5 17 , I I . I spiking and pi. = pi + Si after spiking. Note that a portion of the unknown concentration p; is known. Similar to equation ( 5 l), the measured concentration Y; of the i' QC sample can be expressed as Y; = ~ p; + ei = R( pi + Si) + e, , ( 5 4)

As in equation ( 5 21, the expectation of p: given Y< is given by where A and R are as in equation ( 5 2). Thus the adjusted measured concentration for spiked samples is given by t~ e following estimate of> E(&: I Y:) :

( 5 6)

where B is an estimate of the background concentration ( discussed A A in the following paragraph), and A and R are as in formula ( S?). A The last two columns of Table 5 3 contain the estimates A and R that were substituted in equation ( 5 6) for each compound. The background sample concentration, represented by B in equation ( 5 6), was estimated by fitting a linear regression model. This model, labeled the ful2 batch effects model in Section 5.3.2, estimates the linear relationship between the spiked concentration and the measured concentration in a spiked sample. This relationship was allowed to change according to the batch in which the sample was analyzed. This model has the following form:

yij = ai + Qi S,, + eij I 5 18 where Yij* is the measured concentration for the jfhQC sample ( j= 1,2,3) in the ia batch ( i= l,..., 51, Sj is the spike level of the j* QC sample, and eijrepresents random error. The parameters aiand pi ( i= l,..., 5) represent batch intercepts and slopes, respectively. These parameters were estimated by fitting the model to the QC data. The average of the estimates for the five batch intercepts cyi ( i= l,..., 5) was taken as the value of  3 in formula ( 5 6). Note that the modification presented in this subsection to adjust measured concentrations was relevant only when a native compound was spiked into the given sample. No modification was necessary for adjusting measured concentrations for unspiked native compounds in these samples.

5.3 STATISTICAL ANALYSIS OF QUALITY'CONTROL DATA The statistical analysis of quality control ( QC) data was performed to meet a number of study objectives prior to composite data analysis. These objectives include:

estimating the percent recovery of the analytical method for spiked compounds,

determining if any significant differences exist in the analytical performance among the five batches,

characterizing the precision of the analytical method,

M identifying estimates of measurement error present in the data within a batch,

fl establishing the relationship in spiked compounds between the precision of the analytical method and the level of the spiked concentration,

identifying anomalous results that suggest potential problems in the analytical measurements and which may cause removal of some of all data for a compound in further statistical analysis.

5 19 i Of the seventy samples analyzec in the FY86 semivolatiles study, fifteen were QC samples, and five were method blanks. Each of the five analysis batches contained one method blank, one unspiked control sample, and two spiked samples ( one sample spiked at a lower concentration than the other). The QC samples were prepared from a homogenized bulk lipid sample, allowing for comparisons in method quality to be made between batches. Within a batch, the three lipid based QC samples were randomized with the ten composite samples in determining the order of sample testing. The randomization ensured that no systematic trends due to changes in laboratory procedures were introduced into the analysis results. The method blank was the first sample analyzed within each batch. A total of 36 compounds were spiked into the two spiked QC samples for each batch. The spiking levels and compounds were determined by MRI in consultation with the EPA/ OPPT WAM. Sixteen of these compounds were identified in Section 5.1 as target compounds for statistical analysis. They are listed in Table 5 4 with their spike levels. These levels were multiplied by 200 ( solutionswere spiked in a 200 pL aliquot), then divided by the percent lipid weight ( in grams) of the sample to obtain spike concentrations ( ng/ g) for'thesample. QC analysis was performed on these spiked target compounds. Eight additional compounds were identified in Section 5.1 as target compounds for statistical analysis, but they were not spiked into the QC samples. These compounds were identified as unspiked target compounds. The eight unspiked target compounds were:

R Beta BHC H Bis ( 2 Ethylhexyl) phthalate R Oxychlordane 8 1 Nonene Naphthalene H 1,2,4 Trimethylbenzene m Di N Butylphthalate H Hexyl acetate 5 20 I Table 5 4. Spiked Target Compounds for the FY86 N# ATS, '

With Spiking Levels Pesticides 1 P, P DDT 5.28 21.1 3 P, P DDE 7.38 29.5 12. Heptachlor Epoxide 3.58 14.3 14 Trans nonachlor 5.48 21.9 '

60 Dieldrin@) 5.48 21.9 Chlorobenzenes 18 1,4 Dichlorobenzene 31.0 124.

27 Hexachlorobenzene 4.88 19.5 PCBs 53 Tetrachlorobiphenyl 14.1 56.2 54 Pentachlorobiphenyl 16.3 65.0 55 Hexachlorobiphenyl 13.2 52.6 56 Heptachlorobiphenyl 32.5 130.

57 Octachlorobiphenyl 34.3 137.

Phthalate Esters 66 I Butyl Benzyl Phthalate I 5.65 II 22.6 Other 33 O cymene 7.00 I 28.0 34 D limonene 5.98 23.9 40 Octamethyl Cyclotetrasiloxane 5.28 21 1 ( 1) All listed compounds except octachlorobiphenyl were detected in at least 50% of the NHATS FY86 composite samples. Octachlorobiphenyl was detected in 44% of the samples. ( 2) Detected in > 50% of the " ATS FY86 composite samples when S/ N calculation is used ( see Section 5.1.2).

