11

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NHATS  tissue specimens  are not usually large enough  to provide
adequate   sensitivity  for  both  HRGC/MS  and  GC/ECD  analyses.
Therefore, individual specimens were composited to create a  larger
sample  for  chemical analysis.    The  HRGC/MS  method  required a
20-gram sample  while the  GC/ECD  method required  a  5-gram  tissue
sample.   Thus the  target wet weight of the single-split composite
samples was  25  grams.   Multisplit  composite  samples required  125
grams of tissue.

     The  samples were grouped into 10  batches for analysis.  Each
batch was  designed to have one quality control (QC)  sample  spiked
with a known concentration of the  target compounds,  and one blank
sample.   The multisplit  composites were analyzed in three differ-
ent batches.  For  each analytical method, the unspiked portion  and
two  of the  spiked  portions  were analyzed  in one batch.    The
remaining two  spiked  portions  were  analyzed in  two  separate
batches.

     There were two  deviations from the  sample design.  First,  one
sample  for  the GC/ECD  analysis  was  lost  during  preparation,
leaving 45 paired  samples  for the Comparability Study.3  Second,
problems  in  the preparation of two composite samples from batch 8
and  one  composite sample  from  batch 9 resulted  in the  need to
reconstruct  these  composites.   When these  samples were   recon-
structed  from  stored tissue  specimens,  an additional  blank  was
prepared  for batches 8 and 9.  As a result,  batches 8  and  9 each
had two blank samples.

     The  term "paired"  samples is  used in this report to refer to
the  single-split   composite  samples  and the  unspiked portions of
the multisplit composite samples.  The paired samples were used to
compare the  GC/ECD and HRGC/MS analysis methods.
3A 47th composite was  prepared to replace the lost GC/ECD  sample for making
national estimates  and evaluating trends over time.  This sample was not part
of the  comparability study.  In all, 697  individual specimens were combined
into 47 composite samples.

                                12

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     A  total  of 80  samples  was  analyzed  using each  method.
However, the extracts from the unspiked multisplit GC/ECD  samples
were analyzed three times, once in each batch containing a  spiked
multisplit sample.  Therefore,  there are  2  additional  analyses  for
each of the 3  unspiked multisplit  composites, giving  86 total
analyses for the  GC/ECD method.  Table 1 summarizes the number of
FY84 samples of different types.

3 . 3  Sample  Preparation

     Sample preparation is discussed below,  including  compositing
of the  individual tissue specimens, extracting the lipid from the
tissue for analysis,  and preparing  blank and spiked QC  samples.  A
more complete  description of  the  sample  processing procedures is
presented in Appendix D.

     3.3.1  Preparing  the  composite  samples  and  batches

     The sample design specified which  individual tissue specimens
were to be  combined  into  each composite  sample  and which compos-
ites were  in  each batch.   The tissue  weight contributed by each
individual  specimen  to  a composite  was  made  as consistent  as
possible.   A method blank  sample and all  composites within a batch
were prepared at the  same time.

     The target  compounds to  be measured are associated with the
lipid portion of  the  composite adipose tissue samples.  The first
processing step  extracts the  lipid from the  tissue.   As  part of
the extraction  step,  the  weight  of the lipid as  a percentage of
the wet weight  of the sample  is  determined.  The lipid extracted
from  the   composite   sample  was  split  into two  portions,  one
reserved  for  the GC/ECD  analysis and another  reserved  for  the
HRGC/MS analysis.
                                13

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Table I,   Number  of  Samples  for  HRGC/MS and GC/ECD Chemical
           Analysis by Sample Type  and Batch

Sample Type
Design Composite Samples
Single-split Paired
Unspiked Multisplit Paired
Paired Samples Subtotal
Unpaired Composites
Design Samples Subtotal
Spiked Multisplit Samples
Low level spike
Mid level spike
High level spike
Blank and QC Samples
QC samples
Blank
Regenerated blanks
HRGC/MS Samples Total
GC/ECD Extract Analyses
GC/ECD Samples and
Extract Analyses Total

Total

42
3
45
1
46

4
4
4

10
10
2
80
6
86
Batch
123456

253553
11 1

7 8 9 10

5554

1


21 1
2
2 1

111111
111111

888888
1 11
898998



1 1
1

1111
1111
1 1
8996
111
9 10 10 6
Note.  Blank entries indicate that no samples with the indicated
sample type were analyzed in that batch.
                                 14

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     The  three multisplit samples  were prepared  following  the
procedure  for the single-split  samples,  with the exception  that
the multisplit  samples  had a total wet weight of 125  grams.   One
25  gram portion  of  each multisplit  composite was  removed  and
handled  identically  to the  single-split  samples.   The  remaining
lipid was  sent  to CSU for .spiking  and  splitting  into  eight  spiked
multisplit samples, four  for  GC/ECD  analysis  and four for HRGC/MS
analysis.  The  spiking levels for the multisplit  composite samples
are shown  in  Table 2.   Figure 1  graphically  summarizes the  sample
processing steps.

     3.3.2  Method  blanks  samples

     A procedural blank sample,  consisting of 100 milliliters  of
methylene  chloride,  was prepared for  each batch.   The blank  was
passed through  all  sample processing  steps  used for  the  single-
split samples.   The measurements  from these  blanks  were used  to
determine  if  the  laboratory background contributed to the  target
analytes in the samples.

     3.3.3  Quality  control  samples

     The QC samples were  prepared  with known  quantities  of  target
compounds.    The  QC  samples are  different  for  the  GC/ECD  and
HRGC/MS  analyses.   Reference porcine  fat samples  were  obtained
from  EPA's Las Vegas  Laboratory  (EPA/EMSL-LV)  for  the  GC/ECD
analyses.   The porcine samples  in batches 1  to  3  had  different
spiking  levels  than  the  samples in batches  4 through  10.   The
concentration levels  in these two groups  of  porcine  fat  samples
are provided  in Table 3.   Because the HRGC/MS method was  new  to
CSU and  there was  a  limited source of  standard reference  adipose
material, a sample of  spiked dichloromethane4  was  prepared for  each
batch analyzed using  the HRGC/MS  method.    The  dichloromethane
samples  were  spiked  with 10 nanograms of  all HRGC/MS  target
compounds.
4Dichloromethane is a  synonym for methylene chloride.

                                15

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Table 2.   Spiking Levels for the Multisplit Composite Samples
Compound
Dieldrin
beta-BHC
Hexachlorobenzene
Heptachlor Epoxide
trans-Nonachlor
Oxychlordane
p , p ' -DDE
Low
.20
.10
.05
.10
.10
.10
1.00
Spikina levels
ug/g wet weight
Mid
.40
.20
.08
.20
.20
.15
3.00
High
.60
.40
.11
.30
.30
.20
5.00
                              16

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     Physical Sample  Diagram

     Individual Adipose Tissue Samples
               Processing Steps

         Receive Adipose Samples at  MRI
             Composite

              Sample
       Spiked
Unspiked
                     I         I
                   GC/ECD   HRGC/MS
Create Composite Samples  in Batches
  25 gm. for single-,  125 gm for
       multisplit Composites

                 I
 Extract the Lipid  from the Tissue

                 I
     Calculate Percent Lipid

                 I
 Divide the Lipid into  2 portions,
 one for Spiked ,  one for Unspiked
       Multisplit1 Samples


          Divide Lipid for
     HRGC/MS and GC/ECD Samples
                       Paired

                      Composite

                       Samples
          Send Samples from MRI to  CSU

                        I

         Proceed with chemical Analysis
                     Send the Lipid from MRI to CSU

                                    I
                            Spike the  Lipid

                                    I

3C
•








/BCD HRG<

Spiked
Multi-
Split
Composite

Samples

HRGC/MS and GC/ECD Samples
:/MS 1
"I Split the Lipid into 4 Spiked Samples
1
>K
Associate the Samples with Batches
1
1
4
Proceed with chemical Analysis
Figure 1.  Processing steps for the  sample  preparation and analysis
           of composite samples.
                                  17

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Table 3.     Concentrations  of Target  Compounds  in GC/ECD Porcine
            Fat  QC Samples,  in ug/g wet  weight
Compound
Hexachlorobenzene
beta-BHC
Mirex
Oxychlordane
Heptachlor Epoxide
trans-Nonachlor
Dieldrin
p,p'-DDE
p,p'-DDT
PCB (Aroclor 1254)
Batches 1-3
.070
.120
.065
.060
.046
.080
.150
1.620
.230
-
Batches 4-10
.049
.300
.129
.112
.075
.119
.040
1.860
.175
1.000
                                 18

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3 . 4  Chemical  Analysis  Methods


     Although  the specifics of  the  chemical analysis for  the two

analysis methods differ, the general steps followed are:

     (1)  Spiking with  surrogate standards for estimating
          recovery;

     (2)  Removing the  lipid and leaving the target compounds  in a
          solvent solution;

     (3)  Separating the target  compounds into two fractions to be
          analyzed separately;

     (4)  Spiking with  standard  solutions to aid in identification
          and  quantitation and for estimating recovery (for the
          HRGC/MS method only) ;  and

     (5)  Identification and quantification of the target
          compounds .


     Once the  amounts  of  the target compounds in each sample  have

been determined,  the  concentrations can be calculated by  express-
ing the amounts  as a  proportion of the original sample  wet weight

or lipid weight.   The  concentration in micrograms per gram (ug/g)

wet weight is :


                 . .    Weight of the Target Compound (ug)        . ,. . .
    *wet weight 1)


where  X is  the  measured concentration.   The concentration  in

micrograms per gram (ug/g)  lipid weight is :

                  .   _  Weight of the Target  Compound  (uq)
    XLiPid weight  (ug/g)  -  (Wet Weight (g) ) (percent Lipid/100)
                      _ Weight of the Target Compound (ug)
                                 Lipid Weight (g)              (    '

Unless  otherwise stated,  all  concentrations in  this  report  are
expressed on a lipid basis.
                                 19

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     The  following sections discuss the  analysis procedures,
performed at  CSU,  for the  GC/ECD  and HRGC/MS methods.   Specifics
of each method are presented  in Appendix D.

     3.4.1  GC/ECD analysis  procedures

     For the  FY84  samples,  the GC/ECD method was used to quantify
the amount of 20 target compounds.5  The modified MOG procedure was
used for preparation of the samples  submitted  for GC/ECD analysis.
The lipid  extracts for the GC/ECD analysis were spiked  with one
microgram  of aldrin.    The  bulk  lipid  material was removed  by
partitioning  the  lipid sample  between hexane  and  acetonitrile.
The target  compounds  were separated into  two  fractions  using a
florisil chromatography column.  The  target  compounds in the final
extract were  identified and quantified using the packed column gas
chromatograph (GC)  with electron capture detection (ECD) .

     Concentrations of  compounds were quantitated  based  on the
area of  the  peak representing the target compound and  the aldrin
peak.   The  recovery of aldrin was calculated as a quality assur-
ance check on the  entire process.   Based  on  historical information
on the recovery at the partitioning step, the concentration  of
several compounds  were  reported  as measured  (referred to as uncor-
rected)  and  corrected for recovery.   Concentrations for hexa-
chlorobenzene and  mirex were computed on both a corrected and an
uncorrected basis.  Concentrations for p,p'  DDT  were  reported on a
corrected and uncorrected basis  for  the porcine  fat  tissue samples
in batches 4 through 10.6

     Total  PCB  concentrations  were reported  on  the  following
interval scale  using letters to designate  each interval:  V = not
5£ighteen individual chemicals, one class  of chemicals  (PCBs),  and aldrin
which was used to estimate  recovery.
6 The values reported in the space provided on the form for hexachlorobenzene,
mirex and p,p'  DDT  are  uncorrected unless the uncorrected values for these
compounds are written in at the end of  the list, in which case the entry  for
these compounds is the corrected value.

                                 20

-------
detected,  W =  .33  to 1  ug/g wet  weight,  Y  = 1 to  3 ug/g  wet
weight, and Z = greater than 3 ug/g  wet weight.

     The  following  information  was reported  for each  composite
sample and target compound:
     •    Compound name and code;
     •    "<" if the amount detected was  less than the level of
          quantification  (LOQ) but greater than the level of
          detection (LOD),  in which case  the amount reported was
          the LOQ; and
     •    The concentration reported to 0.01 ug/g wet weight or a
          letter code for the interval in which the PCB  concentra-
          tion lies.

     3.4.2  HRGC/MS  analysis  procedures

     In order to expand the  list of chemicals  that could be moni-
tored by NHATS, the standard  NHATS  method for  detection and quan-
tification of chemical compounds was changed  from GC/ECD method to
the HRGC/MS method.   For the  FY84  samples, the HRGC/MS  method  was
used to  quantify the  amount  of  57  target  compounds.    The sample
was spiked  with  known  amounts of the eleven surrogate  compounds.
Table 4 identifies the surrogate compounds and the level added to
each sample.   Gel  permeation  chromatography   (GPC)  was used  to
separate  target  analytes  from  the  lipid  material.    The target
compounds  were  separated  into  two fractions  (referred  to  as
Fraction 1 and Fraction 2)  using a Florisil  chromatography  cleanup
similar to the MOG method (the Fraction 2 extract  was not analyzed
in batches  4  through  10) .   Known  amounts of  the three internal
standards,   anthracene-dlO,  naphthalene-d8,   and   benzo-(a)-
anthracene-d!2,  were added to each of the final extracts, prior to
analysis by high  resolution  gas chromatography/ mass spectrometry.

     The identification and quantification  was based on relative
retention times  and response factors established during calibra-
tion.   Specific compounds  were identified by  matching characteris-
                                21

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Table 4.     Spiking Levels of Surrogate Compounds in HRGC/MS
            Samples
      Compound
 Spike  Level
	ug	
      Chrysene-d12

      1,2,4  -Trichlorobenzene-d3

      13C6-1,2,4,5 - Tetrachlorobenzene

      13C6- Hexachlorobenzene

      13C6- 4 - Chlorobiphenyl

      13C12-3,3 '4,4 - Tetrachlorobiphenyl

      13C12-3,3 ,3,3 ,5,5 ,6,6 -
                 Octachlorobiphenyl

      13C12 Decachlorobiphenyl

      Diethyl  Phthalate  - 3,4,5,6  -d4

      Di-N-Butyl  Phthalate  - 3,4,5,6 -d4

      Butyl  Benzyl  Phthalate -  3,4,5,6  -d4
      2

      2

      2

      2

      2

      5


      8


     10

      2

      2

      2
                                 22

-------
tic  spectra with reference material.   One of  the  three internal
standards was  designated as the appropriate internal standard for
each target analyte for  purposes of identification and quantifica-
tion.

     In  order  for an  analyte  to be identified,  the following four
criteria had to be satisfied:
     (1)  The  primary and secondary masses had to achieve their
          maximum values within a. specified time span;
     (2)  The  retention  time of the primary and secondary mass
          fragments relative to the designated internal standard
          had  to be within 10 seconds of the known relative  reten-
          tion time of the analyte;
     (3)  The  relative abundances of the primary and secondary
          masses all had to be within 20% of the relative abun-
          dances in the  reference spectrum of the analyte;
     (4)  The  abundances of the primary and secondary masses all
          had  to exceed  2.5 times the background signal to noise
          ratio.
     Concentrations of  analytes  were  computed from the calculated
amounts, the weight of the composite sample, and the percent lipid
in the  composite  sample.   No concentrations were  corrected for
recovery.   The recoveries of the surrogate compounds  were calcu-
lated as a check  on method  performance.   A data  qualifier was
determined  based  on  the relative  magnitude  of  the  noise,  peak
signal,  and the quantity  of each target compound  in the lowest
calibration standard.

     The  following  equation  was used  to calculate the  lipid
adjusted concentration of each target  compound in each  sample:

                      As   Ijs    i       i
                 X = 1000  Ais  RRF  Lipid Wt                  (3'3)

where:
     X = Lipid adjusted concentration  of the target  compound  (ug/g);
                                23

-------
     As  = Area of the primary characteristic ion response for the
          compound being quantified;

     Ij_s = Amount of internal standard added to the extract (ng) ;

     Aj_s = Area of the primary characteristic ion response for the
          corresponding internal standard;

     RRF = Relative response factor, determined during calibration
          of the instrument; and

     Lipid Wt = Weight of the lipid in the adipose tissue  sample
          analyzed  (g), calculated as the product of the wet
          weight and the percent lipid divided by 100.

This equation is used  later in the development of the  statistical
model for the data.
     The following information was  recorded for  each  sample and

target analyte:

     •    Compound name;

     •    Data qualifier (either not-detected "ND", trace "TR",  or
          positively quantified "PQ");

     •    The calculated LOD for trace and not detected measure-
          ments;

     •    The amount of each compound in nanograms (ng.)  for
          positively quantified and trace measurements;  and

     •    Remarks to explain exceptional circumstances.


3.5  Data  File  Preparation


     The measurements reported by the laboratory were entered into

computer files at  EPA's National  Computer Center  (NCC)  for use in

the statistical  analysis.   After data  entry,  the data were veri-

fied  against the  original  reports and  consistency  checks were

performed to  catch possible errors  in  the data  entry or unusual

observations.
                                24

-------
                  4    DESCRIPTION  OF  THE DATA



     This  chapter describes  the general  characteristics  of the

HRGC/MS and GC/ECD concentration measurements in the FY84 samples.

Numerical summaries are shown in Appendix A.


4.1  Organization of the  Data


     Measurements were made on four different  types  of  samples:

      (1)  Method Blanks;

      (2)  QC standards: porcine fat samples in the GC/ECD analy-
          sis, and spiked dichloromethane samples in the HRGC/MS
          analysis;

      (3)  Spiked multisplit composite adipose tissue samples (at
          three different spiking levels) and the associated
          extract samples (for the GC/ECD analysis only); and

      (4)  Paired composite adipose tissue samples.


     The compounds measured can be divided  into two  categories:

      (1)  Target compounds,  those compounds of interest in the
          NHATS program.  These compounds have different levels in
          different samples; and

      (2)  Surrogate compounds used for quality control  and esti-
          mating recovery.  These compounds have similar levels in
          all samples.


     The  different  combinations of  compounds  and sample types
define  groups  of measurements  which are  directly  comparable.

Figure 2 graphically  portrays  these groups of comparable measure-
ments.   Tables  A-l  through  A-18  in  Appendix  A  summarize  the

HRGC/MS  and  GC/ECD  measurements  within  each  group  using  the

percent  detected, median,  and,  for  the  positively  quantitated

observations,  the mean, median and extremes.
                                25

-------
                                             Type of  Compounds
 H

 0)
 r-\


 I
                               Target Compounds
                          Primary         Secondary
                         8 Compounds    11 GC/ECD Compound
                                      49 HRGC/MS Compounds
                                                          Surrogate
                                                          Compounds

                                                       1 GC/ECD Compound
                                                      11 HRGC/MS Compounds
    Method Blanks
    12 Samples
    QC Standards
    10 Samples
                          Porcine Fat  (GC/ECD)

                         Spiked Dichloromethane
                                (HRGC/MS)
Spiked Multisplit
Composite Samples
12 Samples
    Paired Composite
    Samples
    45 Samples
                             Low
                                 Mid
                                 High
                      42  Single-split Composite
                                Samples
                         3 Unspiked Multisplit
                      	Composite Samples	
Surrogate
Compounds
 (HRGC/MS)

 Aldrin
 (GC/ECD)
      3 GC/ECD measurements were made on the final extract of
       the unspiked multisplit paired composite samples
Figure 2.   Subsets of the data with comparable measurements  by
             sample type  and type of compound.
                                     26

-------
     The target compounds can be further  divided into two groups:
     •    Primary compounds: compounds which are positively
          quantified in numerous samples  using both the GC/ECD and
          HRGC/MS methods; and
     •    Secondary compounds which were  either measured using
          only one method or which were not  positively quantified
          using both methods.

     Only nine compounds  were  positively  quantified  in the  paired
samples using both the  GC/ECD and HRGC/MS  methods.   These  com-
pounds  are:  p,p'-DDT,  p,p'-DDE,  beta-BHC,  dieldrin,  heptachlor
epoxide,  oxychlordane,  trans-nonachlor,  hexachlorobenzene and
PCBs.   These compounds are  referred  to  as  the primary compounds
for  the  Comparability Study.   In all cases except dieldrin and
PCBs, the number  of positively quantified  observations which can
be used to compare  the two measurement methods is 37 or  greater.
Dieldrin,  in fraction 2,  was only  analyzed using  the  HRGC/MS
method  in  batches  1,  2,  and 3,  and therefore  has  few HRGC/MS
measurements.    Only  five  samples  had positively  quantified
dieldrin concentrations  reported for  both the HRGC/MS and  GC/ECD
methods.  PCB measurements from the GC/ECD method  were  reported  on
an interval scale,  as described in Section 3.4.1,  rather than  as a
finite continuous value.   The PCB results are discussed separately
in Chapter 9.  Because the  results for dieldrin were limited, the
comparison of the two analytical methods  is  based  primarily  on the
measurements  of:  p,p'-DDT; p,p'-DDE; beta-BHC; heptachlor  epoxide;
oxychlordane; trans-nonachlor;  hexachlorobenzene; and PCBs.

4 .2  Preliminary  Review  of  the  Data

     The initial step  in any data analysis  is called  exploratory
data analysis.   This step usually involves plotting the  data  in
various ways to help  identify important characteristics of the
measurements.

     The percent lipid in the composite   samples ranged from  41.5
to 99.3 percent,  with an average of 77 percent, providing  adequate

                                27

-------
lipid  in  each composite  for  measuring the  concentrations of the
target compounds.

     Initial plots of  the data suggested that the distribution of
the data  was skewed and  that  the standard  deviation  of the mea-
surements increased as the magnitude of the  measurement  increased.
There  were  no  obvious outliers or  extremely unusual observations
which might make subsequent preliminary work suspect.  Plots using
the log  of the measurements  were consistent with  the assumption
that the data can be described by a lognormal distribution.  Plots
of  the HRGC/MS  surrogate  compound measurements suggested that
there were systematic differences  between batches.

     Little  laboratory background contamination  was  noted.  There
were no  quantifiable measurements  in  method blank  samples using
the GC/ECD method.   Positively  quantified measurements  in the
method blank samples were observed for  4  of 50 compounds7 using the
HRGC/MS method.   With the exception of  Di-n-Butyl  Phthalate, the
measured  quantities  were close to  the level of  detection.   Only
one measurement for  a primary compound,  p,p'-DDE,  was positively
quantified.

     From the  tables  in Appendix A, the  coefficient of variation
can be calculated  using the following equation:

         **< •   x.   ^     •  x. •    /   \   standard deviation       , „ _
      coefficient  of  variation  (cv)  = 	       (4.1)
                                             mean

     The  coefficient of  variation is one measure of the variabil-
ity of the  data.   For  many chemical measurements, the coefficient
of  variation provides a  stable  measure  of variability across  a
range of concentrations.  For samples with the same  actual concen-
tration,  such as spiked multisplit samples from the  same composite
or QC samples, the coefficient of variation  measures the precision
of  the analytical technique.   For the paired  composite samples,
7p,p'-DDE, 1,2,4-trichlorobenzene, di-n-butyl  phthalate,  and di-n-octyl
phthalate

                                28

-------
the  coefficient  of variation measures  the variability resulting
from both  differences between  composite  samples and measurement
variation.

     The coefficient  of variation  of the  positively quantified
measurements was  calculated  for the compounds and groups of data
listed in Appendix A.  It was found to be  roughly similar for all
compounds  within the  two  groups:  paired  samples and  all  other
samples.   The  average  coefficient of  variation across  target
compounds measured in paired composite samples was 68% for GC/ECD
measurements and  72%  for HRGC/MS measurements.   For the remaining
samples, the average coefficient of  variation  across all compounds
was  11%  for the  GC/ECD  method  and 29% for  the HRGC/MS method,
suggesting that  the laboratory measurement error  is  greater for
the HRGC/MS method  than  for  the GC/ECD method.  The  coefficients
of variation for  measurements on the  paired samples  are substan-
tially  greater  than  that  for  the  spiked  QC  samples due  to the
additional  variation  in  the   contaminant concentrations  among
composites.   Because the  reported  GC/ECD  concentrations  were
rounded to  0.01  ug/g wet weight, the  variance estimates  for the
GC/ECD  method  will  tend  to underestimate  the true  variance.
However, the effect of rounding on  the results is expected to be
small.

     The distribution of a set  of concentration  values which have
a coefficient  of variation  greater than 50% is  often skewed to the
right.    Investigation of  the   data confirms  that,  as  a  general
rule, the  measurements on the  paired samples are skewed  to the
right.    Measurements  within a  group of  samples  with  the  same
actual  concentrations,  such as the QC  samples, have  a  smaller
coefficient  of  variation  and  a distribution  which is  roughly
symmetric.

     Figures 3 and 4  show histograms of the p,p'-DDE  measurements
on paired samples using the HRGC/MS  and GC/ECD  methods.  Note that
the plots have different concentration scales and that the GC/ECD
                                29

-------
measurements  are  larger  than  the  HRGC/MS  measurements.    Both
figures show a skewed distribution.  Measurements on the composite
samples estimate the average  of  the  concentrations in the individ-
ual samples which were composited.  Since averaging decreases the
coefficient  of  variation,   measurements in  individual  tissues
specimens can be  expected to be more skewed than  those from the
composites.
                                30

-------
             10 +
             8 -•
          o


          §   6
          cr
          0)
             4 -•
             2 ••
                 0.00              4.50               9.00


                       p,p'-DDE BCD Concentration (ppm)



Figure  3.   Histogram of GC/ECD  measurements of p,p'-DDE on paired

            composite adipose tissue samples.
            15 r
Frequency
_*
O1 O
i • i














I I
                0.00                   1.25


                       p,p'-DDE MS Concentration  (ppm)
2.50
Figure 4.   Histogram of HRGC/MS  measurements of p,p'-DDE on paired

            composite adipose tissue samples.
                                  31

-------
          5    OVERVIEW OF  THE  STATISTICAL  ANALYSIS


     Different subsets  of  the data were  used  to achieve each  of
the  study objectives.   The  precision  of the  measurements was
determined from the QC samples,  the spiked multisplit  samples, and
the  surrogate  compounds which  were  common to all  samples.  The
recovery  was estimated  from the QC samples and the spiked  multi-
split  samples.    The HRGC/MS  and GC/ECD methods  were  compared
directly  using  the  primary  compounds  in  the  paired  samples.
Figure 5  summarizes graphically the different subsets of the data
and  how  the data  in  each  subset were used  in the Comparability
Study.

     In order  to achieve the objectives,  some common procedures
for analyzing the  data were established and a model for the data,
a mathematical description  of the structure  and important rela-
tionships in the data, was developed.  This chapter discusses the
model for the data  and assumptions behind the analysis.

5.1   Correction  for  Blanks

     The  method  blanks  consist  of solvent  samples  that  are pro-
cessed as single-split composite samples.  If there is background
contamination  during the sample processing,  this  contamination
will be observed  in the  blank  samples.  Assuming  the background  is
the same  for all samples within a batch,  the measured quantity  in
the  blank can be  subtracted  from the  measured quantity  in the
sample to correct  for the contamination.   Note however,  when the
quantity in the blank is below the detection limit, the appropri-
ate contamination correction is  some  unknown  concentration between
zero and the lowest quantifiable quantity.
                                33

-------
Surrogate
1 GC/ECD Compound
11 HRGC/MS Compounds
Precision
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     For  the  GC/ECD blank samples,  no  compounds were positively
quantified.   In the  HRGC/MS  blank  samples,  four compounds  were
positively  quantified  (p,p'-DDE,   1,2,4-Trichlorobenzene,  Di-n-
Butyl  Phthalate,  and  Di-n-Octyl Phthalate)  of  which  only  one,
p,p'-DDE,  is  used  in  the comparability analysis.   The  compound
p,p'-DDE was detected in only  1 of  12 blank samples,  and  then  at  a
level only 10% greater than the limit of detection.

     It was decided to use all measurements without a blank  cor-
rection rather  than to correct a few measurements while  ignoring
the unknown correction  for most of the measurements.  This deci-
sion is expected to have little effect on the  statistical analy-
sis .

5.2    Outliers  and the  Use   of  Remarks

     Outliers are observations which appear to be unusual  compared
to the bulk of  observations.   A preliminary analysis of  the  data
indicated  that  there  were no  particularly  unusual observations
which might significantly affect the statistical analyses.   As  a
result, no formal outlier analysis  was  performed before  analyzing
the data.   However,  as  different  analyses  are discussed in the
report, any values  which  might  affect the  statistical conclusions
are discussed.

     The  comment fields on  the HRGC/MS  data  sheets  sometimes
contained information  on  the  quality of the reported quantities.
If a comment was supplied for  a positively  quantified measurement,
the comment  generally indicated  that the  actual  quantity  was
judged to be either I) equal to or  possibly  less  than or  2) equal
to or possibly  greater  than the reported measurement.   Since the
size of any bias in these measurements  could  not be assessed,  they
were used as reported.
                                35

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5.3   A  Model  for  the  Data

     The model  for the  data is a mathematical description  of  the
relationships within the data which result from the process which
generated the data.   Statistical procedures  are used to  estimate
parameters in the model and to check if the assumptions  behind  the
model are consistent with the data.  The model is based on  1)  the
sample design,  i.e., the way in which the data are collected;  2)
the objectives  of the  study;  and 3)  the  characteristics  of  the
data.   The  statistical  analysis  procedures are  chosen  to  be
consistent with  the model.

     The model used to describe the NHATS data is  developed  below.
This particular  model was selected only after data analysis  deter-
mined that the model  was consistent with the data.

     The model  assumes the  measurement errors have  a lognormal
distribution, and therefore that  the standard deviation  of  the
measurement error is  proportional to the magnitude of  the  measured
concentration.   Environmental  measurements and measurements made
on a scale from zero to infinity, such as chemical concentrations,
often have a skewed distribution  such as a lognormal  distribution.

     A log  transformation  converts  data which have  a lognormal
distribution to data with a  normal distribution.  While the vari-
ance or standard deviation of the original measurements  depends on
the concentration being measured,  the variance of the  log  trans-
formed measurements is  constant.   Since  many standard statistical
techniques  are  based on  the assumption  that the  errors  have  a
normal distribution  with constant variance,  the  log transformed
data are  often  easier  to  handle statistically than  data  in  the
original scale.
                                36

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     The equation for quantifying the HRGC/MS  concentration X can
be used as a starting point to develop the model for the data8:

                      A    I'     1       1
                 X = 1000  Ais  RRF  Lipid Wt                  (5>1)


     Using  the  log  of the measured  concentration provides some
insight  into an  appropriate  model  for  the log transformed data.
The equation for the log of the measurements is:
       ln(X)  = in      + ln      + In      + In.               (5.2)
where:
       / As \
     Inl      I depends on the actual concentration of the measured
        IQQQ
              compound.   This term might  also be written as:
              where f (C)  is  a function relating the  actual  concen-
              tration to  the measured concentration  and e is the
              measurement error not associated with  other terms .

             depends on how  much internal standard was added to the
              sample and  how much was detected by the equipment .
              This  term can  be written as 8^ .

     Infrrrj depends on the  calibration of the equipment.   This

              term  can be written as 6j .

     ln[r~: — .  ,    1 depends on the characteristics of  the sample, in
              particular  the measurements of wet weight and percent
              lipid.   This term can be written as 8S.
8This equation is the same as equation (3.3)


                                 37

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     After  making  the substitutions  above,  the model  for  one
HRGC/MS measurement might  be  written:

                ln(X)  = f (C)  + 8j -f 8S + Si + e                (5.4)

     The  equivalent  model  in  the  original  concentration  units
would be:

                   X = ef(C)e(63+ 5s + Si+ «                    (5.5)

     Of these two models, equation  (5.4), based on the  log  of  the
measurements, is  easier to  fit  using standard statistical  tech-
niques  than equation  (5.5)  because  1)  the  distribution of  the
residuals  is expected  to be closer  to normal;  2)  the  standard
deviation of the  residuals  are  expected  to be roughly  constant;
and 3)  the terms are additive.

     Equation   (5.4)  provides  an  incomplete model  in  that  it
ignores some characteristics of the data and  the  function f(C)  is
not  defined.   Under ideal  situations the  HRGC/MS  method  would
measure the  actual  concentration  in the sample, not  counting some
variation in the measurements around the actual value.   Because of
losses in the chemical preparation steps,  the measured quantity is
usually less than the actual quantity of a compound in the sample.
Thus, the model  for the measured concentration X might be:

                           X =  CR                           (5.6)

where R is the percent recovery  expressed as a fraction.

     For  spiked samples,  an additional quantity  of the  compound
being measured is added to the sample, resulting in an increase in
concentration of the compound of S.   With this addition,  the model
for the log of the data might be:

                   ln(X) = ln(C+S) + ln(R).                   (5.7)
                                38

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     The  recovery may  be  different  for  the HRGC/MS  and GC/ECD
measurements due  to  differences in the sample preparation.   There
is also  the possibility that the  recovery depends on the charac-
teristics  of the sample  material.   In other words,  the recovery
may be different  for QC samples, blank samples, and adipose tissue
samples .

     In  the final model  for the relationship  between the actual
and measured concentration, it is assumed that:

                        X =  (C+S)P  * R                         (5.8)

where  p is  added to  cover  the case in  which  the  relationship
between the  actual and  measured concentrations is not linear.  In
the log transformed units, equation (5.8)  is:

                  ln(X) =  pln(C+S)  + ln(R).                   (5.9)
     Because  we  have limited information  on when the instruments
were calibrated,  the effect  associated with calibration, 8j, is
difficult to  estimate.   The calibration term may be different for
each  internal  standard.   According  to  the  Quality  Assurance
Project Plan  (QAP jP)  for the  comparability analysis  (USEPA 1986) ,
the instrument's  calibration  was checked every day and calibrated
as necessary.  Since batches  were usually  processed in one or two
days,  the term for calibration effects  is  assumed to be confounded
with batch effects (i.e., cannot be estimated separately,  based on
the data) .

     After the addition of some terms  for random effects associ-
ated with  batch preparation,  calibration,  measurement  of sample
characteristics,  sample preparation and  injection, and measurement
of the internal standard and  target responses,  the complete model
for the HRGC/MS and GC/ECD measurements  is :
  (XmCbsi) =Pmcln (CCS+SCS) +ln (R^) +6mb+6nibi+5Mbic+8s+5ms+5msi+emcbsi (5.10)
                                39

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     The  terms  in  the  model  are  explained  in  Table  5  and
Appendix E .

     Converted back  to the original  measurement units,  equation
(5.10)  becomes:
                          exp <+i+ic4-+i+^3i><5 . 11)
     For the  analysis of  most of the  data,  the  model shown  in
Table 5 can be significantly simplified.   When discussing specific
analyses in  the  following sections, the  appropriate  simplified
version  of the  model  will be  presented.   The  simplification
usually comes from combining terms which are  confounded.   In  this
case, the combined term will be indicated by  a change  in the  form
of the subscripts.  When equations are provided  in  later chapters,
terms are defined only if they  have not been previously defined.

5 . 4   Basis  for  Analyzing  the  Log Transformed
      Concentrations

     Transforming the  data has implications  for the model to use
in the  analysis,  the variance of  the residuals  (i.e.,  the esti-
mated magnitude of the measurement error) ,  the distribution of the
residuals,  and the  interpretation of the  results.   Assuming the
data have a  lognormal distribution,  the following statements can
be made:
     •    For the original  measurements :
          •    The standard deviation of the measurement  error is
               linearly related to concentration  (the coefficient
               of variation of the original data  is constant) ; and
          •    The measurement errors have  a skewed distribution.
     •    For the log of the original data:
          •    The transformed data have a  constant variance; and
          •    The transformed data will have  a normal  distribu-
               tion.
                                40

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Table 5.    Model for the GC/ECD and HRGC/MS Measurements with an
            Explanation of Each Term
          = l^cln(ccs+scs)+ln(Rmct)+5rnb+5inbi-t-5Mbic+8s+5ms+5msi+emcbsi
Where:
  m = an index for the analytical method used to measure the
        concentration, the two analytical methods are GC/ECD and
        HRGC/MS (subscripts E and M respectively when specified
        methods are used) .

  c = an index for the compound being quantitated.   Note that
        some compounds were quantitated using only one analytical
        method.

  b = an index for the batch in which the samples were processed.
        There were 10 batches of samples for each analytical
        method .

  s = an index for the sample being analyzed.   There were a total
        of 80 samples  (not counting extracts for the GC/ECD
        method) which are uniquely identified by the EPA ID
        number, the batch in which the samples were analyzed and
        the "Fldindic" number which indicates if the sample was a
        spiked multisplit sample.

  i = an index for the internal standard used to quantitate the
        data.  Three different internal standards were used to
        quantitate compounds in the HRGC/MS analysis.

  t = an index for the sample type.   There are three different
        sample types, Blank, QC samples, and Adipose tissue
        samples .

         = the measured concentration using analytical procedure
        m for compound c measured in batch b, sample s, and quan-
        titated using internal standard i.

  Ccs  = the actual  concentration of  compound  c  in  sample  s
        before addition of any spike.

  Scs = the  increase in concentration of  compound c due  to a
        spike being added to sample s.
  Pmc = t^ie slope coefficient for the relationship between
        ln(Xcs) and  ln(Ccs + Scs) .   This relationship may depend
        on  the  analytical method m.   In situations where there is
        not enough data to estimate  this term,  it is  assumed to be
        1.0.
                                41

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Table 5.    (Continued)
          = a constant.  If |3mc = 1.0 this constant can be
           interpreted as the recovery for chemical c using
           analytical method m on a sample with type t.

           a random effect associated  with batch  b using
           analytical method m, assumed to be normally distributed
           with a mean zero and standard deviation of 0^,
           depending only on m.

          = a random effect associated with internal standard  i  in
           batch b using HRGC/MS method,  assumed to be normally
           distributed with a mean zero and standard deviation of
           ^mbi-   This  term is confounded with 5^ in the GC/ECD
           measurements.

           = a random effect associated with calibration for
           compound c quantitated by internal standard i in batch
           b  using HRGC/MS method, assumed to be normally
           distributed with a mean zero and standard deviation of
           °Mbic-

     8S = a random effect  associated with the measurement  of wet
           weight and percent lipid in sample s, assumed to be
           normally distributed with a mean zero and standard
           deviation os.  This term is identical for both the
           GC/ECD and HRGC/MS paired samples.

         = a random effect associated  with sample s using  analyti-
           cal method m, assumed to be normally distributed with a
           mean zero and standard deviation O^, depending only on
           m.

          = a random effect associated with internal standard  i  in
           sample s measured with the HRGC/MS method,  assumed to
           be normally distributed with a mean zero and standard
           deviation O^i,  depending only on  m.   This term is
           confounded with 5^ in  the GC/ECD measurements.

           = a random measurement error for compound c measured
           using analytical method m,  in batch b, in sample s, and
           quantitated using internal  standard i, assumed to be
           normally distributed with a mean zero and standard
           deviation om.      	
                                 42

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     The  characteristics  of  the  data have been checked by looking
at the  relationship  between  the  measurement error and the concen-
tration.   This  relationship  is discussed in Chapter 10.  Although
the relationship between the standard deviation of the data to the
measured  concentration  is  not very precise,  it is consistent with
the use of a log transformation.

     Use  of a log transformation may be  justified if the residuals
based  on  the logged data appear  to  have a  normal  distribution.
Several analyses  were performed using both  the  original data and
the logged data.  Based on histograms of the  residuals, the resid-
uals from the log transformation  are more  normally distributed.
However,  for many analyses, the differences are small.

     We have  assumed that the measurement  error  can be described
by a lognormal distribution for the following reasons:
     •     On theoretical grounds, the data can be expected  to  have
           a distribution similar to a lognormal distribution;
     •     The residuals from the statistical analyses of the orig-
           inal data  have a skewed distribution.  The  residuals
           from the statistical analyses of the log transformed
           data have  a roughly symmetric normal distribution; and
     •     The standard deviation of the residuals from the  origi-
           nal data increase roughly linearly with increasing
           concentration.

     When fitting models to the  log transformed data,  the  esti-
mated  error variance  is  for the  transformed data.   It may  be
desirable to  convert the variance  in  the log scale  to a  coeffi-
cient of  variation  in the original scale.   The  following  formula
relates the  variance of the log  data,  s2, to the  coefficient  of
variation of the original measurements, cv:
                       cv = Vexp(s2)-l                       (5.12)

     For reference, Table B-l  in  Appendix B tabulates the coeffi-
cient of variation for selected values of s.  In the discussion of
                                43

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measurement errors,  the results are presented in terms  of both the
variance  of the  log  transformed  values and  the  coefficient  of
variation of the untransformed  measurements.
                                44

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        ANALYSIS  OF  DETECTION  LIMITS  AND  PERCENT  RECOVERY
     This chapter describes and compares the detection limits,  the
recoveries,  and the proportion of samples with measured concentra-
tions above the detection  limit  (i.e.,  percent  detected),  for  the
paired  NHATS  samples.   The detection  limits  and recoveries  are
discussed first.   The  percent  detected is  affected by both  the
magnitude of the detection limit and the recovery and is therefore
discussed after these other two  topics.

6. 1   Comparison  Detection  Limits

     The measurement  which  is very unlikely  to  be  exceeded in  the
analysis of a  blank  sample is referred to as the  detection limit
or  limit of  detection  (LOD).   The  detection  limit is  defined
differently for the HRGC/MS and GC/ECD methods.   The  GC/ECD detec-
tion limit is based on historical experience with the method.   The
detection limit  using the HRGC/MS method  is based on the lowest
calibration  standard and  the  mass spectrometer signal to  noise
ratio.

     The lowest reported concentration  for the  GC/ECD method,  the
limit of quantification  (LOQ), has been  established  from years of
experience with the method.  The limit  of  detection  is defined as
LOQ/3.    Measurements  between  the LOD and the LOQ  are reported as
less than the  LOQ  using the symbol "<" on the  report  forms.   The
label "Trace"  was assigned in  these  cases  in  the  data set.    A
final concentration of  zero was  reported for samples  with  quanti-
tated concentrations  less than the limit of  detection.   The aver-
age  limit of detection  for the GC/ECD  target  compounds  is dis-
played in Table 6 in ug/g lipid weight.  Because the  GC/ECD detec-
tion limits are  defined on a wet  weight basis, the  LOD based on
wet weight has been  converted  to an LOD based  on  lipid  weight to
determine the average detection  limit.
                                45

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Table 6.  Detection Limits  (ug/g) Using the HRGC/MS and GC/ECD
          Methods, for all Compounds Reported on the GC/ECD Forms
Compound
p,p'-DDT
o,p'-DDT
p,p'-DDE
O,p'-DDE
p , p ' -ODD
O,p'-DDD
alpha-BHC
beta-BHC
gamma -BHC (Lindane)
delta-BHC
Aldrin
Dieldrin
Endrin
Heptachlor
Heptachlor Epoxide
PCB
Oxychlordane
Mirex
trans-Nonachlor
Uncorrected
Hexachlorobenzene
GC/ECD
Average LOD for
all samples
(ucr/q)a
.009
.009
.004
.009
.009
.009
.004
.009
.004
.004
.004
.004
.009
.004
.004
.433
.009
.043
.004
.004

HRGC/MS
Average reported
LOD
(ua/q)b
.049
.013
.352
.013
.013
.127
.020
.212
.019
.018
.013
.020
.041
.018
.016
.011
.016
.013
.014
.015

     a Conversion from ug/g wet weight to lipid weight assumes
       percent lipid equals 77%.  The percent lipid in paired
       samples ranged from 41.5% to 99.3%.
     b Based on the LOD for trace and nondetect measurements.
                                 46

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     For  the  HRGC/MS method,  the detection  limit  is the  lowest
reported  concentration  and is based  on the  lowest calibration
standard  used  to calibrate the instrument and characteristics  of
the signal  and noise.   For signals  meeting the quality  criteria
(see Section  3.4.2), the  detection  limit is  the  maximum of the
concentration  based  on 1)  the  lower  calibration standard and  2)
2.5 times the  noise level.  For  signals not meeting the  quality
criteria, the  LOD is the  maximum  of the  concentration based  on  1)
the lower calibration  standard and 2)  the observed signal.   LODs
are reported for both nondetect and trace measurements.   Because
the  detection  limit depends  on  several factors,  it  can  vary
considerably between samples; however, it will always be  equal  to
or  greater than  the lipid  adjusted  concentration based on the
lower calibration standard.  The average  reported detection limits
for nondetect and trace HRGC/MS measurements  in paired samples are
shown  in Table  6.   For  p,p'-DDT,  p,p'-DDE,  and  gamma-BHC, the
average  HRGC/MS  detection  limits  are  based on  five  or  fewer
reported  detection  limits,  some  of  which were  much larger  than
that based on the lowest  calibration limit.

     For  all compounds except  PCBs  and Mirex, the average  GC/ECD
detection limit  is less  than the  average  HRGC/MS detection  limit.
In addition, the average  GC/ECD detection limits  are  less  than the
minimum HRGC/MS  detection limits  based on the lowest calibration
standard.   The average GC/ECD  detection  limits  are smaller  than
the average HRGC/MS  detection  limits by  a  factor  of at least  10
for p,p'-DDE,   beta-BHC,  and  o,p-DDD.   Figure  6  compares the
HRGC/MS and GC/ECD detection limits for the primary compounds.
                                47

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                 48

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6 . 2    Calculating  Recovery

     Due to  losses  in  the  sample  processing steps,  not  all of the
target material  in  the original adipose tissue sample  appears  in
the final  extract for  measurement.   As a result, the quantity  of
compound measured by the GC/ECD or  HRGC/MS  equipment  is less than
that in the  original  sample.   The  ratio of  the quantity measured
to the quantity in the original  sample is the recovery,  usually
expressed as a percentage.

     The percent recovery can be estimated using two approaches:
     •     Comparing measured concentrations  with known concentra-
           tions in quality control samples;  or
     •     Determining changes in the measured concentration as a
           result of spiking a tissue sample  with a known amount of
           compound,  as in the multisplit samples.

     Sections  6.2.1  through  6.2.3 discuss  the  calculation  of
recovery using HRGC/MS and GC/ECD  procedures  for the  QC  samples
and spiked multisplit  composite samples.

     The model for the data, equation  (5.10), allows for different
recoveries at different  concentration  levels.   Because the  QC
samples  and multisplit  samples  were  tested  at  three or  fewer
concentrations, testing of the hypothesis to determine whether the
recovery is  constant  is not  possible  because  there are insuffi-
cient  degrees  of freedom.   Therefore,  the  calculation of recovery
assumes that the recovery is a constant, independent of  the actual
concentration in the sample.

     6.2.1   Calculating  recovery  using  spiked  multisplit
              samples

     For the multisplit  samples,  the average recovery and  its
standard error are  based on  separate  recovery estimates  for  the
three  spiking  levels.   The calculations assume that the recovery
is the same for all concentrations.   The recovery  estimate at each

                                49

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spiking  level  is based on measurements  in  one unspiked multisplit
sample and four  spiked multisplit samples.