Spike level ( ng/ g) = Spike level ( ns/ pL) * 200 uL Percent lipid weight ( 9)

Source: Table 9 of MRI Batch Reports ( updated 8/ 10/ 90)

5 21 QC data analysis for these target compounds was limited to identifying effects due to batch and to QC sample type. Thus QC analysis was performed on a total of 24 of the FY86 semivolatile compounds. If a compound was not detected in a QC sample, the r measured concentration was computed as oneLhalf of the detection limit. This same approach was used in the statistical analysis of the composite samples.

A listing of the QC data, both unadjusted and adjusted for surrogate recoveries, is found in Appendix B. All QC analysis was performed on data adjusted for surrogate recoveries.

5.3.1 Descrlntive Summarv of QC Data <IP_ADDRESS>. Spiked Compounds. . Table 5 5 contains a summary of the QC data for the 16 spiked target compounds. The data are corrected for surrogate recoveries as discussed in Section 5.2.

Presented for each target compound and each of the four QC sample types are the following statistics:

m the number of samples with reported results,

m the number of detected results,

m the average and standard deviation of the observed concentrations ( ng/ g),

m the coefficient of variation (%), equal to the standard deviation divided by the average.

For the spiked samples, the following recovery information is also presented:

the average spike level ( ng/ g),

m the background average recovery (% I, calculated as the average ( across batches) of the following ratio:

5 22 Q)

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Table 5 5 shows that the higher spike level for p, p DDE was approximately ten percent of the average background level given by the control sample. The laboratory analysis was unable to estimate recoveries for p, p DDEdue to the high background level relative to the spiking levels. As a result, estimated background adjusted recoveries ( BARS) for p, p DDE were negative. BARS near zero were observed at low spike levels for trans nonachlor, hexachlorobenzene, and D limonene, all as a result of high background levels. BARs of less than 50% were observed for o cymene, Dlimonene and octamethyl cyclotetrasiloxane, despite spike levels generally above observed background. Thus these three compounds may have recovery problems. The BAR for 1,4 Dichlorobenzenewas less than 60%, reflecting the higher volatility in this compound compared to the other target compounds. Except for hexachlorobiphenyl ( which had low recoveries), the BARs for PCBs ranged from 77 to 112 percent. For p, p DDT, heptachlor epoxide, hexachlorobenzene, and butyl benzyl phthalate, the BARs'ranged from 64 to 122 percent. The " BAR" approach to calculating percent recoveries given in equation ( 5 8) has been recommended for use through the " ATS program. However, an alternative approach to calculating percent recoveries does not place as much emphasis on the ability to detect finite differences in concentration. This approach considers the formula conc. ( spiked sample) * 100% Recovery (% 1 = conc. ( control sample) + spike level ( 5 9)

5 27 Note that the percentages calculated from ( 5 9) are always positive and are equal to 100% when the observed concentration equals the sum of the spike level and the control sample concentration within the batch. Table 5 6 presents the percent recoveries under both approaches ( 5 8) and ( 5 9) for the spiked target compounds. In this setting, approach ( 5 9) generally leads to improved percent recovery values over approach ( 5 8). This is especially apparent with p, p DDE, where the spike levels were much smaller than the observed levels in the control samples. While approach ( 5 8) has been recommended for the " ATS program, both approaches evaluate method performance differently, and thus both sets of results should enter into performance evaluation. Coefficients of variation were widely varied among the samples and compounds ( Table 5 5). Only p, p CDT, heptachlor epoxide, hexachlorobenzene, hexachlorobiphenyl, and heptachlorobiphenyl had coefficients of variation which were at 25% or smaller for all samples. For the other spiked target compounds, the variation in the QC results at a given spike level was as high as 80% of the observed average level across the batches. For dieldrin, butyl benzyl phthalate, o cymene, and octamethyl cyclotetrasiloxane, at least one QC sample result was not detected at the low spike level. Appendix C contains plots of the measured concentrations versus the spike levels for all study compounds. Although some plots indicate a linear increasing relationship, most plots show highly variable results among the batches at a given spike level. Several of the plots suggest that concentrations were higher for Batches 4 and 5 than for the other three batches, such as with p, p DDT, p, p DDE, and some of the PCBs. This was especially evident at high spike levels. Appendix D contains summaries like those in Table 5 5 for spiked compounds not on the target list.

5 28 Table 5 6. Percent Recoveries for Spiked Target Compounds, as Determined from Two Calculation Methods Hexachlorobiphenyl 1.43 45.40 I 89.04 81.71 Heptachlorobiphenyl 90.56 81.49 97.43 88.14 Octachlorobiphenyl 111.94 101.36 109.92 101.17 Butyl benzyl phthalate 119.06 63.55 110.52 66.72 0 cymene 2.63 17.26 15.81 20.35 D limonene  21.45 41.94 64.29 61.94 Octamethyl cyclotetrasiloxane 4.30 27.73 17.02 30.11 Two methods to calculating percent recovery on the surrogate adjusted data:

Recovery (%) . conc. ( spiked sample)  conc. ( controlsample) * Spike level ( 5 8)

conc. ( spikedsample)  Recovery(%) = conc. ( controlsample) + spike level * 100% ( 5 9)

5 29 <IP_ADDRESS> Unspiked Compounds. Table 5 7 contains descriptive summaries ' of eight target compound concentrations that were not spiked in the control samples. The descriptive statistics were calculated for each batch and across all batches.