     Using  equations  for  the  measured   concentration  in  the
unspiked and spiked portions of the multisplit samples (see equa-
tion 5.10),  the  following formula can be derived:

                            (s)    (u)
                                    .  p                         lfi  ,x
                                    +  emcs                      (o.l)
Where:
          = Measured concentration of compound c in the spiked
           split  of the sample;
          = Measured concentration of compound c in the unspiked
           split  of  the  sample;  and

     emcs = random measurement error for the fraction R^,  roughly
           lognormally distributed.

     Equation  (6.1)  can  be used to  calculate  recovery  in the
multisplit  samples .  With  measurements on four  spiked multisplit
samples  four estimates of  recovery  for each spiking  level can be
calculated.    Because  the  HRGC/MS  measurements  in the  unspiked
portions were sometimes below the detection  limit or missing9, an
adjustment  for the  unspiked concentration  required some judgment.
For  the  adjustments,  the  missing observations  were  replaced by
zero and the  nondetect  observations were replaced by the LOD/2 .

     A weighted  average of  the  estimates from  the  four spiked
sample  portions  was  used  to  determine  the  recovery for  each
spiking  level.   The weights for calculation  of the weighted  aver-
age  are  usually  based on  the measurement variance.    Because of
possible differences between  batches,  the variance of the  recovery
9No concentration  or  detection  limit was provided  for two trans-Nonachlor
measurements in the mid level unspiked samples.  The footnote stated "compound
is present but  cannot  be quantitated."  As a result  of substituting  zero for
the missing unspiked  concentration,  the calculated recovery will  tend to
overestimate of the actual  recovery.

                                 50

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estimates  based  on  spiked samples  in  the  same batch  as  the
unspiked sample are likely to be less than that for spiked samples
in  different  batches.  The  measurement variances could be  esti-
mated  using  the  components  of variance  which  are  provided  in
Chapter 10.  However, assumptions must be made concerning the best
estimate of each variance component.  For simplicity,  the recovery
estimates  from each spiked  sample were  given  the  same weight
(equivalent to  assuming no batch  effects)  in order  to  calculate
the  recovery  at each spike  level.  An analysis showed that  the
final results  are affected very  little by the weights chosen.

     The recovery  estimates across the three spiking levels  are
independent and will  have  similar  variances.   Therefore,  a confi-
dence interval for the mean recovery across all spiking levels  was
calculated using a t-statistic.  Because the  t-statistic  for each
compound had at most  two  degrees of freedom,  a pooled estimate  of
variance (pooled across  all  spiked compounds) was used to calcu-
late the confidence  intervals for  the recoveries.   Calculation  of
the t-statistic assumes that the recovery  estimates have  a normal
distribution.    For  the  Comparability Study data, the  bias intro-
duced by assuming normal errors was judged to be acceptably small,
relative to the standard error of the estimates.

     In the GC/ECD  analyses, portions  of  the final  extract  from
the unspiked samples  were  analyzed  with each  batch which  included
a spiked sample.  Although these measurements can be  used in  the
calculation of  recovery,  there was some  concern that changes  in
the extract samples  over time as a  result of storage might  signif-
icantly affect  the  estimated recoveries.   Therefore,  the  results
summarized in Section 6.2.3 are not based  on  the extract  measure-
ments; however, these results are  summarized  in Appendix C.   As
shown in  Appendix C,  the recoveries based on  the extracts  are
similar to  those calculated  without using  the extracts, except
that the recovery estimates  based on  the  extract have  slightly
smaller variances.
                                51

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     6.2.2   Calculating  recovery  using  quality  control
              samples

     For the  quality control  samples,  average recovery  and its
standard error were determined from the spiked QC samples in each
of the 10  independent batches.   The  calculations  assume that the
recovery is  the same for  all  concentrations .   The  recovery for
each  sample  is  estimated  from the  measured  quantity  of  each
compound and the known actual quantity in the sample.

     The following equation was used to estimate recovery for the
quality control samples :
This equation can be derived from equation  (6.1) where the concen-
tration in the unspiked portion is zero.

     Confidence intervals on the average recoveries were based on
a t-statistic assuming  normally  distributed  measurement errors.
However,  if  the measurements have a  lognormal  distribution, the
errors in equation  (6.2)  will also have a lognormal distribution.
For the Comparability Study data, the bias introduced by assuming
a normal distribution was small relative to the standard error of
the estimates.   Equation  (6.2)  provides  estimates which  can be
directly  compared  to the  results  from  the spiked  multisplit
samples using equation (6.1).   Equation (6.2)  was used to estimate
recovery  for  the  HRGC/MS  analyses  on  dichloromethane  spiked
samples and the GC/ECD analyses on porcine  fat samples.
                                52

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Table 7.    Average Recovery,  with 95%  Confidence Intervals,  for
            the GC/ECD Measurements on  Spiked Multisplit  and
            Quality Control  Samples
Compound
p,p'-DDT
p,p'-DDE
Beta-BHC
Dieldrin
Heptachlor Epoxide
Oxychlordane
Mi rex
trans-Nonachlor
Corrected
Hexachlorobenzene
Uncorrected
Hexachlorobenzene
PCBs
Spiked multi-
split samples3
b
82%±19%
89%±19%
90%±19%
83%±19%
73%±19%
b
73%±19%
77%±19%
53%±19%
b
Porcine fat
samples
batches 1-3 a
67%±14%
73%±14%
78%±14%
60%±14%
65%±14%
67%±14%
b
75%±14%
52%±14%
33%±14%
b
Porcine fat
samples
batches 4-10
98%±10%
109%±15%
98%±15%
104%±26%
97%±36%
96%±27%
106%±18%
98%±23%
99%±7%
61% c
78%±7% d
a 95% Confidence intervals are based on a pooled variance due to
  the small number of measurements for each compound
b This compound was not spiked into the samples
c All measurements were identical
d See Chapter 9.
                                53

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     6.2.3   Comparison  of  HRGC/MS  and  6C/ECD  recovery

     Recovery estimates  for  GC/ECD  measurements  are  shown  in
Table 7.   Different samples  of porcine material,  with different
spiking  levels,  were used for  batches 1 to  3 and  4  through 10.
Therefore,   recoveries  for  these  two  groups  of  samples  are
presented  separately.   In general,   recovery  for  the compounds
tested using the  GC/ECD procedure was between  60%  and 109%,  with
the exception of  uncorrected hexachlorobenzene which  has a lower
recovery.  The recovery  based on the spiked multisplit  samples was
between  73%  and  90%  for  all compounds  except uncorrected hexa-
chlorobenzene with a recovery  of  53%.  For  batches 1 to 3,  the
recovery was  between 60% and 78% with  the exception  of  hexachloro-
benzene  with a recovery of  33% for the uncorrected measurements
and 53%  for  the  corrected measurements.   Recovery based on the
porcine  adipose tissue  samples from  batches  4  through  10  were
between  96%  and 109% with  the exception of uncorrected  hexachloro-
benzene and PCBs,  with recoveries of  61% and 78% respectively.

     For each compound except trans-Nonachlor, the GC/ECD  recovery
estimates  for the porcine fat  samples from  batches 1 to 3  were
less than  for spiked  multisplit samples which are in turn  less
than that  for the porcine samples in  batches  4 through 10.  This
pattern  is  statistically  significant10.  The differences between
the recovery in the three sets of  samples  may be  due  to differ-
ences in the sample  material,  deviations  in  the spiking solutions
from the nominal levels, or differences in the recovery in batches
1 to 3 versus in  batches 4 through  10.  In  either case, the esti-
mated recovery depended on the set of  samples  being  analyzed.

     Recovery estimates for HRGC/MS  measurements  are  shown  in
Table 8.    The average HRGC/MS recovery  estimates   for spiked
compounds in  the dichloromethane samples measured were  between 69%
10A  two-way  analysis of variance  (assuming the errors  in the recovery
estimates are independent) suggested that differences among compounds were not
significant and  that differences among  types of sample material were very
significant (p<.0001).

                                54

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and 81% for, with the exception of hexachlorobenzene with an esti-
mated  recovery  of  51%.   The  recovery estimates  based  on  the
HRGC/MS spiked multisplit  samples  are  very variable and not very
accurate.   One compound,  beta-BHC,  contributes substantially to
the pooled  error  estimate.   The precise estimate of recovery for
beta-BHC  depended greatly on  the weights  used to  average  the
recovery  in  the  four  multisplit spiked portions.  Removing beta-
BHC from  the calculations gave a pooled  error estimate  of ±23%
(±28% for dieldrin).   The  recovery estimates for compounds other
than beta-BHC were between  26%  and  56%.

     The  recoveries for  all  samples  using  the GC/ECD method were
similar to those from the spiked dichloromethane samples using the
HRGC/MS method;  however,  they were roughly  twice  those based on
the multisplit  samples using  the  HRGC/MS  method.   The recovery
estimates differed depending  on the sample  matrix.  Therefore,  any
corrections  for  recovery  should  be  based on  recovery estimates
based on the same sample matrix.   Considering only the  recoveries
from lipid  material,  the  GC/ECD  recovery was  greater than  the
HRGC/MS recovery except for beta-BHC for which  the recovery esti-
mates were similar.

6.3   Comparison  of   Percent  Detected

     The  number of  paired samples  in which  each compound  was
detected, i.e.,  has  a  data  qualifier  of  "Trace"  or  "Positively
Quantified", is shown  in Tables A-3  and A-ll in Appendix  A.   The
nine primary compounds were  detected  using both the  GC/ECD  and
HRGC/MS methods.   These compounds  were  used to  compare the percent
detected for the two  analysis  methods.  Table  9 shows the number
of measurements  and  percent  detected for  each  method.   Figure 7
shows the percentage  of HRGC/MS  and GC/ECD paired  samples  with
detectable  quantities of  each compound.    The  HRGC/MS dieldrin
measurements were determined in only 3  of  the 10 batches,  result-
ing in  fewer HRGC/MS  measurements than GC/ECD measurements  for
                                55

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Table 8.    Average Recovery, with 95%  Confidence  Intervals,  for
            the  HRGC/MS Measurements  on Spiked Multisplit  and
            Quality Control  Samples
Compound
p,p'-DDT
p,p'-DDE
Beta-BHC c
Dieldrin d
Heptachlor Epoxide
Oxychlordane
trans-Nonachlor
Hexachlorobenzene
PCBs f
Spiked multisplit
samples a
b
26%±50%
99%±50%
37%±61%
50%±50%
42%±50%
56%±50% e
41%±50%
b
Dichloromethane spiked
samples
76%±13%
80%±21%
74%±15%
77%±75%
81%±9%
71%±6%
73%±8%
51%±5%
69%±6%
a
b
c
±95% Confidence intervals based on pooled variance
This compound was not spiked into the samples
The estimate of recovery for the lowest spike level of beta-
BHC in the multisplit samples is quite high, resulting in a
high estimate of recovery and contributing to the large
pooled variance.
Dieldrin, in Fraction 2, was analyzed in only 3 batches, thus
has fewer measurements and a larger confidence interval.
As a result of substituting zero for the missing unspiked
concentration, the calculated recovery will tend to overesti-
mate of the actual recovery.
See Chapter 9.
                                 56

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this compound.  For PCBs the GC/ECD concentrations were coded into
concentration ranges.   The coded concentrations  were  reported  on
the analysis report forms.

     The percentage of samples with detected quantities depends  on
the detection limits, the recovery,  and the concentration level  in
the samples.   Low concentrations are  more likely to  be detected
using methods with lower detection limits.  The  detection  limits
apply  to the  concentration after  recovery losses.    Therefore,
given two methods  with the same detection limit, the  method with
lower recovery will  have lower  measured concentrations and  possi-
bly more  samples with concentrations  below the  detection  limit.
Note that the recovery and  detection limits make  no  difference  to
the percent  detected if  all concentrations are high enough  to  be
positively quantitated in all samples.

     In  general,  the  analysis  shows  that the  recovery for the
HRGC/MS method  is similar  to or lower  than that for  the  GC/ECD
method and the detection limits  for the HRGC/MS  method are  higher
than those  for the  GC/ECD  method.   Both  of  these factors  would
indicate that there  would  generally be  a  higher  percent detected
in the GC/ECD than the HRGC/MS paired samples.   With  the exception
of PCBs, this  conclusion is consistent with the  results shown  in
Table 9.  The GC/ECD detection limit for PCBs was much higher than
the HRGC/MS  detection limit.   However, the percent  detected  is
similar  because  the concentrations  in most  samples were  high
enough that  PCBs were detected in all GC/ECD samples.
                                59

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  7    RELATIONSHIP  BETWEEN  THE  GC/ECD  AND  HRGC/MS METHODS


     This chapter presents the  statistical procedures  and  results
comparing  the  GC/ECD  and  HRGC/MS  measurements  in  the  paired
samples.  After a summary of the results,  the  relationship  between
the GC/ECD  and HRGC/MS  measurements  for each  compound are dis-
cussed,  followed by  scatter plots  of  the  measurements  on the
paired samples.

7 .1   Modeling the  Relationship Between  the  GC/ECD  and
      HRGC/MS  Methods

     Determining the relationship between the  GC/ECD and HRGC/MS11
measurements  is desirable  in  order to  determine  factors  that
affect  the  relationship  and to  predict the  measurements which
would  be obtained using one  analytical  method  based  on the
observed measurements from the  other method.   The relationship
between  the  measurements  can  be  expressed  by a  mathematical
formula such as:

                     BCD = f(MS)  + error                      (7.1)

where f  is  a  function  to be determined.   This  equation  can be
solved for the HRGC/MS measurements to give:

                     MS  = g(ECD)  + error                      (7.2)

where g is the inverse function  of  f.

     Differences between  the  HRGC/MS  and GC/ECD measurements may
be  due  to   many  factors,   including  differences  in  recovery
(extraction  efficiency)  and  calibration.   The  following discussion
assumes  that,  due to the calibration  process, the  HRGC/MS and
GC/ECD measurements are  unbiased estimates of the concentrations
11 HRGC/MS  and GC/ECD are  abbreviated as MS and BCD,  respectively,  in  the
equations.

                                61

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in the  final extract.   Assuming  the  two portions  of the  split
sample have the same initial concentration,  consistent differences
between HRGC/MS and GC/ECD measurements will  be due to differences
in recovery.   Therefore,  the expected  relationship between  the
GC/ECD and HRGC/MS measurements is:

                         MS = R1  BCD                         (7.3)

where R' is the ratio of the  recovery  using  the  HRGC/MS method to
the recovery  using the  GC/ECD method.   Note that R1 can  also be
described  as the  ratio of the  HRGC/MS to  GC/ECD  measurement.
According to  equation  (7,3),  a  doubling of  either the GC/ECD or
HRGC/MS measurement should be accompanied by a doubling of  the
other measurement.

     Questions which might be  asked  about  the  applicability of
equation (7.3) as  a model  for  the observed data are:
          Is the ratio of the HRGC/MS to  GC/ECD measurements
          really constant?
     •    If the ratio is constant, is  it different from 1.0? and
     •    Does the ratio of the HRGC/MS to GC/ECD measurements
          depend on the batch?

     Statistical analysis  is  used to determine  if equation  (7.3)
provides an adequate description  of  the  data.  For this analysis,
both the HRGC/MS and GC/ECD measurement errors are assumed to have
a lognormal distribution.   The analysis of measurement error vari-
ances to support this assumption  is  discussed  in Chapter  10.   For
data  with a  lognormal  distribution,  1)  the measurement  error
increases as the concentration increases  such that the coefficient
of variation is constant,  and  2)  the  log  transformed data  has
constant measurement error  variance,  independent  of concentration.

     To make  the  data  consistent with the assumptions  behind the
statistical analysis (i.e., that  the error variance  is constant),
the log transformed data are used.   Taking  the logarithm of the
                                62

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HRGC/MS  and  GC/ECD  measurements  in equation  (7.3)  gives  the
following equation:

                   In (MS) = In(R') + In (BCD)                    (7.4)

     The correlation  of  In (MS)  and In (BCD)  provides a descriptive
measure of the  linear relationship between  the HRGC/MS and GC/ECD
measurements.   The correlation can be used to  test  if there is a
significant  linear  relationship between the HRGC/MS  and GC/ECD
measurements.

     The appropriateness of equation  (7.4) for describing the data
can  be tested  by  fitting  additional  terms  to  describe  other
factors which might also affect the  HRGC/MS-GC/ECD relationship.
Two  additional  terms were  used:  terms  for  differences  between
batches and for nonconstant recovery ratio,  i.e., a recovery ratio
which depends on the  concentration  in the sample.   If these addi-
tional terms are statistically significant,  there is evidence that
equation (7.3)  and (7.4)  do not adequately describe the HRGC/MS-
GC/ECD relationship.  With these additional terms added, the model
which was fit to the  data is shown below in equation (7.5) .   This
model  can  also  be  obtained from  the  model  (equation 5.10)  by
equating the true  concentrations in the  equations  for the HRGC/MS
and GC/ECD  measurements and  combining  terms.
          ln(XMcbs) = Rc + Pc * ln(XEcbs) + SbC + ecbs           (7.5)
Where:
     ln(XMcbs) = the HRGC/MS measurements for compound c in sample
          s analyzed in batch b;
     ln(XEcbs) = the GC/ECD measurements for compound c in sample s
          analyzed in batch b;
     Pc = a constant  for each  compound.  This term will be 1.0 if
          the recovery ratio for the two methods is  constant,  i.e.
          independent of concentration.  Testing if  the  recovery
                                63

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           ratio is not constant is achieved by testing if J3C is
           significantly different from 1.0;
     Rc = a constant for each compound.   If |3C  =  1,  this  constant
           can be interpreted as the log of the ratio of the
           HRGC/MS recovery to the GC/ECD recovery;

     &bc = a random effect  associated  with  batch b and compound c,
           assumed to be normally distributed with a mean zero and
           standard deviation of cybc.   This  term combines  the batch
           effect terms in the models for both methods.

     ecbs  = a random error  for compound c in sample s analyzed in
           batch b, assumed to be normally distributed with a mean
           zero and standard deviation o.  This error is the
           combined result of within sample errors and measurement
           errors in both the HRGC/MS and GC/ECD measurements.

Equation  (7.5)  can be obtained from the model  (equation  (5.10)) by
equating the true concentration  (Ccs)  in equations for the  HRGC/MS
and GC/ECD measurements and combining  confounded  terms.

     Standard  regression procedures  can  be used to estimate the
parameters  Rc,  |3C, and 8^  in equation (7.5).  However,  regression
procedures  assume that  only the  dependent variable  (HRGC/MS in
this case)  has measurement error.  When both the dependent  and the
independent variable are measured with error,  the regression esti-
mates of the parameters are biased.  Fitting equation  (7.5) to the
data using  regression provides  a  functional equation.    This equa-
tion is optimal in the sense that it minimizes the  squared  predic-
tion error  for the  data  on which  the  equation is based.  However,
the functional equation  may provide particularly poor predictions
of HRGC/MS measurements from  GC/ECD  measurements  when  both mea-
surements  have error  and the equation  is  extrapolated beyond the
range of the original data.

     Statistical  models which  account  for measurement error in
both the  independent and dependent  variable are  called  structural
models.    The  slope  for the  functional  model,  estimated using
regression, is a  biased estimate of  the  slope for the  structural
                                64

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model.   Under either one of the  following conditions the bias  is
minimized  and the regression slope  provides a good  approximation
to the slope for both the functional and structural model:
      •     Measurement errors in the independent variable are small
           relative to the range in the independent variable; or
      •     The  slope of the relationship, |3C, is close to zero.

     Although the slope  estimate may be  biased,  a test  of the
hypothesis that  the  slope  is significantly different from zero  is
relatively unaffected  by  errors  in  the  independent  variable.12
Therefore, regression results can be used  to test  if  the slope for
either the functional or structural model  is significantly differ-
ent from zero.

     The  slope  in equation (7.5)  is  expected  to be  1.0  if the
recovery  ratio is constant.   Therefore,  in order  to test if the
recovery  ratio is constant, the  slope from the structural model
must be  compared to  1.0.   Unfortunately,  with errors in the inde-
pendent  variable,  regression,  the primary tool  for fitting equa-
tion  (7.5), may  perform poorly  unless  the slope is close to zero.
An alternate  approach  is  to transform  the data  by  rotating the
coordinate axes  so that a statistical test comparing the regres-
sion slope to  zero is used  to  test if  the slope in the functional
model is different from 1.0.

     Both the  HRGC/MS and GC/ECD data  from the Comparability Study
have  measurement errors which  cannot  be  ignored.   As a result,
standard regression  procedures  must  be  modified  to model  the
structural relationship between the two measurements  and  to
satisfy the assumptions behind the regression procedures.  To meet
these objectives, the following five steps were used to model the
relationship between  the HRGC/MS and GC/ECD measurements:
12Errors in the independent variable will reduce  the power of the test,
however the probability  level for the test will be correct.

                                65

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      (1)  Use log transformed data, equalizing the measurement
          error variance across concentrations within each
          analysis method;

      (2)  Scale the data to make the measurement error for the
          scaled GC/ECD and the scaled HRGC/MS data equal;

      (3)  Rotate the coordinate axes so that the regression slope
          will be zero if the recovery ratio is constant;

      (4)  Use analysis of covariance on the transformed scaled and
          rotated data to estimate parameters and test hypotheses;
          and

      (5)  Transform the parameters back to the original units for
          reporting and plotting.


     The scaling  of the data  to equalize the  HRGC/MS and GC/ECD

measurement errors is based on the analysis of  variance  components

discussed in Chapter  10.   The measurement  errors for the HRGC/MS

method were found  to  be roughly three  times  those for the GC/ECD

method.  For estimating the structural models,  a  measurement  error

ratio of 3 was used for all compounds.


     The result  of the scaling,  transformation,  and rotation  is

equivalent to fitting the  following  equation to the data for each

compound:


              ln(Y)  = a +  b *  ln(Z)  + ^ + error              (7.6)


Where:

     Y = the ratio of the HRGC/MS and GC/ECD measurements;

     Z = the transformed product of the HRGC/MS and GC/ECD mea-
          surements: MS'33 BCD3;

     b = slope;  if the slope b is equal to zero,  the ratio of the
          HRGC/MS and GC/ECD measurements is constant;

     a = intercept; and

     6b =  a batch effect;   if  the batch effect is  significant,  then
          there are differences in the ratio of the HRGC/MS and
          GC/ECD measurements between batches.
                                 66

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     Equation  (7.6)  was  fit to all positively quantified measure-
ments,  ignoring  pairs of measurements in which one  member  of the
pair  was either  a trace  or nondetect measurement.   Except  for
dieldrin, both methods had positively quantifiable concentrations
for most  (at least  37 out  of 45)  samples,  thus  any bias  caused by
ignoring  the below-detection results  is  expected  to be  small.
Because most of the trace  and nondetect measurements were obtained
using the HRGC/MS  method,  the  slope  will tend to  be biased toward
the low side.   An analysis was performed to  assess  the  affect of
trace and  nondetect measurements.   The results of the hypothesis
tests   (significant  versus  nonsignificant)  were  unaffected  by
replacing  trace  and  nondetect  observations  by the  corresponding
LOQ or LOD and including these samples into the model fit.

     The full model for  the comparability  analysis (equation 7.6)
was fit to data for the  following seven compounds: p,p'-DDT,  p,p'-
DDE, beta-BHC, heptachlor  epoxide,  oxychlordane,  trans-nonachlor,
and hexachlorobenzene.   For hexachlorobenzene the  relationship
between the HRGC/MS measurements and both the corrected and uncor-
rected GC/ECD measurements  were modeled.   Due to  the small  number
of paired observations for dieldrin,  the results  for dieldrin are
approximate and differences between batches could not be tested.

     The  results of  fitting equation  (7.6)  to the data  include
parameter estimates for the slope and tests of the hypotheses that
there are  no batch effects  and that  the  ratio of the HRGC/MS to
GC/ECD measurements    (i.e.,  the recovery ratio) is  constant.   If
the regression  slope  is significantly different   from zero,  then
the hypothesis  that  the recovery  ratio is constant  is  rejected.
The equation which fits the data has the form:

                       MS = R1 * ECD d                       (7.7)

where R1  and d are  constants.   The  exponent d is  determined by
transforming  the  slope  in equation  (7.6)  back  to the  original
scale,  involving  an  adjustment to correct for measurement  error
                                67

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and  to  reverse the  rotation,  scaling,  and log  transformations.
Equation (7.7) for the HRGC/MS-GC/ECD  relationship is referred to
as the "best  fit" equation.  The  constant,  R1,  in equation (7.7),
depends on the measurement  units.  For the  analysis,  all concen-
trations are in micrograms per  gram of extracted lipid (ug/g).   If
different measurement units  are  used  (for example,  nanograms  per
gram) the exponent in equation  (7.7)  remains the same, however the
constant must be  adjusted  for  the new  measurement  units.    No
adjustment  is necessary if d  =  1.0.

     The scaling  constant R1 is  calculated  so that  the  best  fit
line passes through the  average of the HRGC/MS and GC/ECD measure-
ments.  The method for calculating the averages depends on assump-
tions about  the significance  of a  batch effect.   If there  are
significant differences  between measurements in different batches,
an estimate of R'  is obtained by averaging the measurements within
a  batch and  then averaging the batch  averages  to obtain  the
overall average,  as  in equation  (7.8):

                                 nv
                       •     «   S 10 m                      <7'8'

where m is the  number of paired samples with  measurements within
batch b.

     If there are no differences between batches, then an estimate
of R' is obtained  by averaging across all samples  as  in equation
(7.9) :
                    In(R')  = *—l —^	fc                     (7.9)

where n is the number  of  paired samples with measurements.


                                68

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     The  two equations for estimating R'  are  equivalent  if the
number of measurements  in  each batch is the same.   Because  (I) the
number  of  samples per batch  vary  from  I  to  5,  (2)  there are
statistically significant batch  effects  for some,  if  not  all,
compounds,  and  (3)  batch effects  are significant  based  on the
components  of variance, equation  (7.8) was used in all  cases to
estimate R1.

7.2  Comparison  of  HRGC/MS  and  GC/ECD Measurements

     Statistical  hypothesis tests were used to test  the following
overall  hypotheses:  (1) there is  no linear  relationship between
the GC/ECD  and  the HRGC/MS measurements,  (2)  there are  no differ-
ences between batches,  (3)  the recovery ratio is  constant, and (4)
the average  recovery  ratio is  1.0.   Each of these overall hypothe-
ses were  tested at the 95 percent  confidence level.   There  were
multiple chances  to test each hypothesis,  using data  from each of
the primary compounds.   To limit  the probability of  incorrectly
rejecting  these overall hypotheses  due to the  use of  hypothesis
tests on  multiple individual  compounds,  the  following  procedure,
based on the Bonferroni inequality,  was used.  An overall hypothe-
sis was  rejected at the 5 percent level if the probability level
for the hypothesis test using any one  compound was less than  0.71
percent13.   If the overall  hypothesis was rejected at  the 5 percent
level then  hypothesis  tests  for each  compound,  testing  at the  5
percent  level,   were  used to  identify individual  compounds for
which the data  were inconsistent with  the  hypothesis.   Using  this
procedure,  the  probability  of rejecting  an overall  hypothesis
using all of the compounds  is less than 5 percent.

     For the first overall hypothesis,  the correlations  of the log
transformed HRGC/MS and GC/ECD  measurements are  shown in Table 10,
13There are 7 compounds with data which are both independent and numerous
enough to test the hypotheses (corrected and uncorrected Hexachlorobenzene  are
considered to be one compound and dieldrin is not counted) .  The conservative
formula for the Bonferroni  limit would set alpha  (i.e.,  1  -  the confidence
level) for the overall hypothesis test to 0.05/7 = .0071 or 0.71 percent.

                                 69

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along with the  results  of a test of the hypothesis  that  the true
correlation is  zero  (i.e.,  no  linear  relationship).   The  signifi-
cance levels  are only  approximate,  since  possible  batch effects
have not  been  considered.   The overall hypothesis  of no  linear
relationship  is rejected  at the 5 percent level,  indicating that
there is  a  statistically significant linear  relationship between
the measurements from the HRGC/MS and the  GC/ECD methods.   As can
be seen from Table 10, the hypothesis of no linear  relationship is
rejected at the 5 percent level for all compounds except dieldrin.
Significant correlations are indicated by using bold type.

     For testing the second and third overall hypotheses,  equation
(7.6) was fit  to  the  GC/ECD  and HRGC/MS data for the  primary
compounds.  Table 11 shows the results of the statistical  tests to
answer the following questions:
     •    Were  there consistent differences in the  HRGC/MS-GC/ECD
          relationship between batches  (i.e.,  are there batch
          effects)? and
     •    For samples within batches,  does  the ratio  of the
          HRGC/MS to GC/ECD measurement  depend on the concentra-
          tion  (i.e., is there non-constant recovery)?

     Both the overall hypotheses of no  batch effects and  constant
recovery  are  rejected,  indicating  that,   at  least for some  com-
pounds,   the   recovery ratio  differs among  batches  or  that  the
recovery depends on the concentration.  As can be  seen  from Table
11,  the hypothesis  of no  batch  effects  is rejected at the  5
percent  level  for p,p'-DDE,  beta-BHC,  oxychlordane,  and  trans-
nonachlor.  The hypothesis of constant recovery  is  rejected at the
5  percent level  for p,p'-DDE,  beta-BHC,  and  hexachlorobenzene
(both corrected and uncorrected).
                                70

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    Table 10.  Correlation Between Log  Transformed HRGC/MS  and
               GC/ECD Measurements
Compound
p,p'-DDT
p,p'-DDE
Beta-BHC
Dieldrin
Heptachlor
Epoxide
Oxychlordane
trans-
Nonachlor
Uncorrected
Hexachloro-
benzene
Corrected
Hexachloro-
benzene
Number of
paired
samples
39
41
39
5
37
37
41
37
37
Correlation21
0.86
0.84
0 .52
0.52
0.57
0.57
0.84
0.63
0.71
Significance test
for correlation
different from
zero
p < .0001
p < .0001
p = .0008
p = .68
p = .0002
p = .0002
p < .0001
p < .0001
p < .0001
aThe overall  hypothesis of no linear relationship between the HRGC/MS and
 the GC/ECD  measurements was rejected at the 5 percent level using the
 Bonferroni approach.   Values in  Bold text identify individual compounds
 for which the hypothesis is rejected.
                                   71

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Table  11.   Summary  of the Statistical Tests for Batch Effects and
             Nonconstant Recovery
Compound
p,p'-DDT
p,p'-DDE
Beta-BHC
Dieldrin
Heptachlor
Epoxide
Oxychlordane
trans-
Nonachlor
Uncorrected
Hexachloro-
benzene
Corrected
Hexachloro-
benzene
Test for
Batch
Effects3
p = .37
p = .014
p = .0001

p = .13
p = .024
p = .0089
p = .13
p = .25
Test for a
nonconstant
HRGC/MS-
GC/ECD
measurement
ratio3
p = .16
p < .0001
p = .014
p = .99
p = .44
p = .065
p = .86
p < .0001
p = .0006
Comments

Batch effects are not
significant after removing
batch 3
Data in batches 1, 2, and 3
are significantly different
than the other batches
Not enough data to fit a
batch effect
There is one extreme
observation

Data in batches 1, and 2
are significantly different
than the other batches


3The overall hypotheses  of no batch effects  and constant  recovery  were  re-
  jected at the 5 percent level using the Bonferroni approach.  Values in Bold
  text identify individual compounds  for which the hypothesis is rejected.
                                    72

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     On the assumption  that  the  recovery ratio is constant (i.e.,
d = 1.0), the fourth overall hypothesis was tested using the ratio
of the GC/ECD to  the  HRGC/MS measurements  in  equation (7.8).   The
results are presented in Table  12  and Figure 8.   The  confidence
intervals  were  calculated  using a  t-statistic  and  the  standard
error of ln(R')/  assuming the batch  means are  independent.

     Based  on the  confidence intervals,  the overall  hypothesis
that  the  recovery  ratio is  equal  to  1.0  is rejected at the  5
percent  level,   indicating  that,  for  at  least  some  compounds,
recoveries for the  HRGC/MS and the  GC/ECD methods  are  different.
As can  be seen from  Table  12,  the  hypothesis that  the  recovery
ratio is  equal to 1.0 is rejected at the 5 percent  level  for all
compounds except  beta-BHC and dieldrin.  For  compounds  for which
the test for nonconstant recovery ratio is significant,  the calcu-
lated ratio (and  confidence  interval)  represents  an average ratio
for composite  samples  in the 1984 NHATS  survey.

     Table 12 also shows the ratio of the GC/ECD to HRGC/MS recov-
eries estimated from the multisplit spiked samples.   Although this
ratio estimate is not very  precise,  the  correlation  of  the recov-
ery ratio calculated  from the  multisplit samples  and the  ratio of
the GC/ECD to HRGC/MS measurements is statistically significant at
the 5 percent  level.

     The estimated GC/ECD to HRGC/MS ratios were  greater  than 1.0
for all  compounds,  indicating that the recovery for the  GC/ECD
method is typically greater than for the HRGC/MS method.  However,
the 95%  confidence intervals for beta-BHC  and dieldrin  include
1.0.    For all  other  compounds  tested, the  GC/ECD  recovery  was
significantly  greater  than that for  the HRGC/MS method.
                                73

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Table  12.   Geometric Mean  Ratio of  GC/ECD  to HRGC/MS Measurements
Compound
p,p'-DDT
p,p'-DDEc
Beta-BHCc
Dieldrin
Heptachlor
Epoxide
Oxychlordane
trans-
Nonachlor
Uncorrected
Hexachloro-
benzene0
Corrected
Hexachloro-
benzenec
Geometric
Mean Ratio of
GC/ECD to
HRGC/MS
Measurements3
3.88
2. 67
1.25
2.63
2.76
2.04
2.31
1. 41
2.11
95%
Confidence
interval
3.38-4.47
1.99-3.59
0.78-2.02
0.50-14.0
2.13-3.57
1.52-2.75
1.90-2.80
1.19-1.68
1.85-2.41
Ratio of
GC/ECD to
HRGC/MS
Recoveries in
Multisplit
Samples
b
3.15
0.90
2.43
1.66
1.74
1.30
1.29
1.88
p-value
<.05
<.05


<.05
<.05
<.05
<.05
<.05
 aThe overall hypothesis  that the recovery ratio is  1.0 was rejected at the
   5 percent  level  using the Bonferroni approach.   Values in Bold  text
   identify individual compounds for which the hypothesis  is  rejected.
 bp,p'-DDT was not spiked into the multisplit  samples.
 °The data  suggests that  the recovery ratio is not constant for these
   compounds.
                                    74

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Figure 8    Geometric mean ratio of the  GC/ECD and HRGC/MS
            measurements for primary  compounds,  with 95% confidence
            intervals.
                                 75

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7.3   Comparisons  for  Each  Compound

     The results  for each compound are discussed below  along with
an equation  describing the  relationship between  the HRGC/MS  and
the GC/ECD measurements assuming constant recovery ratio.   If  the
assumption of  constant recovery  ratio is rejected  based on  the
statistical tests, both the  best fit equation and a  simpler  equa-
tion  based  on  the  assumption of  constant  recovery  ratio  are
presented.   Although the best fit  equation  is  likely  to  apply over
a wider range of concentrations, the  simpler equation is easier to
use and  describes  the  ratio  of  the  geometric mean HRGC/MS  and
geometric mean GC/ECD measurements for the 1984 NHATS survey.

     p.p'-DDT

     Neither the  test  for batch effects  nor the test for  noncon-
stant recovery  ratio was significant.   The  equation which best
approximates  the relationship between the HRGC/MS  p,p'-DDT  and
GC/ECD p,p'-DDT measurements  in the 1984 NHATS survey is:

                         BCD
                    MS - Y~QQ = °-26 * ECD                   (7.10)

Differences between the geometric  mean  HRGC/MS and GC/ECD measure-
ments were significant,  indicating that the  recoveries for  the  two
methods are different.

     pfp'—DDE

     The test  for nonconstant  recovery ratio was highly signifi-
cant (p < .0001) and the test for  batch effects  was  significant (p
= .0136).  The  data  for batch  3 are noticeably  different from  the
other batches.   After  removing the  data for batch  3,   the  batch
effect  is  no  longer significant  and the  test  for nonconstant
recovery ratio  is still highly significant (p < .0001).   Because
the slope estimates are almost  identical  with  and without batch 3,
                                76

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all  of  the  data  are used  for the  summary statistics  presented
below.

     The equation which best approximates the relationship between
the HRGC/MS p,p'-DDE and GC/ECD p,p'-DDE measurements is:

                     MS = 0.47  * BCD0-64                     (7.11)

     The  following  equation  relates the  HRGC/MS  p,p'-DDE  and
GC/ECD p,p'-DDE measurements in the  1984 NHATS  survey,  and can be
used for extrapolation if the  recovery ratio can  be  assumed to be
constant :
                    MS - -=- =  0.37  * ECD                   (7.12)
                         Z . D /

Differences between the geometric mean HRGC/MS and GC/ECD measure-
ments were significant, indicating that the recoveries for the two
methods are different .

     Beta-BHC

     The  test  for nonconstant recovery  ratio  was  significant
(p = .0138) and the test for batch effects was highly significant
(p =  .0001) .    Although the batch  differences  follow a  general
trend,  with higher measurement  ratios in batches  1,  2,  and 3  than
in  later  batches,  no  observations  or  batches  are obviously
unusual.   Excluding  the first three batches  makes  little differ-
ence in the results.

     The equation which best approximates the relationship between
the HRGC/MS beta-BHC and GC/ECD  beta-BHC measurements is :

                     MS = 0.38  * ECD0-64                     (7.13)

     The  following  equation relates the HRGC/MS  beta-BHC  and
GC/ECD  beta-BHC measurements  in the  1984 NHATS survey,  and can be
                                77

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used for extrapolation  if  the  recovery  ratio  can  be assumed to be
constant:
                    MS = Y~^J =  0.80  * ECD                    (7.14)

Differences between the geometric mean HRGC/MS and GC/ECD measure-
ments were not statistically significant.

     Dieldrin

     There were  only five paired samples with  positively quanti-
fied measurements for dieldrin.   Although the full model could not
be  fit  to so  few data  points, the  data  were used to  test  for
nonconstant  recovery ratio.   No significant relationships were
found.

     The equation which best approximates the relationship between
the HRGC/MS dieldrin  and GC/ECD dieldrin measurements  in the 1984
NHATS survey is:
                    MS = T   =  0.38  * ECD                   (7.15)
                         / . D-3

Differences between the geometric mean HRGC/MS and GC/ECD measure-
ments were not statistically significant.

     Heptachlor Epoxide

     Neither the  test for  batch effects nor the  test  for noncon-
stant recovery ratio  was significant.   The  lowest observation was
particularly  influential on the regression  fit.   However,  with
this observation removed, the conclusions are unchanged.

     The equation which best approximates the relationship between
the  HRGC/MS  heptachlor epoxide and  GC/ECD  heptachlor  epoxide
measurements in the 1984 NHATS  survey is:
                                78

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                    MS = -r7 =  0.36  *  BCD                    (7.16)
                         Z . /O

Differences between the geometric mean HRGC/MS and GC/ECD measure-
ments were significant, indicating that the recoveries for the two
methods are different.

     Qxychlordane

     The test  for  nonconstant recovery ratio was not significant;
however, the recovery  ratios for  the GC/ECD and HRGC/MS oxychlor-
dane  measurements  differed  significantly between  batches  (p  =
.0243).

     The equation  which best  approximates the relationship between
the HRGC/MS  oxychlordane and GC/ECD oxychlordane  measurements  in
the 1984 NHATS survey is:

                         ECD
                    MS = 2(j£ -  0-49  *  ECD                    (7.17)

Differences between the geometric mean HRGC/MS and GC/ECD measure-
ments were significant, indicating that the recoveries for the two
methods are different.

     trans-Nonachlor

     The test  for  nonconstant recovery ratio was not significant,
however, the ratio  of the  GC/ECD and HRGC/MS  recoveries  varied
significantly  between  batches  (p =  .0089).    The  ratio  of  the
GC/ECD to HRGC/MS  measurement was  smaller  in batches 1  and 2 than
for the other batches.

     The equation  which best  approximates the relationship between
the HRGC/MS  trans-nonachlor  and  GC/ECD trans-nonachlor measure-
ments in the 1984 NHATS survey is:
                                79

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                         ECD
                    MS = Y~3l = °'43 * ECD                   (7.18)

Differences between the  geometric  mean  HRGC/MS  and GC/ECD measure-
ments were significant,  indicating that the  recoveries  for the two
methods are different.

     Uncorrected Hexachlorobenzene

     The  test  for  nonconstant recovery ratio of uncorrected hexa-
chlorobenzene  was  highly significant (p < .0001) and the test for
batch effects  was  not significant.  Inspection of the plot  of the
data and  the residuals from the  fit suggested that  there were five
observations which might be judged to be  unusual and were influen-
tial  on  the estimated  slope.   If these five  observations  were
removed from the model,  the test for nonconstant recovery ratio is
still significant  (p  = .0332).   The five unusual  observations are
associated with the lowest three  and  highest  two GC/ECD measure-
ments.  The  estimated slope  depends   on  which observations  are
removed from the data set.   Because none of the observations are
clearly in  error,  the results  summarized below are  based  on all
data points.14

     The  equation which  best  approximates the relationship between
the HRGC/MS  hexachlorobenzene and GC/ECD uncorrected hexachloro-
benzene measurements is:

                      MS  -  0.12  * ECD0-47                     (7.19)

     The  following  equation relates the  HRGC/MS hexachlorobenzene
and GC/ECD  uncorrected hexachlorobenzene measurement  in  the  1984
NHATS survey,  and can be  used for  extrapolation  if  the  recovery
ratio can be assumed to be constant:
14If the two most  influential  points are  removed,  the  results change very
little and the conclusions do not change.

                                 80

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                         ECD
                    MS = —£Y = 0-41 * ECD                   (7.20)

Differences between the  geometric mean HRGC/MS  and GC/ECD  measure-
ments were significant,  indicating  that the  recoveries  for the  two
methods are different.

     Corrected Hexachlorobenzene

     The test for nonconstant  recovery ratio for  Hexachlorobenzene
was very  significant  (p =  .0006)  and the test for  batch  effects
was not  significant.   As with the  uncorrected hexachlorobenzene,
several points are  influential in determining the slope;  however,
removing these points  does not  change the conclusion that  there  is
a nonconstant recovery ratio.

     The equation which  best approximates the relationship between
the HRGC/MS  hexachlorobenzene  and GC/ECD corrected hexachloroben-
zene measurements is:

                     MS  = 0.13 * ECD0-56                      (7.21)

     The following  equation  relates the HRGC/MS  hexachlorobenzene
and GC/ECD  corrected hexachlorobenzene measurement  in the paired
1984  NHATS  samples,  and can  be  used for  extrapolation if the
recovery ratio can be assumed to be  constant:

                    MS = j~ = 0.47 * ECD                   (7.22)

Differences between the  geometric mean HRGC/MS  and GC/ECD  measure-
ments were significant,  indicating  that the  recoveries  for the two
methods are different.   Note that the corrected GC/ECD  hexachloro-
benzene measurements  were adjusted by the  laboratory to  correct
for low  recovery,  increasing  the  ratio  of  the GC/ECD to  HRGC/MS
measurements from 1.41 to 2.11.
                                81

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7.4   Plots of  HRGC/MS  versus 6C/ECD Measurements


     Plots of the HRGC/MS versus GC/ECD  concentration measurements

of primary compounds  in paired composite  human adipose tissue

samples are shown in Figures  9  through 17.   Figures 18 through  26

show the same data after the transformation, scaling, and  rotation

required for the statistical tests.   Each figure  shows:

     •    An open diamond (O), which indicates the paired HRGC/MS
          and GC/ECD measurements used to compare the two methods;

     •    A dotted line (- -), which serves as a  reference  line
          showing where the HRGC/MS and GC/ECD measurements are
          equal;

     •    A dashed line (— —), which indicates the best fit
          relationship between the HRGC/MS and GC/ECD measurements
          under the assumption that the HRGC/MS measurements are
          proportional to the GC/ECD measurements (i.e.,  the
          recovery ratio is constant, independent of concentra-
          tion) ; and

     •    A solid line (      ),  which indicates the best fit
          relationship between the HRGC/MS and GC/ECD measurements
          using equation 7.7.

     In addition, Figures 9 through 17 also show:

     •    A closed diamond  (•),  indicates paired HRGC/MS and
          GC/ECD measurements where at least one of the measure-
          ments was trace or not detected.  The plotted value is
          the LOD for non-detect measurements and the measured
          amount for trace measurements.
                                82

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                   1


                 0.9


                 0.8  4-


                 0.7


                 0.6
      HRGC/MS
     Measurement 0.5
        (ppm)

                 0.4


                 0.3


                 0.2
                 0.1  •-
                                  1—I—I—I—I—I—I—I
                     0   0.1  0.2  0.3  0.4  0.5  0.6  0.7  0.8  0.9   1
                                GC/ECD Measurement  (ppm)
            Trace or non-    O Paired data   """Best fit for
            detect for one     for comparing    paired data
            or both methods    methods
               Reference line,
               HRGC/MS equals
               GC/ECD
'Best  fit assuming
 HRGC/MS proportional
 to  GC/ECD
Figure 9.   HRGC/MS versus  GC/ECD concentration  measurements for
            p,p'-DDT  in  paired composite human adipose tissue
            samples.
                                  83

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  HRGC/MS
Measurement
            3


            2
            1 • •
                   H	1	1	1	1	1	I	1	1
                    123456789
                           GC/ECD Measurement (ppm)
        Trace or non-
        detect for one
        or both methods


        """ Reference line,
           HRGC/MS equals
           GC/ECD
                              1 Paired data   '
                               for  comparing
                               methods
•Best fit for
 paired data
                                       •Best fit assuming
                                       HRGC/MS proportional
                                       to GC/ECD
Figure  10.
        HRGC/MS versus  GC/ECD  concentration measurements for
        p,p'-DDE in paired  composite human adipose tissue
        samples .
                              84

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  HRGC/MS
Measurement
   (ppm)
            0.7
            0.6
            0.5
            0.4
            0.3
            0.2
            0.1 -•   *
                0     0.1    0.2   0.3    0.4    0.5    0.6
                           GC/ECD Measurement (ppm)
                                                                 0.7
        Trace or non-    O Paired data  ^^Best fit for
        detect for one    for comparing    paired data
        or both methods   methods
           Reference line,
           HRGC/MS equals
           GC/ECD
                                       'Best fit assuming
                                       HRGC/MS proportional
                                       to GC/ECD
Figure  11.
        HRGC/MS versus GC/ECD  concentration measurements for
        beta-BHC in paired  composite human adipose  tissue
        samples .
                              85

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  HRGC/MS
Measurement  0.1 +
   (ppm)
                      I	1	1	1	1	1	1	1	1	1
                 0  0.020.040.060.08 0.1 0.120.140.160.18 0.2
                            GC/ECD Measurement (ppm)
          Trace  or non-    O Paired data   ™^Best  fit for
          detect for one     for comparing   paired data
          or  both methods    methods
             Reference  line,
             HRGC/MS  equals
             GC/ECD
                                       'Best fit assuming
                                       HRGC/MS proportional
                                       to GC/ECD
Figure 12 .
         HRGC/MS versus GC/ECD concentration measurements  for
         dieldrin in paired composite human adipose tissue
         samples .
                               86

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  HRGC/MS
Measurement
   (ppm)
            0.35
             0.3
            0.25
             0.2
            0.15 • •
             0.1
            0.05
                 0    0.05   0.1   0.15   0.2   0.25    0.3
                            GC/ECD Measurement  (ppm)
                                                             \
                                                                0.35
        Trace or non-    O Paired data  ^^"Best fit for
        detect for one    for comparing    paired data
        or both methods   methods
           Reference line,
           HRGC/MS equals
           GC/ECD
                                       'Best fit assuming
                                       HRGC/MS proportional
                                       to GC/ECD
Figure 13.
        HRGC/MS versus  GC/ECD concentration measurements for
        heptachlor epoxide  in paired composite human adipose
        tissue samples.
                              87

-------
             0.2


            0.18


            0.16


            0.14


            0.12

  HRGC/MS
Measurement  0.1 +
   (ppm)
                      I—I—I—I—I—I—I—I—I—I
                 0  0.020.040.060.08 0.1 0.120.140.160.18 0.2
                            GC/ECD Measurement  (ppm)
 Trace  or  non-
 detect for  one
 or both methods


™~ Reference line,
   HRGC/MS  equals
   GC/ECD
                               Paired  data
                               for  comparing
                               methods
                                           Best fit  for
                                           paired data
                              "•"• "Best  fit  assuming
                                   HRGC/MS proportional
                                   to GC/ECD
Figure 14.
        HRGC/MS versus GC/ECD  concentration measurements for
        oxychlordane in paired composite human adipose  tissue
        samples .
                              88

-------
             0.4
            0.35
             0.3
            0.25


  HRGC/MS
Measurement  0.2 +
   (ppm)


            0.15
             0.1 • •
            0.05
                      H	1	1	1	I	1	1	1
                 0   0.05  0.1  0.15  0.2  0.25  0.3   0.35  0.4
                            GC/ECD Measurement  (ppm)
        Trace or non-   O Paired data  "••••Best  fit  for
        detect for one    for comparing    paired data
        or both methods   methods
           Reference line,
           HRGC/MS equals
           GC/ECD
                                       "Best fit assuming
                                       HRGC/MS proportional
                                       to GC/ECD
Figure 15.
       HRGC/MS versus GC/ECD  concentration measurements  for
       trans -nonachlor in paired composite human adipose
       tissue samples.
                             89

-------
            0.25
             0.2
            0.15
  HRGC/MS
Measurement
   (ppm)
             0.1
            0.05
                         0.05      0.1      0.15     0.2

                            GC/ECD Measurement (ppm)
                                                                0.25
        Trace or non-    O Paired data   """Best fit for
        detect for one    for comparing    paired data
        or both methods   methods
           Reference line,
           HRGC/MS equals
           GC/ECD
                                     "•"Best fit assuming
                                       HRGC/MS proportional
                                       to GC/ECD
Figure 16.
        HRGC/MS versus  GC/ECD concentration measurements for
        uncorrected hexachlorobenzene in paired composite human
        adipose tissue  samples.
                              90

-------
  HRGC/MS
Measurement
   (ppm)
            0.35
             0.3
            0.25
             0.2
            0.15
             0.1  • -
            0.05
                      0.05   0.1   0.15   0.2   0.25   0.3   0.35
                            GC/ECD Measurement  (ppm)
        Trace or non-    O Paired data  ^^Best fit for
        detect for one    for comparing    paired data
        or both methods   methods
           Reference line,
           HRGC/MS equals
           GC/ECD
                                     •"Best fit assuming
                                       HRGC/MS proportional
                                       to GC/ECD
Figure  17.
        HRGC/MS versus  GC/ECD concentration measurements for
        corrected hexachlorobenzene for recovery  in  paired
        composite human adipose tissue samples.
                              91

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    Ratio of
   HRGC/MS to
     GC/ECD
   Measurement
    0.1
               0.01
           111 nun  i  11 linn   i 11 nun  i 11 mm  i 11 nun
                 0.00001   0.0001
                       0.001
0.01
0.1
                  Product of Scaled HRGC/MS and GC/ECD Measurements
                                  (MS**.33) (ECD**3)
               Paired data
               for comparing
               methods

               ™  Reference line,
                 HRGC/MS equals
                 GC/ECD
                         •Best fit for
                         paired data
                         'Best fit assuming
                          HRGC/MS proportional
                          to GC/ECD
Figure 18.
Transformed HRGC/MS versus GC/ECD  p,p'-DDT measurements
used for  statistical tests.
                                  92

-------
                10
    Ratio of
   HRGC/MS to
     GC/ECD
   Measurement
               0.1
                         *
                                          *  o
                  0.01
               0.1
10
100
1000
                   Product  of  Scaled HRGC/MS and GC/ECD Measurements
                                  (MS**.33)(ECD**3)
             O Paired data
               for comparing
               methods

             ~" •" Reference line,
                 HRGC/MS  equals
                 GC/ECD
                         •Best  fit  for
                          paired  data


                         "Best fit assuming
                          HRGC/MS proportional
                          to GC/ECD
Figure 19.
Transformed HRGC/MS versus GC/ECD p,p'-DDE measurements
used for  statistical tests.
                                  93

-------
                10
    Ratio of
   HRGC/MS to
     GC/ECD
   Measurement
     1  • •
               0.1
                0.000001  0.00001   0.0001
                               0.001
0.01
0.1
                  Product of Scaled HRGC/MS  and GC/ECD Measurements
                                 (MS**.33)(ECD**3)
               Paired data
               for comparing
               methods

               "• Reference line,
                 HRGC/MS equals
                 GC/ECD
                         •Best fit for
                         paired data
                         'Best fit assuming
                          HRGC/MS proportional
                          to GC/ECD
Figure  20.
Transformed HRGC/MS versus GC/ECD  beta-BHC measurements
used for  statistical tests.
                                  94

-------
                10
    Ratio of
   HRGC/MS to
     GC/ECD
   Measurement
               0.1
                  1  I  I I INI
1—I  I  I I III
                 0.00001
                           0.0001
         0.001
                  Product of Scaled HRGC/MS and GC/ECD Measurements
                                  (MS**.33) (ECD**3)
               Paired data
               for comparing
               methods

               ™ Reference line,
                 HRGC/MS equals
                 GC/ECD
                         •Best  fit  for
                          paired  data


                         "Best fit assuming
                          HRGC/MS proportional
                          to GC/ECD
Figure 21.
Transformed HRGC/MS versus GC/ECD  dieldrin measurements
used for  statistical tests.
                                  95

-------
                10
    Ratio of
   HRGC/MS to
     GC/ECD
   Measurement
               0.1
                0.000001  0.00001
                      0.0001
0.001
0.01
0.1
                  Product  of  Scaled  HRGC/MS and GC/ECD Measurements
                                 (MS**.33) (ECD**3)
             OPaired data
               for comparing
               methods

             ~ ~  Reference line,
                 HRGC/MS equals
                 GC/ECD
                         •Best fit for
                         paired data


                         "Best fit assuming
                          HRGC/MS proportional
                          to GC/ECD
Figure 22.
Transformed  HRGC/MS versus GC/ECD  heptachlor epoxide
measurements used for statistical  tests.
                                  96

-------
                10
    Ratio of
   HRGC/MS to
     GC/ECD
   Measurement
     1 • •
               0.1
           I  I IN Mil	1 I 11 Hill	1 I 11 Illli	1 I 11 Hill
                0.000001
               0.00001
0.0001
0.001
0.01
                  Product of Scaled HRGC/MS and GC/ECD Measurements
                                  (MS**.33)(ECD**3)
               paired data
               for comparing
               methods

               — Reference line,
                 HRGC/MS equals
                 GC/ECD
                         •Best fit for
                          paired data


                         "Best fit assuming
                          HRGC/MS proportional
                          to GC/ECD
Figure  23.
Transformed HRGC/MS versus  GC/ECD oxychlordane
measurements used for statistical tests.
                                   97

-------
                10
    Ratio of
   HRGC/MS to
     GC/ECD
   Measurement
     1 • •
•
o
•
o
0 0
o o <*>
Jk —^ ^%
^f ^7 ^Bk»
O
               0.1 H—HHHHf—h++BW|—h+ttMH—M+Hftf—H-H+ttfl

                0.000001  0.00001   0.0001    0.001    0.01     0.1

                  Product  of Scaled HRGC/MS and GC/ECD Measurements
                                 (MS**.33)(ECD**3)
             O Paired data
               for comparing
               methods

             •" ~  Reference line,
                 HRGC/MS  equals
                 GC/ECD
                         •Best fit for
                         paired data
                         "Best fit assuming
                          HRGC/MS proportional
                          to GC/ECD
Figure 24.
Transformed HRGC/MS versus GC/ECD  trans-nonachlor
measurements used for statistical  tests.
                                  98

-------
                 10
    Ratio of
   HRGC/MS to
     GC/ECD
   Measurement
                                         iiinil  i  11inn   i 11nuu

                 0.000000 0.000001  0.00001   0.0001    0.001    0.01
                    1

                  Product of Scaled HRGC/MS and GC/ECD Measurements
                                  (MS**.33)(ECD**3)
             OPaired data
               for comparing
               methods

             ~ ~ Reference line,
                 HRGC/MS equals
                 GC/ECD
                         •Best  fit  for
                          paired data
                          "Best fit assuming
                          HRGC/MS proportional
                          to GC/ECD
Figure 25.
Transformed HRGC/MS versus GC/ECD uncorrected
hexachlorobenzene measurements  used for statistical
tests.
                                  99

-------
                10
    Ratio of
   HRGC/MS to
     GC/ECD
   Measurement
               0.1
          i i HIM—i i lima  i i iimil  i friinu—M-HHHI
                0.000001  0.00001   0.0001
                               0.001
0.01
0.1
                  Product  of  Scaled HRGC/MS and GC/ECD Measurements
                                 (MS**.33)(ECD**3)
               Paired data
               for comparing
               methods

               ~ Reference line,
                HRGC/MS equals
                GC/ECD
                         •Best fit for
                         paired data


                         "Best  fit assuming
                          HRGC/MS proportional
                          to GC/ECD
Figure  26.
Transformed HRGC/MS versus GC/ECD corrected
hexachlorobenzene for recovery measurements used for
statistical tests.
                                 100

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  8     COMPARISON  OF  6C/ECD  AND HRGC/MS MEASUREMENTS  ACROSS
                               YEARS
     One objective of the Comparability Study is to assess whether
the  relationship between the  HRGC/MS and GC/ECD  measurements in
1984 is useful  for  comparing trends in concentrations measured by
different  methods  in  different  years.    The  analysis  in  this
chapter uses the results from the  comparability analysis to adjust
the  1982  HRGC/MS measurements  for comparison to  the  GC/ECD mea-
surements using plots of the data.

     An  important consideration  in  the  decision as to  whether
measurements from  different years and methods can be  compared is
the  extent  to which  any  relationship  between  the  GC/ECD  and
HRGC/MS results from 1984 might apply to other years.  The statis-
tical  procedures  discussed in  Chapter  7  can be used  to find the
"best" relationship to  predict  the 1984 HRGC/MS  measurements from
the  1984 GC/ECD measurements.   However,  this relationship may not
be  best  for  data collected in  another  year,  data  analyzed by
another laboratory,  or  for samples with different  concentration
levels than  those for  which the  equation  was developed.   These
issues are discussed at the end of this  chapter.

8 . 1    Plots  of  Measurements  Over Time

     Figures 27 through 34  show the arithmetic average GC/ECD and
HRGC/MS  measurements  for  the  NHATS  design samples15  from 1970
through 1984.  The GC/ECD method was used in 1970 through 1981 and
in 1983 and  1984.   The HRGC/MS method was used  in 1982  and 1984.
For  these  two years/  both the HRGC/MS  average  and the adjusted
HRGC/MS  average  are  shown.    The averages  in the   figures  are
weighted by age group and therefore approximate national  averages.
15The design  samples are  those samples which  are within  the quota for the
hospital.  Some hospitals collect more samples than required under  their
quota.

                               101

-------
     The    adjusted HRGC/MS  average  approximates  the arithmetic
average  concentration which  would  have been  obtained  from  the
GC/ECD  method.    It  was  calculated  by multiplying  the HRGC/MS
average for a compound by the corresponding  geometric mean  ratio
of  the  GC/ECD  to HRGC/MS  measurements  in  Table  12,  referred  to
here as  the  adjustment ratio.   The  calculation  of  the adjusted
HRGC/MS average assumes that  the  ratio of the  GC/ECD to HRGC/MS
measurements observed in the ' FY84  samples  is  constant  across
years.  The adjusted  HRGC/MS  average for 1982  can be compared  to
the trend  in  the  GC/ECD data in the years around 1982 to  assess  if
this procedure  for adjusting  the HRGC/MS data  provides a reason-
able approximation to  the  averages  made  using  the GC/ECD method.

     In calculating the average for  the GC/ECD method,  zero  was
used for nondetect measurements and the  approximate LOQ16 was used
for trace  measurements.   For the HRGC/MS average,  the value  LOD/2
was used  for the  nondetect measurements and the  measured amount
for trace  measurements.    Differences in how the  trace  and non-
detect measurements  were  handled result  in  only small differences
between the GC/ECD and  adjusted HRGC/MS averages.   The adjusted
HRGC/MS averages  for  1984 and  corresponding GC/ECD  averages  for
1984 are  not exactly  equal due  to a combination  of  1)  different
procedures for  handling   nondetect  and trace  measurements,   2)
exclusion  from the averages  of specimens  from one hospital17,  3)
inclusion  of  measurements  from the  unpaired samples, and  4) use  of
only paired samples  to calculate the  adjustment ratio.  Note that
for dieldrin, only five paired measurements were used to  calculate
the adjustment  ratio.   However,  the GC/ECD average  for dieldrin  is
based on  42 measurements  and the  HRGC/MS  average  is  based  on  11
measurements.
16The procedures for recording the data have changed slightly over time.  The
majority of trace measurements use the LOQ.  Of the remaining cases,  some use
the LOQ/2,  some use a value higher than the LOQ.
17During the course of the Comparability Study, multiple specimens  from one
hospital were suspected of  coming from the same  donor.   It was  decided to
exclude specimens from this hospital from the comparisons across years.  For
the years in which composite samples were analyzed,  4 composite samples were
excluded from each of the 1982 and 1984 averages.
                                 102

-------
        0.4  -•
        0.2 -•
          0

IJ-— — «»»«ec
          1970   1972   1974
                   1976   1978

                      Year
1980   1982   1984
                •GC/ECD
                     •HRGC/MS  --*-  HRGC/MS
                                       adjusted
Figure 27.
Weighted average GC/ECD, HRGC/MS, and HRGC/MS adjusted
p,p'-DDT concentrations for design samples from 1970
through 1984.
                                103

-------
        8 T
        7 ••
        6 -•
      a
      04
      . 5 -•
      o
     -H
     -U
      (0
      s-l
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4 -•
3 -•
        2 -•
        1 -•
        0
         1970   1972
               1974    1976   1978

                         Year
           1980    1982    1984
                •GC/ECD
'HRGC/MS  --
                                        -  HRGC/MS

                                           adjusted
Figure 28.
   Weighted average GC/ECD, HRGC/MS, and HRGC/MS adjusted
   p,p'-DDE concentrations for design samples from 1970
   through 1984.
                                 104

-------
U T 	 1 	 1 	
1970 1972 1974






	 1 	 1 	
1976 1978
Year
1" — " HRGC/MS
	 1 	 1 	 r
1980 1982 1984

--^- HRGC/MS
adjusted

Figure 29.
Weighted average GC/ECD, HRGC/MS, and HRGC/MS adjusted
beta-BHC concentrations for design samples from 1970
through 1984.
                                105

-------
         0.3
        0.25
      e
      o.
      a
      G
      O
      0)
      o

      o
      u
0.2 -•
        0.15  -•
0.1 -•
        0.05  -•
U T 	 1 	 1 	
1970 1972 1974






— I ' l —
1976 1978
Year

^^ HRGC/MS
	 1 	 1 	 1
1980 1982 1984

- - O- - HRGC/MS
adjusted

Figure 30.  Weighted average GC/ECD, HRGC/MS, and HRGC/MS adjusted

            dieldrin concentrations for design samples from 1970

            through 1984.
                                106

-------
        0.16
      ^ 0.06  +
      c
      o
      CJ
        0.04  +
        0.02  -•
           o  -4
   +
+
+
•+
           1970   1972   1974   1976   1978   1980   1982   1984
                                  Year
•GC/ECD
 'HRGC/MS
                                                -  HRGC/MS
                                                   adjusted
Figure 31.  Weighted average GC/ECD,  HRGC/MS, and HRGC/MS adjusted
            heptachlor epoxide concentrations for design samples
            from 1970 through 1984.
                                107

-------
        0.16  -r
        0.14  -•
        0.12  -•

      Q.
      a

      .  O.i  4
      c
      o
     •H

     $ 0.08
      i-4
     4-J


      g 0.06

      o
     o
        0.04
       0.02  -•
U T 	 1 	 1 	
1970 1972 1974






	 1 	 1 —
1976 1978
Year
0 — -" HRGC/MS
	 1 	 1 	 r
1980 1982 1984

	 * - HRGC/MS
adjusted

Figure 32.
Weighted average GC/ECD, HRGC/MS,  and HRGC/MS  adjusted

oxychlordane concentrations for design samples from

1972 through 1984.
                                 108

-------
         0.2  -•



        0.18  -•



        0.16  -•



      0, 0.14  -•
      c 0.12
      o
     -H

     £01  +
      (u  v • U.
      g 0.08  -I-
      o
      c

        0.06  -•
        0.04  -•
        0.02  -•
                                                    +
                                             1	h

1970   1972   1974   1976  1978   1980   1982   1984

                       Year
•GC/ECD
                      HRGC/MS   --
                                                -  HRGC/MS

                                                   adjusted
Figure 33.  Weighted average GC/ECD, HRGC/MS, and HRGC/MS adjusted

            trans-nonachlor concentrations for design samples  from

            1975 through 1984.
                                 109

-------

0.18 j


0.16 -•


0.14 -•


0.12 -•
                                                       I
                                                       I
•S
-p
(0
_p
G
0)
o
c
o
o



0



0


0.1


.08



.06












        0.04  -•
        0.02  -•
               _i	,	.	1	•	1	.	,	,	,	1	1	•	f

           1970   1972  1974   1976  1978   1980   1982   1984

                                  Year
                •GC/ECD
HRGC/MS  - -
                                        -  HRGC/MS
                                           adjusted
Figure 34.
    Weighted average GC/ECD, HRGC/MS, and HRGC/MS adjusted
    corrected hexachlorobenzene concentrations for design
    samples from 1974 through 1984.
                                110

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8 .2   Assessment  of  the Results

     As  can be  seen  from Figures  27  through  34,  the 1982 adjusted
HRGC/MS  average is closer to the trend in the GC/ECD averages than
is  the unadjusted  average for  four  compounds,  p,p'-DDT,  p,p'-DDE,
heptachlor epoxide,  and  trans-nonachlor.   Although  the unadjusted
beta-BHC HRGC/MS  average approximates  the  trend  in the  GC/ECD
averages more  closely than' the adjusted value, both  the adjusted
and unadjusted beta-BHC values are close to the GC/ECD trend.   For
oxychlordane there were no 1982 measurements for comparison.

     For two compounds,  dieldrin  and corrected hexachlorobenzene,
the adjusted 1982 averages are considerably higher than the  neigh-
boring GC/ECD  averages.   The  1982 hexachlorobenzene  measurements
are  known to  have  two  very  high  observations, which might be
considered to  be outliers.  These  values  contribute to both  the
high average  concentration  and to  a high  standard error of  the
average.  The  difference  between  the HRGC/MS  adjusted average  and
the GC/ECD trend may be due to the presence of the outliers.   With
the two  outliers  removed,  the  adjusted  HRGC/MS average reasonably
follows the GC/ECD trend,  as  can be seen  in Figure 35.

     For dieldrin, the  adjusted  HRGC/MS average  does not  agree
well with the  trend  in the GC/ECD averages; however,  the differ-
ences  may be  explained  by variation  in  the estimates  of  the
HRGC/MS  average  and  the  adjustment  ratio.   The adjustment  ratio
from Table 12 has  a  wide  95% confidence  interval  due  to  the  small
number of paired measurements  in  1984.   In  addition,  the estimate
of  the 1984 HRGC/MS  average  is imprecise due to the  small  number
of  fraction  2  extracts analyzed   using  the  HRGC/MS method.
Although the adjustment  ratio results  in  poor agreement between
the  HRGC/MS  adjusted  averages and  the GC/ECD  trend,  there  are
values within the 95% confidence intervals  that would provide good
agreement.  Additional paired samples would be needed to provide  a
more precise ratio estimate  with  which  to  evaluate  the adjustment
ratio for dieldrin.
                               Ill

-------
       0.18 j


       0.16 ••


       0.14 -•


       I 0.12 -•


     0 0.1 -f
       0.08 -•
     c
     0)
     g 0.06 -4-
     o

       0.04 -•
       0.02 -•
           1970   1972   1974   1976  1978   1980  1982   1984
                                  Year
                •GC/ECD
HRGC/MS  	
                                    ~  HRGC/MS
                                       adjusted
Figure 35.
Weighted average GC/ECD, HRGC/MS, and HRGC/MS adjusted
corrected hexachlorobenzene concentrations for design
samples from 1970 through 1984, with two outliers
removed from the calculation of the 1982 HRGC/MS and
HRGC/MS adjusted average.
                                112

-------
     For all  compounds  tested,  given the  likely  errors  in estima-
tion of the yearly  averages  and the adjustment ratios,  the proce-
dure of using the adjustment ratio from the 1984 data  (Table  12)
and the average HRGC/MS concentration  from 1982 to approximate  the
1982 GC/ECD averages  cannot be  rejected  based on the data.   For
five compounds,   the  adjusted  HRGC/MS  average  was  close to  the
GC/ECD  trend.    For  one  other compound,  the  adjusted  HRGC/MS
average was close to  the  GC/ECD trend  after  removing two outliers
from the HRGC/MS  data.  For  one additional compound,  the data  and
the ratio  estimates were  too  variable to attribute  the observed
differences in the GC/ECD and HRGC/MS adjusted  averages to  the
adjustment  procedure.  For  one compound, oxychlordane, no 1982
measurements are available for comparison.

     Extending this procedure to other years requires making  the
assumption  that  the  ratio  of  the  GC/ECD  measurements  to  the
HRGC/MS measurements  is constant for all  years.   The relationship
between the  GC/ECD and HRGC/MS measurements might be  different
than in  1984  if the data were from  another year,  analyzed by
another laboratory,  or for  samples with different  concentration
levels than those from  which  the relationship was developed.   For
example,  for  the  1984 data,  statistically significant differences
between batches were found for four compounds: p,p'-DDE,  beta-BHC,
oxychlordane,   and trans-nonachlor.  If differences in the  sample
processing among  batches  within a  year can be significant,  there
may also  be  differences  in  the sample  processing between  years
which would affect the ratio of the  HRGC/MS to GC/ECD  measure-
ments .

     The comparability  results for p,p'-DDE, beta-BHC,  and  hexa-
chlorobenzene  suggest that in  some circumstances the  recovery  may
not be constant and independent  of concentration.   In this  situa-
tion,  the  ratio  correction factor will  depend  on  concentration.
Extrapolation to other concentration  levels  may  provide  quite
inaccurate results  when the  concentrations  substantially  change
from those observed in 1984.
                               113

-------
     An alternate procedure for comparing the  HRGC/MS  and GC/ECD
data is  to correct  all  measurements for recovery.    This  would
require good  estimates of recovery.   Measurements  on different
sample  matrices  provide  different   estimates  of  recovery.
Therefore,  care  must be  taken in selecting the  samples  on  which
the recovery estimates  are based.   The  recovery correction can be
done on  a sample  by  sample  basis,  perhaps  based  on  surrogate
compounds,  or by  adjusting   all  concentrations  using a  common
recovery value.   If the same  recovery ratio  is used to  adjust all
measurements  for a  selected   compound,   an  error in  the  recovery
ratio will result  in a similar error  in  the  average  or  median.
The results from this Comparability Study can be used to determine
the number of samples  required to achieve a  desired  precision in
the estimated  recoveries and the corresponding  averages.
                               114

-------
                           PCB  MEASUREMENTS
     This  chapter summarizes  the  PCB measurements  obtained using
the HRGC/MS  and GC/ECD  methods.   The PCB recovery measurements are
discussed  first,  followed by a comparison of  the PCB measurements
on  paired NHATS samples  using the HRGC/MS  and GC/ECD  methods.
Unlike  the primary  compounds  discussed  in  Chapter 8, the  proce-
dures for  reporting  PCBs  using the HRGC/MS  and GC/ECD methods were
quite different.  Therefore, although the concentration levels can
be compared,  it is  not possible to  determine  a  mathematical rela-
tionship between the GC/ECD and HRGC/MS measurements.

9.1   Comparison  of  PCB  Reporting and  Measurement
      Procedures

     PCB  reporting  procedures for the two analytical methods
differed   considerably.   In the GC/ECD method,  the  limit  of quan-
tification  for PCBs was  1  ug/g and the  PCB concentrations,  on  a
wet weight basis, were reported on the following interval scale:
     V = Not  detected;
     W = Detected, with a concentration between the LOD and the
           LOQ,  i.e., between .33 and 1 ug/g  (Equivalent to a trace
           measurement);
     Y = Detected, with a concentration of 1 to 3 ug/g; and
     Z = Detected with a concentration > 3 ug/g.

     The porcine adipose tissue samples  in  batches 4  through 10,
used  for  quality  control  in   the GC/ECD method,  had known  PCB
concentrations  of I  ug/g.   Only for  these GC/ECD  samples  were the
PCB concentrations reported in both  the  interval categories  listed
above and in ug/g wet weight18.
18For these samples  the measured  concentration, rather  than the limit of
quantification (LOQ), was reported, even though the concentrations were below
the LOQ.
                                115

-------
     Although the GC/ECD method measures PCB  concentrations  as  if
"PCB" is one compound, PCBs  comprise many  different  compounds  (or
congeners).  PCBs can be grouped into  10 classes  called homologs.
Each homolog class  includes the chlorinated biphenyl  compounds
with the  same  number of chlorine atoms (from 1  to  10) .  In the
HRGC/MS method,  the PCB concentration  within each homolog  class
was  reported.    The  average detection limit  for  each  homolog
concentration ranged from .01 to .03 ug/g  lipid weight.   Thus the
HRGC/MS measurements were much more sensitive than those  from the
GC/ECD  method  for  which  the detection  limit was  0.33 ug/g wet
weight.

     The GC/ECD concentrations for the  paired  samples,  reported  on
the interval scale,  could not be converted  from a  wet weight  basis
to a  lipid basis.   Therefore,  for  the analysis  in this  chapter,
PCB concentrations  from both methods  are expressed as  micrograms
per gram  wet weight.  Note  that this  is  different  than  for the
discussion in Chapter 7, which used concentrations on  an extract-
able lipid basis.

     Interpretation of differences between the HRGC/MS  and GC/ECD
measurements is complicated by differences  in  the  reporting proce-
dures and significant differences in the data  reduction procedures
for the two techniques.  The  GC/ECD analyses  of PCB's  is  based  on
comparison of a limited number of major peaks  associated with the
PCB response.  The HRGC/MS analysis  of  PCB's  is generally based  on
the area  sums  of the  peaks  for all  congeners at  each level  of
chlorination.

9.2   PCB  Recovery Using  the  HRGC/MS and  GC/ECD  Methods

     Recovery measures  the  proportion of  the PCBs in  the sample
which were detected by  the  measurement method.  The  PCB recovery
can be  estimated from measurements on samples which were spiked
with  PCBs.  These   samples  include the  porcine  tissue  samples
(measured using the GC/ECD method) and  the  dichloromethane samples
                               116

-------
and all  samples  spiked  with  surrogate  compounds  (measured with the
HRGC/MS  method).   Because the multisplit  samples  were not spiked
with PCBs, these samples cannot be used to estimate PCB recovery.

     For the  GC/ECD method,  only the porcine samples in batches 4
through  10 were  spiked  with  PCBs.  These porcine samples had known
PCB  concentrations of  1  ug/g.   The PCB  recovery  measurements in
these  seven  porcine samples  were,  in order:  68%,  75%,  75%,  78%,
80%,  80%, and  95%.   The average  recovery was  78%,  with  a 95%
confidence interval from 71% to 85%.   The PCB recovery was lower
than  for most  primary compounds measured in  the same  porcine
samples  (see Table  8).

     For the  HRGC/MS method,  the  PCB recovery  can be determined
from  measurements  on  the  dichloromethane spiked samples  and
measurements  of  the surrogate  compounds  in all  samples.   One
spiked  dichloromethane  sample  was analyzed with  each of  the 10
batches.   Because  the  HRGC/MS  method provides  a  measurement for
each  of  the  10 PCB homologs,  the  total  PCB  concentration was
determined by adding the measurements  for the homologs.  The total
PCB  concentration was  used  to  calculate  recovery19.   The
dichloromethane  samples  were spiked  with  equal amounts  of  each
homolog.   This  even distribution  of PCBs  among homologs  may not
reflect  the   distribution of  PCBs found  in naturally  occurring
samples.   In  addition,  recovery of  PCBs  from dichloromethane may
be different  than  that  from lipid material.  The PCB recoveries
for the  10 dichloromethane samples were,  in order: 57%, 61%,  62%,
65%, 69%, 70%,  74%, 75%, 79%, and 80%.   The average recovery was
69%, with a 95%  confidence interval  from 64% to 75%.  This recov-
ery  is  similar to  or  higher than that for most  other compounds
spiked into the dichloromethane samples (see Table  8).
19As  can  be  seen in Table A-2  in  the  appendix,  the PCB recovery  in the
Dichloromethane spiked samples tended to increase as the number of chlorine
atoms increased,  ranging from 51% to  83% recovery.

                                117

-------
     Another  means  of estimating the  PCB  recovery  using  the
HRGC/MS method uses  surrogate  compounds  which were added to  each
sample.  Four  stable isotope labeled surrogate compounds,  repre-
senting PCB  homologs with 1,  4,  8,  and  10  chlorine atoms,  were
spiked into all HRGC/MS lipid samples at  levels of  2,  5,  8,  and 10
micrograms  respectively.   Because  PCB recovery may  be  different in
the presence of lipid,  only measurements  on  adipose tissue samples
were used  to  estimate  PCB recovery.   The recovery estimates  are
for the sum of the  four  surrogate homologs.  Figure 36 shows  a
histogram  of the PCB recovery measurements for lipid samples.   The
average recovery for the  surrogate PCBs  in  the 57  adipose  tissue
samples was 62%,  with a 95% confidence interval from 59% to  66%.

     The PCB recovery estimates for the HRGC/MS and GC/ECD methods
are summarized in Table 13.   The average  recoveries of spiked  PCBs
using the  HRGC/MS  and the GC/ECD methods ranged  of  62% to  78%,
with  the   GC/ECD  recovery  slightly  higher than the  HRGC/MS
recovery.

9.3   Comparison  of HRGC/MS  and GC/ECD  Paired  Measurements

     In order to compare the HRGC/MS and  GC/ECD measurements,  the
total HRGC/MS  PCB concentration  across  all  homologs was  calcu-
lated.   Figure 37  shows a histogram of the HRGC/MS  PCB  concentra-
tions for  paired samples.   The  corresponding  GC/ECD coded concen-
trations are shown  using shading.

     As can be  seen  from  Figure 37,  the  HRGC/MS measurements  are
skewed, with many  low measurements and  a few high measurements.
The sample  with the largest  measurement had a  concentration  of
1.29 ug/g,  almost twice the next highest  concentration in a  paired
sample.  The GC/ECD  concentration  for this sample  was between  1.0
and 3.0 ug/g,  in  agreement  with  the  HRGC/MS measurement.    The
remaining  GC/ECD measurements  were between  .33 and 1.0 ug/g  and
the corresponding  HRGC/MS measurements  ranged from nondetect  to
0.67 ug/g,  with an average of  0.28 ug/g  and  a standard deviation
                               118

-------
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Table 13.  Summary of PCB Recovery  Measurements
Analytical
method
GC/ECD
HRGC/MS
Sample type
Porcine samples
Dichloromethane
samples
Surrogate
compounds
Mean recovery
78%
69%
62%
95%
Confidence
interval
71% to 85%
64% to 75%
59% to 66%
                      120

-------
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-------
of 0.24  ug/g.  Two-thirds  of the HRGC/MS measurements  were below
0.33  ug/g,  indicating  that  concentrations  measured  using  the
HRGC/MS method are generally below those from the GC/ECD method.

     A cross tabulation of the  GC/ECD and HRGC/MS data  in paired
samples,  coded using  the  same  interval  categories as the GC/ECD
data,, is  shown in Table 14.   The GC/ECD and  HRGC/MS  measurements
are in the same concentrations  intervals  for 15 samples.   In the
remaining  30 paired samples,  the GC/ECD measurements are  in the
range  from  0.33  to  1.0 ug/g and  the HRGC/MS  measurements  are
lower, less than 0.33 ug/g.

     The  PCB measurements  on the multisplit  samples  can  also be
used  to  compare  the  HRGC/MS and GC/ECD  methods.   Because  the
spiking  solution  contained no PCBs, the PCB measurements  on all
five multisplit samples  for each of three composites  were compar-
able.  For these  samples,  all GC/ECD measurements  were  within the
0.33 to 1.0  ug/g  range20.  Table  15  shows the number of  multisplit
samples with  HRGC/MS  measurements either below, in the  same range
as or above  the  GC/ECD  measurement.   As  can be  seen  from the
table, the HRGC/MS measurements  are  either below  or similar to the
GC/ECD measurements.   No HRGC/MS measurements  fall into  a concen-
tration category  greater than that for the paired  GC/ECD measure-
ment .

     The  data do not  allow  a determination  of  the  ratio  of the
HRGC/MS to the GC/ECD measurements  due to  the interval  nature of
the reported data and the  small range of the  reported  concentra-
tions.  However, the results  for the paired  and multisplit samples
supports  the conclusion that  the ratio of  the GC/ECD to HRGC/MS
measurements  is greater than 1.0.
20For one unspiked multisplit sample, one of two extract PCB measurements had
a coded  concentration between  1.0 and 3.0  ug/g, higher than the other two
measurements on the  same  extract.   This measurement  was ignored for  this
analysis.   Its inclusion  would not change  the result  that  the  HRGC/MS
measurements are less than  or similar to  the  GC/ECD measurements.
                                122

-------
Table 14.   Coded HRGC/MS  versus Coded GC/ECD PCB Measurements in
            Paired  Samples
Coded HRGC/MS
measurements
        Coded GC/ECD measurements

Not Detected  .33 to 1 ug/g   1 to 3 ug/g
Not Detected or less
than 0.33

.33 to 1 ug/g
1 to 3 ug/g
                    30
                    14
                                123

-------
Table 15.   Comparison of HRGC/MS  and GC/ECD Measurements  in
            Multisplit Samples

Composite3
A
B
C
Number of
HRGC/MS lower
than GC/ECD
5
1
lb
HRGC/MS measurements
HRGC/MS
Similar to
GC/ECD
0
4
3
HRGC/MS
greater than
GC/ECD
0
0
0
Note.  All GC/ECD samples had coded PCB measurements between 0.33
and 1.0 ug/g.

aOther than distinguishing composites from which the multisplit
samples were prepared, the composite identifier has no meaning.

 In one sample, five homologs could not be measured.  Because the
HRGC/MS concentration based on the remaining five homologs may be
significantly lower than the actual PCB concentration, it has been
left out of the total for this cell.
                                124

-------
    10  ANALYSIS OF  PRECISION  AND  COMPONENTS  OF  VARIANCE
     Precision refers to the variability of  the measurements.   The
variability may  be measured  in  terms of the variance, standard
deviation,  or coefficient of variation.  An analysis of precision
attempts to quantify the  variability  of  the  measurements  and
factors  which affect  that variability.   The precision  of  the
measurements  may be  a function  of concentration,  the compound
being measured,  and/or factors associated with the  sample process-
ing steps,  such  as  the batch in which a sample is analyzed.

     The model in Chapter 5 was used as  a basis  for the analysis.
The variance  components are estimated  using  only  the positively
quantified  measurements, unless otherwise indicated.  Thus  samples
with trace  measurements and measurements  below the  detection  limit
are not included in the analysis  of variance  components.  Although
the decision  to  use only  the  positively quantified measurements
and the  rounding of  the reported GC/ECD concentrations will have
some affect  on  the  variance  estimates,  the  importance of  these
factors is  expected to be  small.

10.1  Standard  Deviation  Versus  Mean

     Experience  has  shown that,  for  concentration  data  which
cannot  be negative, the measurement error increases with the size
of the  measurement.  If the data  have  a lognormal distribution, as
assumed in  Chapter 5,  the  standard deviation  of the data will be
linearly related  to the concentration as:

                           O = K  C                          (10.1)
                               125

-------
where :

      G = the standard deviation of the measurements;

      K = the proportionality constant; and
      C = concentration being measured.

      For measurements on  a  group of  samples  with  the  same  expected
concentration,  equation (10.1)  can be approximated by:

                          Sj = K Xj                          (10.2)

where :
      Sj = the standard deviation of the measurements in group j;

      Xj = the average of the measurements in group j .

      Taking the log of both sides of equation (10.2) gives:

                    ln(S) = ln(K) +  ln(X)                   (10.3)
     The  variance  of ln(Sj) is  roughly inversely proportional to
the degrees  of  freedom.   Therefore, the following equation can be
fit  using weighted regression,  with  the weights  equal  to the
degrees of freedom:

                   ln(Sj)  = a + yln(xj)  + e.                  (10.4)

     For each compound, the slope, y,  can be tested to  determine if
equation  (10.3)  fits the data.   The  assumption that the data can
be described by a  lognormal distribution  is  consistent with the
data if the  confidence interval  for y includes  1.0.  An  assumption
that the measurements  have a constant variance is consistent with
the data if the confidence interval for y includes  zero.
                                126

-------
     Data  from  the  spiked multisplit and paired samples were used
to  estimate the  slope in  equation  (10.4).   These  analyses  are
described in the following sections.

     10.1.1  Spiked  multisplit  samples

     Four  spiked multisplit samples were prepared at each of three
spiking  levels.   The mean 'and  standard  deviation of measurements
for  the  three spiking  levels  were used to estimate the parameters
in equation (10.4) .   The mean  and  standard deviation were calcu-
lated using the following formulas:
                             -
                      Mean = Xj = - ; —                      (10.5)
                                     nj
                                   / X"1     —
     Standard Deviation = s^ = -\  / ^(xij~xj)2              (10.6)
                                        nrl

where:
     j = the spike level, 1 = lowest spike level,  3  =  highest
           spike level.
     nj = the number of split samples at  the j*-*1 spike level, in
           this case nj = 4 for all spike  levels unless some
           measurements are missing; and
     x^j = the measured concentration for the i*-*1  sample,
           i = 1 to 4, for samples with the jtn spike level.

     The  estimates  of  standard  deviation,  Sj,  each  have  three
degrees  of freedom  and  thus are  not very  accurate.   With  only
three  spiking levels, there is  only one  degree of  freedom  for
calculating confidence  intervals for y.    Under this  condition,  a
precise  estimate of  y is obtained only  if the  spacing  between
spiking  levels  is large.  Equation (10.4) was fit to the spiked
                                127

-------
multisplit  data using  all primary  compounds  except  PCBs.21  The

results are presented in Section 10.1.3.


     10.1.2  Paired  samples


     The  procedure used  for  the multisplit  samples was  modified

for the  paired samples  because there  are  no paired samples with

the  same  expected  concentration.   Instead,  the  residuals  and

predicted  values  from regression  were used  to estimate  the mean

concentrations and standard deviations  for  application  of equation

(10.4) .


     The analysis assumed  that  the  relationship between the GC/ECD

and HRGC/MS measurements followed the simple regression model:


                  xMcs "  Ac + BC*XECS + error                  (10.7)


where:

     XMCS = measurement  for compound c in  sample s  using the
            HRGC/MS method;

     XECS = measurement  for compound c in  sample s  using the GC/ECD
            method;

     Ac =   intercept for the  linear relationship between measure-
            ment methods  for measurements  on compound c; and

     Bc =   slope  for the linear relationship  between measurement
            methods for  measurements on compound c.


The steps in the analysis  were:

     (1)  Fit the model  in equation  (10.7)  to all  samples  with
          positively quantified values  for  each method,  determine
          the residuals  and predicted values;

     (2)  Order the  predicted values  from smallest to largest;

     (3)  Divide the predicted  values into  four groups  from
          smallest to largest;
21  p,p'-DDT was  not  spiked  in the  multisplit  samples,  however, the
concentrations  in the  unspiked  samples had a great  enough range to fit
equation (10.4).


                                128

-------
      (4)   For  each group, calculate the average predicted HRGC/MS
           concentration, Xj, and the standard deviation of the
           residuals, Sj/ and

      (5)   Use  the four values of Xj and Sj to fit equation (10.4)
           and  estimate 7.

     Equation  (10.7) was used to estimate  the  relationship between
the standard deviation  and  the  concentration for HRGC/MS measure-
ments.  The following similar equation was used  to estimate preci-
sion for the GC/ECD measurements:

                  XECS = Ac + BC*XMCS +  error                  (10.8)

     Although  the results of these  procedures  are  only approxi-
mate,  they provide  more degrees  of  freedom for estimating the
standard  deviations and more  data  values  for  fitting equation
(10.4) .

     10.1.3   Results

     Plots of  ln(Sj) versus ln(Xj)  were prepared for all  compounds
and groups of  comparable  measurements  derived from the multisplit
and paired samples.  From the plots, the  following conclusions are
evident:
     •     The  estimates of 7 for each compound are not  very
           precise; and
     •     There is a general increase in variability  with increase
           in concentration.

     To  estimate a  more stable  confidence  interval  for  7, the
calculations  used a pooled variance  across  all compounds.   To
provide a possibly more precise  estimate of 7,  a  pooled slope  esti-
mate was also  calculated under the assumption  that the slope  7 was
identical for all compounds.
                                129

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Figure 39.
Slope of the  linear  relationship between the log of the
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measurements  on multisplit spiked and paired samples.
                                 131

-------
     Figures 38 and 39 show confidence  intervals for y based on the
multisplit  spiked  samples and point  estimates of y based  on the
paired  samples.   Because  the measurements within  each group  of
paired  samples are  not true replicates, confidence intervals for
the slopes  would be approximate and  are therefore not  reported.
Results for each primary compound and  the pooled  estimate  for all
compounds  are  shown.   Figure  38  shows  estimates  for   GC/ECD
measurements.  Figure  39  has estimates  for HRGC/MS measurements.
On both figures,  a  dotted  line marks a slope  of  1.0  corresponding
to a lognormal distribution.

     As can be seen  from Figure 38  and 39,   the results   do not
contradict the assumption  that the  data have a lognormal distribu-
tion.    Most  confidence intervals include a slope of  1.0,  consis-
tent with the  lognormal distribution,  and fewer  confidence  inter-
vals include a slope  of zero, consistent with a  normal  distribu-
tion.    The  confidence  interval for the pooled estimate  is  consis-
tent with the  assumption that the errors have a  lognormal  distri-
bution.  For subsequent analyses, the error  components are  assumed
to have a lognormal distribution, or equivalently, the  log  trans-
formed data are assumed to have a normal  distribution.

10.2  Components  of  Variance

     A  components of variance analysis divides the variance  of the
measurements into components which  can be associated  with specific
sample processing steps.   The components add  together.   Thus, the
variance of the measurement error is  the sum of the variance asso-
ciated  with  differences between  batches and  the  variance  associ-
ated  with  differences between  measurements on  samples   within
batches.   The calculation of variance components uses the log
transformed measurements  from the QC  samples, the spiked  multi-
split  samples,  and the surrogate  compounds.
                               132

-------
     The  model  for the data, equation  (5.10), includes  components
of variance associated with:
     •     Differences between batches, a^-,;
     •     Differences between batches which depend on the internal
           standard used to  quantitate the measurement, 0^,^  (this
           term  is  confounded with O^., in the GC/ECD data) ;
           Differences between batches which depend on the internal
           standard  and  compound being quant itated, CT^lc  (this term
           only applies  to the HRGC/MS data) ;
           Differences between samples within batches which are the
           same for  both the HRGC/MS and GC/ECD measurements, o|;
           Differences between samples within batches which are
           different  for the HRGC/MS and GC/ECD measurements,
           Differences between measurements quantitated using the
           same internal standard within a sample, O^g^  (this term
           is  confounded with O   in the GC/ECD data) ;  and
      •    Unexplained measurement error, cr^.

      Not  all of  these  components  can  be estimated  using every
subset  of data.   For most  subsets  of  the data,  the following two
variance components can  be  estimated,  1)  a between-batch  component
(°mb + ambi  (+ °mbic for the HRGC/MS data))  and  2)  a within-batch
component (o| + (J^s + ^msi + °m) •    ^ne second of these components
is the  sum of the individual  components  associated with samples,
internal  standards  within samples,  and unexplained measurement
error.

     There  is no one  optimal way  to estimate the  components of
variance.   Calculation procedures  based  on different assumptions
about  which  estimation  criteria  to  optimize  give  different
results.   Estimation of the variance  components  was performed on
the computer using SAS PROC VARCOMP  (SAS  1985) which provides  four
calculation procedures.   In order  to  show  the range of estimates
which  might be  achieved using  different  estimation procedures,
                                133

-------
Tables  16  through  19  present  results  using  three procedures
labeled MIVQUE, Type I,  and REML22.  Discussions of the results are
based  on the  restricted  maximum  likelihood  (REML) procedure,
because this method provides  information on the precision  of the
variance component  estimates which  are  useful  for  interpretation.
The discussion notes  if the interpretation might change  based on
results from the  MIVQUE  or  Type  I methods.

     All  of the variance components  are  estimated  under  the
assumption that  the magnitude  of  the components are  the  same for
all compounds.    To  the  extent that  this  is  not  true,  the  variance
components represent the average component  across compounds.   The
summary tables in Appendix  A provide  information on  each  compound
separately.   In  general,  compounds within either  Fraction  1 or
Fraction 2 appear to have similar  variability.   However Fraction 1
compounds appear to have  less  variability than  Fraction  2  com-
pounds.  Therefore, separate results  are  presented for Fraction 1
and Fraction 2  compounds.

     10.2.1  Components  of  variance  for the HRGC/MS
              measurements

     Eleven surrogate  standards were injected  into  all but  3 of
the 80 samples  analyzed using the  HRGC/MS method.  Eight of the 11
surrogate  compounds  were  measured in  Fraction  I   and three in
Fraction 2.   Because Fraction  2  compounds  were analyzed  only in
three batches,  less data are available  for  estimation of  variance
components for Fraction 2  compounds.  The  spiked dichloromethane
and multisplit samples also  provide data  for  estimation  of the
overall measurement variance.
22The unrestricted maximum likelihood procedure was not selected due to the
longer computation times required and similarity to REML.

                               134

-------
          Variance  components  for  HRGC/MS  Surrogate
          compounds in  Fraction  1

     The model for determination of the components of variance for
Fraction 1 compounds is:
  ln(XMcbsi>  = ln%    +  ^i*    c +  **> +   s1  +     + £Mcbsi

where 8. =  6   + 6
                   S .

     This  model  has components  associated  with batch, batch  and
internal standard, batch, internal standard, and compound,  samples
within batches, internal standards within samples,  and unexplained
                                                       / Sc \
error.   The fixed effects  for  different compounds,  Inrr - ,  are
                                                       VRMcty
removed before estimating the variance components.

     The model assumes that  (1)  the measured concentrations in  the
blank  samples,  dichloromethane  spike   samples,  and composite
adipose tissue samples differ due  to different  recoveries  in each
sample matrix  and (2)  after correcting  for differences in recov-
ery,  the variance  components  are the  same  for  all  subsets  of
measurements.   The  differences  among  the sample  matrices  are
statistically  significant  (based  on the output from  the type  I
components of variance analysis, p <.05) .  The measured concentra-
tions  in  the  blank  samples are  lower than  in  the  other  two
matrices .

     The estimated  components of variance are shown in Table  16.
All  components  except  the  batch component are significantly
different  from zero.   However, the component  for batch  effects
which differ by internal  standard  is significant.  Thus there  are
significant differences  between batches.  The differences depend
on  the  internal  standard  used to quantitate  the measurements.
Visual inspection of the  residuals indicates  that  the  unexplained
variance is  greater for some compounds  than others.  Therefore,
                                135

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these  variance  estimates  represent an  average  across all  com-
pounds .

     For  Fraction 1 compounds, the  estimate of  within-batch vari-
ance is  .062.23   The  estimate  of between-batch variance is  .030.24
Thus  variation   within  batches   (due  to  measurement  error  and
differences between samples)  contributes more to  overall  measure-
ment error than  differences  between batches.   Adding  the  compo-
nents  together,   the  overall  measurement error  variance  for  the
surrogate compounds  in Fraction 1  is  .092.    The  corresponding
coefficient of variation of an untransformed measurement is 31%.

     The  residuals from the analysis of variance had  several  low
observations which might be designated  outliers.   To determine  the
effect  of the more extreme observations on  the variance  compo-
nents,  the cases  with  the lowest six  residuals were  removed  and
the  components  were  recalculated.   With  the  extreme  outliers
removed there  were no changes  in the conclusions and  the overall
measurement error variance estimate drops slightly  to  .083  with a
corresponding coefficient  of  variation  for the  untransformed
measurements of  29%.   The  results based on all measurements were
used in subsequent  summaries.

           Variance  components   for HRGC/MS  Surrogate  com-
           pounds  in  Fraction 2

     The  model for determination  of  the components of variance is:

       ln(XMcbsi)   =  l«      + 5Mb'  + We + SMS!' + ^Mcbsi     (10.10)
23This is the  sum of the  REML variance estimates for the three within-batch
variance components, .012, 022,  and .028.
24This is the  sum of the REML  variance  estimates for the two between-batch
variance components, .011, .014, and 005.

                                137

-------
where
           = 8S + ^MS  +  $Msi '  ttle variance  associated with both
           sample differences and differences in the within-sample
           internal standard responses .   This term is assumed to
           have a variance of o| + o^s + GMSJ_; and
          = $Mb + 6^1 , the variance associated with batch differ-
           ences which are common to all measurements  in  a batch
           and which are associated with internal  standards .   This
           term is assumed to have a variance  of O  +
     In this  model,  the sample and internal  standard  effects are
combined  because  all  surrogate  compounds  in  Fraction  2  were
quant itated on the same internal standard.   The model assumes that
the average  concentration in  the  blank samples/  dichloromethane
spike samples, and adipose tissue  samples  differ  due to different
recoveries in the each sample matrix.

     This model  has  components  associated with batch,  batch and
compound,  samples within  batches, and unexplained error.   The
                                         / Sc  \
fixed effects for different compounds,  In  _   , are removed before
estimating the variance components.   The estimated variance compo-
nents are shown in Table 17 .

     The component associated with differences between samples and
internal standards within samples  is significantly  greater than
zero.  The variance components for sample and unexplained error in
Fraction 2  compounds  are much  larger  than  for  Fraction  1 com-
pounds.  For Fraction 2 compounds, the overall estimate of within-
batch  variance is  .749.   The overall  estimate of  between-batch
variance is  .006.   Adding the  components  together,  the  overall
measurement error variance for the surrogate compounds in Fraction
2 is  .754.  The  corresponding  coefficient of variation of one un-
transformed measurement is 106%.   Differences between measurements
in different  batches  is due  mostly to  differences  between the
samples within batches and not  differences between the  batches.
                                138

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          Variance  components  for  HR6C/MS  spiked
          dichloromethane   samples

     One spiked  dichloromethane  sample  was  analyzed in each batch
using the HRGC/MS method.   Because  there  is only  one  sample per
batch,  the  within-batch and between-batch components for  the
dichloromethane  samples cannot  be  estimated independently using
the dichloromethane data.  The model  for the  data is:
        ln(XMcbsi) = Inr--  + 5^+8 + 8^+^. + eMcbsi-        (10.11)
where:
                 + ^Ms"1" ^s a random effect  associated with batch b
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          o ; and

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          have a variance of
     The variances  for Fraction  1  and Fraction 2 compounds differ
for the QC compounds.  On  the  assumption  that they may differ for
the dichloromethane samples also,  Fraction 1 compounds  are ana-
lyzed separately.  This model has components associated with batch
and sample, internal  standards,  and unexplained  error.   The fixed
                                      f sc \
effects  for different  compounds,  In _.   ,  were removed before
estimating the  variance components.   The estimated components of
variance are shown in Table 18.
                                140

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     The  overall  measurement error variance  for  Fraction 1 com-
pounds in the dichloromethane spiked samples  is  .095.   This corre-
sponds to a coefficient of variation for an untransformed measure-
ment of 32%.  The  component  associated  with  internal  standards  is
significant, indicating that compounds  within a  sample quantitated
using the same  internal  standard will have correlated measurement
errors.

     The  recovery  measurement for  p,p'-DDE  in batch 2  is 155%.
This  measurement  is  unusually  large   compared to  the  recovery
measurements  for  p,p'-DDE  in dichloromethane  samples  in other
batches.  Because  removing this measurement  from the  calculations
reduces the  overall  error variance by  less  than  2%, the results
for all data including this unusual value are reported.

     Because  Fraction 2  compounds were  analyzed in only three
batches, relatively little data was available to estimate variance
components.   Therefore,  only the  overall measurement error vari-
ance  for  the log  transformed measurements  is  estimated for the
Fraction 2 compounds.  As with the  surrogate  compounds,  the vari-
ance  for  Fraction  2  compounds  is greater  than  for Fraction 1
compounds.  The overall measurement error variance for Fraction 2
compounds in  the dichloromethane  spiked samples  is  .396.   This
corresponds  to  a  coefficient  of  variation  for an untransformed
measurement  of 70%.

          Variance  components  for  Fraction  1 compounds in
          the  spiked multisplit  samples.

     Due to  the sample design for  the  multisplit  samples (three
composites,  each with  four splits  analyzed in  three  batches),
estimated variance  components would have  few degrees of freedom
and not be very precise.  Therefore, only the overall measurement
variance was  calculated  for  the  multisplit samples.   The average
measurement error variance for the  log  transformed measurements  on
primary Fraction  1 compounds in the spiked multisplit  composite
                               142

-------
samples  is  .176.   The corresponding coefficient  of  variation for
the untransformed concentration is 44%.

     10.2.2  Components  of  variance  for  the  GC/ECD
              measurements

     Aldrin was injected into all but 3 of the 86 samples analyzed
using the GC/ECD method.  The  recovery  for the  aldrin can be used
to estimate overall batch and sample effects.  Porcine samples and
spiked multisplit  samples analyzed in  each batch can be  used to
estimate the overall measurement variance.

          Variance  components  for GC/ECD  aldrin
          measurements

     Only one compound/ aldrin, was spiked into the GC/ECD samples
to  estimate recovery.   Because  there  is  only  one  compound per
sample,  only the within-  and  between-batch components of variance
can be estimated.   Therefore,  the model  is:
               ln(XEcbs) = ^      +  b' + £Ecbs             (10.12)
where S^1  is the between-batch component and EEcbs  is the within-
batch component .

                                                      f sc "N
     The  fixed effects  for  different compounds,  In p y ,  were
removed before estimating the variance  components.   The estimated
components of  variance are  shown in  Table  19.   The model  fit  to
the aldrin measurements  assumes  that  the  average concentration  in
the blank samples, porcine fat samples,  and adipose tissue samples
differs even  though the spiking  levels were the same.   Although
the differences between  these groups  of samples  are not statisti-
cally significant  (p <.058), the  differences were  modeled because
they were close to significant  and differences  were found  in the
HRGC/MS measurements.
                                143

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     For  the  aldrin data,  the  between-batch variance  component,
 .003,  is  not  significantly  greater  than zero.   The estimate  of
 within-batch  variance  is .022.   Adding the components  together,
 the  overall measurement error variance  for  aldrin  is  .025.   The
 corresponding  coefficient of variation  of  an untransformed  mea-
 surement is 16%.

          Measurement   error variance   for  the  GC/ECD  porcine
          fat   samples

     The  model  for the  QC  samples is  simpler  than  that for  the
 HRGC/MS measurements because only  one internal  standard was  used.
 As  with the  dichloromethane samples,  with only one sample  per
 batch, the batch and sample components of variance cannot be  esti-
 mated  independently.   Therefore, only the  overall measurement
 error variance, pooled across spiked  compounds,  was estimated from
 the  porcine  sample  data.    Because  the porcine  fat samples  had
 different sources and spiking levels for the first 3 batches  than
 the  last  7 batches,  the error estimates were made separately  for
 batches 1 to  3 and 4 through 10.

     For  the  porcine fat samples  from  batches 1, 2,  and 3,  the
 overall measurement error variance for the log  transformed data is
 .009.  The corresponding  coefficient of  variation for an untrans-
 formed measurement is 10%.

     For  the  porcine fat samples  from  batches  4  through 10,  the
 overall measurement error variance for the log  transformed data is
 .008.  The corresponding coefficient  of  variation  for the untrans-
 formed concentration is  9%.

          Variance  components  for  GC/ECD  measurements on
          Fraction  1  compounds  in  the spiked  multisplit
          samples

     Because  only one of the Fraction 2 compounds (dieldrin)  had
positively quantified  measurements,  variance  estimates from  the
                               145

-------
GC/ECD  spiked  multisplit samples were  calculated  for Fraction  1
compounds only.  On the  assumption that the variances are  similar
for  all primary Fraction 1 compounds  in the  spiked multisplit
composite samples,  the overall measurement error variance  for  the
log  transformed  data  is  .013.   The  corresponding coefficient of
variation for the untransformed concentration is 11%.

10.3 Summary  and  Comparison  of BRGC/MS and GC/ECD
      Precision

     Based on the analysis of standard deviation versus concentra-
tion and  plots of the  data,  the variability of  the HRGC/MS  and
GC/ECD measurements increases as the  concentration being measured
increases.  The decision to model the  log  concentration and assume
that the  log transformed concentration had constant variance  was
based on the relationship between the standard  deviation and mean
concentration from spiked multisplit and paired  samples, theoreti-
cal considerations,  and observations on the residuals from analy-
ses using the  log  transformed data.    Modeling  of  the log trans-
formed  data  results  in  a statistical model  which  is relatively
easy to fit  to the data  using standard statistical techniques.

     Where possible,  the overall measurement  error  variance  was
divided into components of variance  associated with sample pro-
cessing steps.   Of particular interest is  the  within- and between-
batch estimates of variance.  The within-batch  component of vari-
ance is the  variance  of measurements  on split  samples within  the
same batch.   This includes errors associated with sample handling
within  a  batch  or day, injection  of  internal standards,   and
quantitation.   The between-batch component  of variance  is  the
portion of the measurement variance which  is attributed to  differ-
ences  between batches.    This   includes  errors associated with
calibration  and  possible  changes in  the preparation of different
solutions  or  equipment  setup   between batches  or  days.    The
between-batch components of variance and overall measurement  error
variance for the transformed data are  summarized in Table 20.   The
                               146

-------
Table 20.   Summary of Variance Components  for GC/ECD and HRGC/MS
            Measurements
Analysis method and
subset of the data
HRGC/MS
HRGC/MS
HRGC/MS
HRGC/MS
HRGC/MS
GC/ECD
GC/ECD
GC/ECD
GC/ECD
Surrogate
compounds
Fraction 1
Surrogate
compounds
Fraction 2
Spiked
di chlor omet hane
samples
Fraction 1
Spiked
dichloromethane
samples
Fraction 2
Spiked multi-
split samples
Fraction 1
Aldrin
Porcine fat
samples Batches
1 to 3
Fraction 1
Porcine fat
samples Batches
4 to 10
Fraction 1
Spiked multi-
split samples
Fraction 1
Log transformed
measurements
N Between Overall
batch error
variance variance
620 .030
66 .006
428
27
82 Assumed
zero
73 .003
27
62
84 Assumed
zero
.092
.754
.095
.396
.176
.025
.009
.008
.013
Untransformed
measurements
Coef. of 95%
variation Pred.
Interval
31%
106%
32%
70%
44%
16%
10%
9%
11%
±61%
±208%
±62%
±137%
±86%
±31%
±19%
±17%
±22%
N is the number of measurements from which the variance components
     are estimated.

The within-batch variance component can be calculated from the
     difference between the between batch and overall variance,

The 95% prediction interval for an individual measurement is
     calculated as 1.96 times the coefficient of variation.
                                147

-------
overall measurement variance is also expressed as  a coefficient  of
variation for an untransformed  measurement.

     The  components  of variance  were estimated  from different
subsets of  the data according to  how the samples were prepared.
The components  based  on different  subsets may differ due  to  many
factors including 1) different  compounds analyzed  in  the different
subsets, 2)  different  matrices in  which the  compounds are found,
and  3)  different  analytical  procedures  used  to process  the
samples.

     The variances  were  calculated  assuming that all compounds  in
the subset of  data  being analyzed have  the same variance.   Review
of the data  suggests that  this assumption is  not unreasonable but
that some  differences  may exist.   Therefore,  the components  of
variance and overall variance represent averages across compounds.
Additional information  about individual compounds  can be  obtained
from the tables in Appendix A.

     As  can be  seen  in Table 20,  measurements   for  Fraction  2
compounds using the HRGC/MS procedures have  greater variances  than
those for Fraction  1 compounds.  The  three variance  estimates for
Fraction 1 compounds are .092,   .095,  and  .176.  The  estimates for
Fraction 2 compounds are .396 and .754.

     Both the  Fraction  1 and Fraction 2 compounds measured using
the HRGC/MS procedures  have overall  variance  estimates  greater
than those  for  the GC/ECD  measurements.   The  overall  variance
estimates for  the  GC/ECD  compounds are all  less  than  .03.  The
HRGC/MS overall variance  estimates range from .092  to   .176 for
Fraction 1  compounds.   As  a rough  rule of thumb,   the variance  of
the HRGC/MS  measurements is nine times  greater than that  of the
GC/ECD measurements, resulting  in a coefficient  of  variation three
times  greater  for the  HRGC/MS measurements  than  the GC/ECD  mea-
surements.  Based on a  weighted average  of the  variance estimates
                                148

-------
in  Table  2025  approximate  95%  prediction  intervals  for  the
Fraction 1 GC/ECD and HRGC/MS measurements are 22% and 63% respec-
tively.

      Systematic differences between  batches  are  small relative to
the differences between measurements on samples in the same batch.
However,  batch differences  for the  HRGC/MS measurements  on  the
surrogate compounds are statistically significant.  Therefore,  the
calculation  of recovery and  determination  of  the  relationship
between  the  GC/ECD and HRGC/MS measurements  (Chapters 6  and 7)
accounted for the presence of any  batch  effects.

      The  proposed  model for  the  data hypothesizes sample  and
internal  standard  effects,  in addition  to batch  effects.   The
statistical  analysis  indicates that both the  sample  and internal
standard  effects  are  significant for  the  HRGC/MS measurements.
Although these effects can be ignored for this Comparability Study
and many uses of  the  data,  comparison of  measurements for differ-
ent compounds in the same sample and different samples in the same
batch  must   take  these  variance  components  into  account.
25The average was calculated using the number of measurements,  N, for the
weights.

                               149

-------
                         11   REFERENCES
Fuller W.  1987. Measurement Error Models. New York: Wiley, pp.
       1-48.

Mack G.  1986. Battelle Columbus Division. Statistical Design and
       Analysis  For a Study  to  Determine Whether  Changing
       Analytical  Methods  Has a Significant Effect  on Estimates  of
       Baseline  Levels and Time Trends. Washington,  DC: Office  of
       Pesticides  and Toxic  .Substances, U.S.  Environmental
       Protection  Agency.  Document No. NHATS-SS-03.  Contract No.
       68-02-4243.

Mack G., Panebianco D.  1986. Battelle Columbus Division.
       Statistical Analysis  of  the FY82 NHATS Broad Scan Analysis
       Data,  Draft Final Report, August 1986.  Washington,  DC.
       Office of Toxic Substances, Exposure Evaluation Division,
       U.S. Environmental  Protection Agency.  Document No. NHATS-SS-
       03.  Contract No.  68-02-4243.

Mack G., Mohadjer  L.  1985. Baseline Estimates and Time Trends for
       Beta-benzene hexachloride, Hexachlorobenzene, and
       Polychlorinated Biphenyls in Human  Adipose Tissue 1970-1983.
       Washington,  DC: Exposure Evaluation Division, U.S.
       Environmental Protection Agency. Document No. NHATS-SS-01.
       EPA  Pub.  560/5-85-025.

Remmers J.    1987.   Letter from J. Rammers to J.  Tessari,  October 1,
       1987.  Washington, DC: Office of Pesticides and Toxic
       Substances,  U.S. Environmental Protection Agency.

Robinson, P.  E., Mack, G.  A., Remmers,  J., Levy,  R., Mohadjer,  L.,
       1990,  "Trends of PCB, Hexachlorobenzene, and j3-Benzene
       Hexachloride Levels in the Adipose  Tissue of the U.S.
       Population," Environmental Research 53, 175-192, Academic
       Press, Inc.

SAS.  1985.  SAS User's Guide: Statistics,  Version 5. Gary,  NC:  SAS
       Institute,  Inc.

Sherma J.,  Beroza  M.  1980.  Analysis of Pesticides Residues in
       Human  and Environmental  Samples: A  Compilation of Methods
       Selected  for use in Pesticide Monitoring Programs.
       Washington,  DC: U.S.  Environmental  Protection Agency. EPA-
       600/8-80-038,  Section 5,  A,  (a), pp. 11-19  (1980).

Stanley J.    1986.   MRI. Preparation of the FY 1984 NHATS Composite
       Samples for the Method Comparability Study,  Final Report.
       MRI  Report,  June 17,  1989. Washington, DC: Office of
       Pesticides  and  Toxic  Substances, U.S.  Environmental
       Protection  Agency. MRI Report, June 17, 1989. Contract No.
       68-02-3938.
                                151

-------
Stanley J. 1985.  Analytical Method for the Determination of
       Semivolitile Organic  Compounds  in Human Adipose Tissue,
       Draft  Interim Report  vl, Revision 3, December 20,  1985.
       Washington, DC:  U.S.  Environmental Protection Agency.
       Contract  No. 68-02-3938, Work Assignment 8.

USEPA.  1986. U.S. Environmental Protection Agency. Comparability
       Study  of  Analytical Methodology for TSCA Chemicals in Human
       Adipose Tissue,  Quality Assurance Program Plan.  Washington,
       DC: Office  of Pesticides and Toxic Substances, U.S.
       Environmental Protection .Agency.

USEPA.  1988. U.S. Environmental Protection Agency. Computer
       printout  from NCC: Historical averages for NHATS samples.
       Oct. 13,  1988.   Washington, DC:  Office of Pesticides and
       Toxic  Substances, U.S. Environmental Protection Agency.

USEPA.  1990. U.S. Environmental Protection Agency. Computer
       printout  from NCC: Historical averages for NHATS samples
       without hospital 313.  Mar. 26,  1991.  Washington, DC:
       Office of Pesticides  and Toxic  Substances, U.S.
       Environmental Protection Agency.
                                152

-------
APPENDIX  A:   SUMMARY  DATA TABLES
                153

-------
Tables  A-l through A-18 summarize the  measurements used  in the
Comparability  Study.    The  following  guide  is  provided  to help
locate the appropriate table:

Tables A-l to A-7 summarize the HRGC/MS measurements:
     A-l           Method blanks;                        page 156
     A-2           Spiked dichloromethane samples;        page 158
     A-3           Paired composite samples;             page 160
     A-4 to A-6    Spiked multisplit samples; and        page 162
     A-7           Surrogate compounds in all samples.    page 168

Tables A-8 to A-18 summarize the GC/ECD measurements:
     A-8           Method blanks;                        page 169
     A-9 and A-10  Porcine fat samples;                  page 170
     A-ll          Paired composite samples;             page 172
     A-12          Aldrin in all samples;                page 173
     A-13 to A-15  Spiked multisplit samples; and        page 174
     A-16 to A-18  Extracts of unspiked multisplit
                   samples.                              page 177
                                155

-------







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EH
H-
O
cu
*•-
M?
EH
a
cu
number (
:ed.
+->
±) T~l
TJ
(0 (0
"•o
•O-H
o> a
C --H
-H rH
M-l

0) C
•6 4>
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4J C
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0) U
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U rH
CU rH
CU <
179

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APPENDIX  B:   CONVERSION  FROM  STANDARD DEVIATION OF  LOG
      TRANSFORMED  DATA  TO COEFFICIENT OF  VARIATION
                               181

-------
When fitting  models to the  log transformed  data,  the estimated
error variance is  for the  transformed  data.   It may be desirable
to  convert  the variance  in the  log  scale  to a  coefficient of
variation in  the  original  scale.   The following formula relates
the variance of the log data, s2, to the coefficient of variation
of the  original measurements, cv:
                       cv = Vexp(s2)-l                        (B.I)

     For  reference,  Table  B-l  tabulates  the  coefficient  of
variation for selected values of s.
                                  183

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Table B-l.  Coefficient of Variation  for the Untransformed Data for
           Selected Values of s,  the Standard Deviation of the Log
           Transformed Data
s
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0.00
.000
.100
.202
.307
.417
.533
.658
.795
.947
1.117
1.311
0.02
.020
.120
.223
.328
.439
.557
.685
.824
.979
1.154
1.353
0.04
.040
.141
.243
.350
.462
.582
.711
.854
1.012
1.191
1.396
0.06
.060
.161
.264
.372
.485
.607
.739
.884
1.046
1.230
1.441
0.08
.080
.181
.286
.394
.509
.632
.767
.915
1.081
1.270
1.487
            The coefficient of variation  is  read at the
            intersection   of  the   "row"   and  "column"
            defined  such  that  the  value in the  left
            column of the  "row"  and the top  row in the
            "column"  add to s.
                                  184

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APPENDIX  C:   RECOVERY  FROM  MULTISPLIT  SAMPLES
                          185

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Table C-l.  Recovery for  Spiked Compounds  in Multisplit Samples
           Analyzed Using the HRGC/MS Method.
Compound
p,p'-DDE (a)


Beta-BHC


Dieldrin (b)


Heptachlor Epoxide


Oxychlordane (c)


trans-Nonachlor (a)


Hexachlorobenzene
(uncorrected)

Average
Recovery
±95%
Confidence
Interval
26±50%


99±50%


37±61%


50±50%


42±50%


56±50%


41±50%

Average
Recovery
by Spike
Level
23%
42%
14%
201%
32%
64%
43%
-
31%
46%
59%
46%
37%
76%
14%
48%
88%
31%
53%
32%
38%
Spike
Level
(ug/g)
1.00
3.00
5.00
0.10
0.20
0.40
0.20
0.40
0.60
0.10
0.20
0.30
0.10
0.15
0.20
0.10
0.20
0.30
0.05
0.08
0.11
Recovery
Based on
Samples
in Same
Batch
42%
45%
9%
322%
30%
196%
42%
-
62%
47%
45%
42%
42%
78%
23%
96%
104%
38%
56%
41%
35%
Recovery
Based on
Samples in
Different
Batches
5%
39%
19%
81%
34%
-68%
44%
-
—
44%
72%
49%
33%
73%
4%
-1%
72%
24%
49%
23%
41%
(a)  Unspiked sample for mid spike level  is  missing  (footnote  says
    "compound is  present but cannot be quantitated" and no  LOD was
    provided),  replaced by  0.  As a result  of  substituting  zero  for
    the  missing unspiked concentration,  the calculated recovery
    will tend to  overestimate of  the actual recovery.
(b)  Unspiked sample for low spike level  is  below the  LOD,  replaced
    by LOD/2.   Between  batch recovery for low  spike level based  on
    a single value.
(c)  Unspiked sample for low spike level  is  below the  LOD,  replaced
    by LOD/2.
                                  187

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Table C-2.  Recovery  for Spiked  Compounds in  Multisplit Samples
           Analyzed Using the GC/ECD Method.
Compound
p,p'-DDE


Beta-BHC


Dieldrin


Heptachlor Epoxide


Oxychlordane


trans -Nonachlor


Hexachlorobenzene
(corrected)


Hexachlorobenzene
(uncorrected)

Average
Recovery
±95%
Confidence
Interval
82±19%


89±19%


90±19%


83±19%


73±19%


73±19%


77±19%


53±19%

Average
Recovery
by Spike
Level
64%
98%
82%
85%
91%
92%
88%
95%
88%
75%
99%
76%
73%
83%
64%
45%
104%
69%
80%
100%
52%
55%
63%
41%
Spike
Level
(ug/g)
1.00
3.00
5.00
0.10
0.20
0.40
0.20
0.40
0.60
0.10
0.20
0.30
0.10
0.15
0.20
0.10
0.20
0.30
0.05
0.08
0.11
0.05
0.08
0.11
Recovery
Based on
Samples
in Same
Batch
49%
97%
74%
75%
93%
99%
98%
95%
91%
80%
103%
75%
80%
80%
63%
65%
100%
75%
60%
106%
36%
56%
69%
32%
Recovery
Based on
Samples in
Different
Batches
80%
100%
90%
95%
90%
85%
78%
95%
85%
70%
95%
77%
65%
87%
65%
25%
107%
63%
100%
94%
68%
60%
56%
50%
                                  188

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Table C-3.  Recovery for  Spiked Compounds  in  Multisplit Samples
           Analyzed  Using  the  GC/ECD  Method,  with  Unspiked
           Concentrations Measured in Extracts
Compound
p,p'-DDE


Beta-BHC


Dieldrin


Heptachlor Epoxide


Oxychlordane


trans-Nonachlor


Hexachlorobenzene
(corrected)


Hexachlorobenzene
(uncorrected)

Average
Recovery
±95%
Confidence
Interval
85±14%


89±14%


90±14%


84±14%


73±14%


81±14%


82±14%


51±14%

Average
Recovery
by Spike
Level
75%
94%
84%
85%
91%
91%
88%
94%
89%
80%
96%
76%
75%
80%
65%
65%
103%
77%
83%
94%
69%
55%
63%
36%
Spike
Level
(ug/g)
1.00
3.00
5.00
0.10
0.20
0.40
0.20
0.40
0.60
0.10
0.20
0.30
0.10
0.15
0.20
0.10
0.20
0.30
0.05
0.08
0.11
0.05
0.08
0.11
Recovery
Based on
Samples
in Same
Batch
49%
97%
74%
75%
93%
99%
98%
95%
91%
80%
103%
75%
80%
80%
63%
65%
100%
75%
65%
100%
75%
50%
69%
32%
Recovery
Based on
Samples in
Different
Batches
101%
92%
95%
95%
90%
84%
78%
94%
87%
80%
90%
77%
70%
80%
68%
65%
105%
78%
100%
88%
64%
60%
56%
41%
                                 189

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190

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APPENDIX  D:   DESCRIPTION OF  THE  ANALYTICAL  PROCEDURES
                               191

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D. 1  Summary  of  the  MOG-GC/ECD  Procedure

     The  modified Mills  Olney Gaither  (MOG)  packed  column gas
chromatography/electron capture  detector (PGC/ECD)  procedure was
used  for a  number of  years  as  the  standard NHATS  method for
detecting and  quantifying  chlorinated pesticides  and  PCBs  in
samples  of  adipose tissue.   The MOG-PGC/ECD  procedure has  five
major components:
      (1)  Extraction;
      (2)  Partitioning;
      (3)  Florisil clean-up;
      (4)  Dilution; and
      (5)  Identification and quantitation.

     For  the 1984  Comparability Study, the normal extraction  step
was modified.   Extraction of composite samples was carried out  by
MRI.   The extracts were split,  with one portion reserved for the
MOG-PGC/ECD analysis and another portion reserved for  the HRGC/MS
analysis.   The MOG-PGC/ECD  procedure in the  1984  Comparability
Study is  described below.    This  description  is for single-split
composite samples, the  multisplit composite samples, the  extracts
of the multisplit composite samples,  and the  regenerated composite
samples  and their method blanks.

     In the extraction  step, a 25 gram composite was placed  in a
culture  tube.   Next,   10  milliliters  (mL) of  methylene  chloride
were  added  to  the culture  tube,  and the entire  contents of the
tube were blended in  a Tekmar tissuemizer.   The blended mixture
was  allowed to separate and  the methylene  chloride  layer was
transferred to  a  filter funnel containing glass  wool and sodium
sulfate.  The mixture  resulting  after eluting through the filter
funnel was collected in a 100 mL flask.
                                  193

-------
     Another  10  mL  of  methylene  chloride  were  added  to the
sediment,  and the process of blending,  separating,  transferring  to
funnel, and storing in flask was repeated.  The addition of  10  mL
of methylene chloride  were repeated one or two more times.

     The  culture tube was  rinsed  with methylene  chloride, the
rinse  mixture  added  to  the funnel,  the funnel  rinsed,  and the
final  output from the funnel was  added to the 100  mL flask.   A
sufficient amount of methylene chloride was added to the flask  to
bring the  volume of  the mixture in the flask to 100 mL.

     For  some  samples,  particles remained in  the mixture in the
flask.  These  samples were  refiltrated,  and the final volume for
these samples was 200  mL.

     The percentage of the  composite sample that  was lipid tissue
was  determined  as  follows.   A glass  vial  was  weighed  to the
nearest 0.0001  gram.  One mL of  the final mixture was  transferred
to the vial.   The volume of the mixture  in  the vial  was reduced
until only an  oily  residue  was  left.   The vial was again weighed
to the nearest 0.0001 gram, and the  percentage of the composite
sample that was lipid  material was calculated.

     Of the remaining volume for the sample,  approximately 20% was
designated  for  the MOG-PGC/ECD procedure,  with the remainder
designated  for the HRGC/MS procedure.    The  appropriate volumes
were concentrated to remove  most  of the  methylene  chloride.   These
concentrated volumes  were  transferred  to vials  for shipment  to
CSU.

     After receipt  at CSU,  1 microgram of the  internal standard,
aldrin, was  added or "spiked"  to  the  sample  designated for the
MOG-PGC/ECD method.   The  sample  was then ready for the next  step
in the procedure.
                                  194

-------
     In the partitioning step,  the sample  was  transferred to a  125
mL  separatory  funnel by  rinsing with hexane.   Next,  hexane  and
acetonitrile were  mixed together in a separate container.  About
30  mL  of the  hexane/acetonitrile mixture  were  added  to   the
separatory  funnel.   The funnel was shaken  for 2  minutes,   and
layers were allowed to separate.   The lower  layer  was then  drained
into a  1-liter  separator to which  550 mL of  a 2% sodium  sulfate
solution  and  100  mL  of hexane  had previously been added.    The
process  of  adding  30 mL of  hexane/acetonitrile mixture  to  the
funnel,  shaking the  funnel  for 2  minutes,  allowing  layers to
separate, and draining  the  lower layer into the 1-liter separator
was repeated three  more  times.

     Next,  the  1-liter  separator was inverted,  and  layers were
allowed to separate.  The lower layer was  discarded.  The contents
of  the  1-liter  separator were  washed  twice  with  100 mL of 2%
sodium  sulfate  solution.   After  separation of  layers  in   the
1-liter separator,  the  lowest  layer was  discarded.   Excess water
was drained.  The remaining contents of the  1-liter separator were
transferred to a 500 mL boiling flask.  The  contents of this flask
were reduced to 3 to 5 mL through rapid evaporation.

     The third step was  Florisil cleanup.  A  reservoir column  was
prepared with one half  to 1 inch of  sodium sulfate on the  bottom,
4 inches  of Florisil in the  middle, and an  inch and  a  half of
sodium  sulfate  on  the  top.   A  solution  of  6% diethyl  ether in
hexane  and  a  solution  of 15%  diethyl ether  in  hexane  were
prepared.

     Approximately  100  mL of  hexane was added to the  column  and
allowed  to  flow through the  column.    This   "rinse"  hexane   was
discarded.  A 500  mL  boiling  flask labeled  "6%" was placed under
the column to collect the output of the  column.   Then  the 3 to  5
mL from the partitioning step was added to the column.   Next,  200
mL of the 6% solution  mix were added to the column.
                                  195

-------
     At this  point,  a  500  mL boiling   flask labeled  "15%" was
prepared with 0.2 micrograms of aldrin.   When the 6% solution mix
reached the sodium  sulfate at  the top  of the  column,  the 6%
receiver boiling  flask  was  replaced with the  15%  boiling flask.
Two hundred mL  of the  15 percent solution mix were  added to the
column, and the  column  was allowed to  drain into  the receiver
flask labeled 15%.

     The  contents of  both the  6%  receiver flask  and  the 15%
receiver  flask  were   reduced  to  3  to  5 mL  through  rapid
evaporation.

     In the  dilution step,  the  contents of  the  6% and  the 15%
flasks were  transferred to separate  centrifuge tubes.  A volume of
hexane was  added to each centrifuge tube in accordance  with an
established  algorithm.

     In the identification  and  guantitation  step,  a 5-microliter
sample from each  centrifuge tube was injected separately into a
gas chromatograph.   Compounds were  identified by  their retention
time relative to  the chromatographic "peak"  representing aldrin.
Concentrations of compounds  were quantitated based on the area of
the peak representing the  compound.

     The limits of quantification and the limits  of detection for
the  MOG-PGC/ECD  method  have been established  from  years  of
experience  with the  method.  For samples for which quantitated
concentrations  were  less than  the  limit  of detection,  a final
concentration of  zero  was  reported.   For samples  for  which the
quantitated concentration was between the  limit  of  detection and
the  limit of  quantification, the  limit  of quantification was
reported  as  the  final  concentration of  the sample.   The label
"Trace" was  assigned in this case.   For all other  samples, the
quantitated  concentration  was reported as  the final concentration.
                                  196

-------
     The  recovery of aldrin was calculated as a quality assurance
step on  the  entire  process.   However,  this recovery was not used
to  correct  the  compound concentrations.    Concentrations  for
hexachlorobenzene  and,  in  some cases,  mirex and  p,p'-DDT were
computed on  both  a  corrected and  an uncorrected basis.    The
correction  factors  were  based  on  historical information on  the
recovery  of  these three  compounds  at  the partitioning step.  The
concentrations  of  the other  chemicals were  not  corrected  for
recovery.

D . 2 Summary of  the  HRGC/MS  Procedure

     In  order  to  expand the list  of chemicals  that could  be
monitored by the  NHATS,  the  standard  NHATS  method for detection
and  quantification  of chemical compounds  was changed  from MOG-
PGC/ECD  to  High Resolution Gas Chromatography/Mass Spectrometry
(HRGC/MS).  The  HRGC/MS procedure has five major  components:
     (1)  Extraction;
     (2)  Gel permeation chromatography;
     (3)  Florisil clean-up;
     (4)  Addition of internal standards; and
     (5)  Identification and quantification.

     The  HRGC/MS procedure used in  the  1984 Comparability Study is
described below.   The description is  for  single-split composite
samples.     The   same   basic  procedure,   with  appropriate
modifications,  was  followed  for the  method blanks,  the  quality
control   samples,  the  multisplit  composite  samples,  and  the
regenerated composite samples  and their method blanks.

     The  HRGC/MS  procedure   began  with  the  extraction   step
described for the MOG-PGC/ECD procedure.  A volume designated for
the HRGC/MS procedure was  placed in a vial and shipped  to CSU.
                                  197

-------
     After  receipt  at CSU,  the  sample volume  was  spiked with  a
known amount  of  the  11 surrogate compounds.  The extraction  step
was continued as  follows.

     Twenty mL of methylene chloride were  added  to the sample,  and
the  sample  and  the  methylene  chloride  were  transferred  to  a
culture tube.  The  rest  of the extraction step followed  the  same
process as  described in the extraction step for the  MOG-PGC/ECD
procedure.   Ten mL of methylene chloride were added  to the culture
tube, and the entire contents of the tube  were blended in  a Tekmar
tissuemizer.  The blended  mixture was allowed to separate into  a
sediment and  a methylene  chloride layer.   The  methylene  chloride
layer was transferred to a filter funnel containing  glass  wool  and
sodium sulfate.  The mixture resulting from  the filter funnel  was
stored in a 100-mL flask.

     Another  10  mL  of  methylene  chloride were  added  to  the
sediment,  and the process of blending, separating, transferring to
funnel,  and storing in flask was  repeated.   The addition  of  10 mL
of methylene chloride and the  subsequent steps  were repeated 1 or
2 more times.

     The culture tube  was rinsed  with  methylene  chloride,  the
rinse mixture added  to  the funnel,  the  funnel  rinsed,  and  the
final output  from the funnel  was added  to  the  100-mL flask.   A
sufficient  amount of methylene chloride was  added to  the  flask to
bring the volume  of  the mixture in the flask to 100 mL.

     The mixture  in  the  flask  was transferred to a 250-mL
evaporator.  The volume in the evaporator was reduced to  a volume
of 10 to 20  mL.   The contents of the evaporator  were transferred
to a vial.   A sufficient amount of methylene chloride  was  added to
the vial  so that the vial  contained about  0.25 grams of  lipid
material for each mL of mixture.  The mixture in  the  vial was  the
final extract,  and this extract was passed to the next  step.
                                  198

-------
     The   next  step  in   the   procedure  was   gel  permeation
chromatography.   The  purpose of  this  step was  to separate  the
target analytes  from the lipid  material.   A column of  Bio-Beads
SX-3 in dichloromethane was  prepared.

     The  sample  extracts were  filtered  to remove particles  that
might  interfere  with the flow  of  the  column.   Then the  extracts
were transferred to the column in successive aliquots.   The output
of the column was collected  in amber bottles sized 1 to 4 liters.

     The  contents of  the  amber bottles were transferred to an
evaporator, and  reduced  to  a volume  of about 10 mL.  Next, 50 mL
of hexane  were added to the  evaporator.   Then the volume  of  the
evaporator was reduced, first to 10 mL and then to 1 mL.

     If the  1  mL volume  was unusually colored or viscous,  2  to  5
mL of methylene chloride  were added,  and the resulting volume  of  3
to 5 mL  was  injected into  the  column.   The output of the  column
was  collected in  amber  bottles,  placed in the  evaporator,   and
again reduced to 1 mL.

     A Florisil  column was  prepared  by placing glass wool  at  the
bottom of the  column, adding  100 mL of hexane  to  the column,  then
adding 12.5 grams of Florisil, and finally placing a one-half  inch
layer  of  sodium  sulfate  on top  of the  Florisil.   The hexane  was
drained so that  the  top  of  the  hexane  was level  with the  top of
the sodium sulfate.

     The  1 mL mixture from  the  previous step  was transferred to
the top of the column.   A 500 mL flask  was placed  underneath  the
column.  The column was drained until the sodium  sulfate  layer was
almost exposed.   At  that point,  200  mL of a 6% solution  of ethyl
ether  in  hexane were   added  to  the  column  at  a   rate  of
approximately 5 mL per minute.   When the sodium sulfate  layer  was
almost exposed, the  flask underneath the column was  replaced  with
a second 500 mL flask,  and 200 mL of  a 15% solution  of ethyl ether
                                  199

-------
in hexane were added to the column.   When  the  sodium  sulfate  layer
was almost  exposed,  200 mL of  a 50% solution of  ethyl ether in
hexane were added  to  the  column,  without  any  change in the  flask
under the column.

     After the column drained  into the  second  collector  flask, the
output of the column was contained in two separate 500  mL  flasks.
The contents of the  first  collector  flask are referred to as the
"first  fraction",  and the  contents  of  the second  flask are
referred  to as  the  "second  fraction".   The  first  and  second
fractions were concentrated separately,  first  to  5 to 10 mL, then
to less than 1 mL,  and then to  less  than  one-half mL.  The  final
volume of each fraction  was  stored in  two  separate  vials with
Teflon-lined  screw tops.   The  vials  were stored at  4  degrees
centigrade.

     The  volumes  for  the  two  fractions were  each  reduced  to 0.2
mL.   Known  amounts of the three  internal standards, anthracene-
dlO,  naphthalene-d8, and benzo  (a)  anthracene-d!2,  were added to
each  of  the fractions.   A 1-  to 2- microliter aliquot  of each
fraction  was   injected   into  the   gas   chromatograph/mass
spectrometer.

     The  final step was identification and quantification.    There
were 57 target analytes for the  HRGC/MS analysis.  Associated with
each of the 57 were a primary  mass fragment and two secondary mass
fragments.  In addition, one  of the  three internal standards was
designated as  the appropriate internal standard  for each target
analyte for purposes  of identification and quantification.

     In order for an analyte to be identified, the following four
criteria had to be satisfied:
      (1)   The primary  and  secondary masses had to achieve  their
          maximum values within  a  specified time span;
      (2)   The  retention  time  of  the primary and  secondary mass
          fragments relative to  the  designated internal standard
                                  200

-------
          had  to be  within  10 seconds  of  the  known  relative
          retention time of the  analyte;
      (3)  The  relative abundances  of  the primary  and secondary
          masses  all  had  to  be   within 20%  of  the  relative
          abundances in the reference spectrum of the analyte;
      (4)  The  abundances  of  the primary and secondary masses all
          had to exceed three times the background signal to noise
          ratio.
     Quantitation   was   carried  out   as   follows.      The
chromatograph/mass spectrometer was  calibrated.   In calibration,
known  amounts  of the  target  analyte and  a  known amount  of  the
internal  standard  were  injected  into  the  instrument.    The
calibration  was  done with  five  different  amounts of  the  target
analyte.   A  relative response  factor was  calculated for  each
analyte from the  calibration  data for use in  quantitation.   The
lower  calibration  limit  was  also  used  in  quantitation,   as
described below.

     For  a  composite  sample,  the amount  of the target  analyte
present was  calculated from the  abundance  of the primary  mass of
the analyte,  the abundance of the primary mass  of  the internal
standard, the  amount  of  the internal standard,  and  the relative
response  factor.   This  calculated amount was  given a label of
"Positive  Quantifiable"  if  the  abundances  of  the primary  and
secondary masses  all  exceeded 10 times the  background  signal to
noise ratio and the  lower calibration limit was exceeded.  A label
of  "Trace"  was to be assigned to an  amount  for which 1)   the
primary and secondary masses were  all above three  times the signal
to noise  ratio,  2)    one  of  the  primary or  secondary  masses  was
between 3 and  10  times the  background  signal to noise ratio,  and
3)  the lower calibration limit was exceeded.

     For samples for  which 1)   at  least one of the mass abundances
was less  than  three  times  the signal to noise ratio  and 2)   the
lower calibration limit  was  exceeded,  a label  of "Not  Detected"
was assigned.   The  detection  limit was the  maximum  of  the lower
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calibration limit and three times the signal to noise ratio.   For
samples for which there  was  no  response or the response was  less
than the  lower  calibration limit,  a label  of  "Not Detected"  was
assigned.   The  detection limit was  the lower  calibration  limit.
For samples for which a response above the  lower  calibration limit
was observed,  but the  identification  criteria  were not  met,  a
remark  was made  indicating an  interference  was  present.    The
detection limit  was quantitated based on the response observed.

     Cases not covered above were handled by inclusion of written
remarks indicating what was observed.

     Concentrations  of analytes were computed  from the calculated
amounts, the weight of the composite  sample,  and  the  percent lipid
in the  composite sample.   No  concentrations  were corrected  for
recovery.    The  recoveries  of  the  surrogate  compounds  were
calculated as a  check on  method performance.
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APPENDIX  E:   DISCUSSION  OF  THE VARIANCE  COMPONENTS
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     In  order to provide  a more  intuitive  understanding of  the

variance components, this appendix discusses  the  various  terms in

the model  in the context of  the  lab procedures  which might  have

contributed to each term.  This presentation is not meant  to  imply

that the possible  causes of error are limited  to those discussed

here or  that  the  those discussed here are  more important  than

other sources of error.


     The  discussion below  assumes  the  following  simplified

procedure for the HRGC/MS measurement method:

     The analysis procedures,  laboratory equipment,  and solutions
     are prepared for each batch,  including the internal standards
     spiking  solution with  three  internal  standards.   The HRGC/MS
     equipment  is  calibrated  using  a  set of  six dilutions  of  a
     standard  calibration solution.  From  the  calibration data,  a
     relative response  factor  (RRF)  is calculated for  each
     compound.   The RRF relates  the  target compound  response to
     the internal standard response.

     The sample preparation involves measuring  the wet weight  and
     percent lipid followed by several processing steps which  move
     the target  compounds  from the tissue sample  to the final
     extract and adjust the volume of the  final extract to 200  uL.
     A measured amount of the internal standard solution is spiked
     into the  final  extract before a portion  of the  final extract
     is injected into the HRGC/MS.

     The primary output  from the HRGC/MS is a  response trace  with
     multiple  peaks.  The area below the  internal standard peak,
     the area below the peak  for  the target  compound,  the  wet
     weight  and  percent lipid, and  the  relative response factor
     are used to quantitate  the  amount of  each target  compound
     (see equation (3.3)).


     Measurement errors  can result from these  processing steps  due

to small variations in:   (1)  the measurement of weights  and volumes

or  the  area  under a  mass  spectrometer  peak  (due partly  to

uncertainty   in  assessing  the background  response);   (2)   the

rounding in  calculation  results;  and (3)  the equipment setup  and

sample  processing steps  (possibly due to age  of the  reagents,  the

equipment condition, or  the time  spent on the  processing  of  each
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sample).    Large  variations  due  to measuring,  processing,  or
calculation  errors  will  also  contribute  to  the   variance
components.

     The model assumes that  the measurements for each compound  (on
a log  scale)  vary  around a constant equal to  the  product of the
true  concentration  and  the  method recovery,  Rmct^  which are
constant for each compound.

                liUXjnobsi)  =ln(CcsRmct) + error                 (E.I)

Variation of the method recoveries  from  year to year or lab to lab
are not  considered.   Variation or measurement error  around this
expected value  can be  separated into the  components  shown in the
model.  Table E-l provides a description of each component and an
example  of  processing  steps  which might  contribute   to  each
component.
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