<IP_ADDRESS>. Method Blanks. Method blanks were used to assess laboratory background contribution to concentration levels within the composite samples. Eight of the target compounds were detected in the method blanks. When detectable concentrations were measured in method blanks, the results are presented in Table 5 8. Detection in the method blanks suggests a potential bias in the reported concentration levels within the affected batches for the given compound. The method blanks for Batches 1 and 5 had detectable levels for the three target phthalates. The bis ( 2 ethylhexyl) phthalate was also detected in the method blank for Batch ' 3. The method blank for Batch 4 was not analyzed for phthalates. In most cases, the method blank concentration was at or above the control ( unspiked) sample, suggesting laboratory background contribution to the measured concentration.

5.3.2 Statistical APDroach to Analvzincr the QC Data To address the statistical objectives presented at the beginning of Section 5.3, the QC data were statistically analyzed using linear models fitted to the surrogate adjusted concentrations for each compound. A linear regression model was applied to concentration data for spiked compounds. This model included effects for batch and spike level. A similar analysis of variance application determined whether batch and sample type effects were statistically significant on concentrations for unspiked compounds. The statistical methods and results are described in this subsection.

<IP_ADDRESS> Spiked Compounds. Two types of linear regression models were fit to the QC data for spiked target compounds. One model,

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Table 5 8. Batch Analysis Results on Method Blanks and Control Samples for Compounds Detected in At Least 50% of Compositee, where the Compound Was Detected in the  Method Blank Phthalate Estere Di n butyl phthalate 1 17901 44.9 5 17957 13.7 12.05 20.2 373.

67.8 Butyl benzyl phthalate 1 17901 29.1 5 17957 14.0 10.55 13.7 276.

102.

Bis ( 2 ethylhexyl) 1 17901 205. 57.8 355. phthalate 2 17915 581. 560. 104.

3 17929 288. 222. 130.

5 17957 15.4 348. 4.4 Other D limonene 2 17915 27.9 85.4 32.7 5 17957 19.5 164. 11.9 Octamethyl 3 1.7929 156. 20.3 768. cyclotetrasiloxane Other ( sualitative)

1 nonene 1 17901 600. 200. 300.

2 17915 1000. 600. . 167.

~~

1,2,4 3 17929 40.0 I 30.0 I 133. trimethylbenzene Hexyl acetate 1 17901 40.0 .

3 17929 16000 ~

Note: Concentrations are unadiusted for surrogate recoveries.

5 32 known as the batch slopes model, provided estimates of batch recoveries and tests for equality of these estimates across batches. The other model, called the batch intercepts model, was considered when spiked sample results were not sufficiently above background to allow for batch recovery estimates to be made. The batch intercepts model provided for separate background levels to be estimated for each batch. These models are summarized in Table 5 9 and satisfactorily characterize the FY86 QC data for all compounds. The full batch effects model introduced in Section 5.2.1 and presented in ( 5 7) was also considered in this application. The full batch effects model, a composite of the batch slopes and batch intercepts models, contains ten parameters which represent separate slopes and intercepts for the five batches. This is a large number of parameters compared with the number of data points ( 151, leading to overparametrization problems. When either constant batch backgrounds or constant batch slopes cannot be assumed, a simple linear regression model, with constant background and slope across batches, was considered. The batch slopes model tested for significant differences in batch recoveries for the spiked compound. This model also estimated the batch recoveries and the average recovery across all batches, and calculates predicted concentrations at each spike level. The average recovery was tested for significant difference from loo%, thus determining the accuracy of the analytical method. The estimated intercept term was interpreted as the estimate of background ( or systematic error) across all batches. Batch effects were present when at least one of the estimated slopes was found to be significantly different from the others. According to the descriptive results presented earlier in this section, the spike levels for some compounds were low relative to background. Thus the reported concentrations for spiked samples were at the background level. This outcome was 5 33 a

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5 34 observed for p, p DDE. A batch slopes model was not appropriate in this situation, as batch recoveries cannot be estimated from the observed data. Affected compounds were analyzed using the batch intercepts model or simple linear regression model to note overall differences among batches. The statistical analysis of/ QCdata established that significant batch effects existed in the data for virtually all spiked target compounds. Specifically, estimated recoveries for Batches 4 and 5 tended to differ from the first three batches.

As a result, all statistical analyses on composite samples included a " batch classggeffect ( Batches 1 3 versus 4 5). Any batch effects existing beyond the " batch class" effect were treated as random effects. The " ATS additive model assumes that the standard deviation of the measured concentration in composite samples has two components:

a component associated with the within batch measurement error, estimated by the mean squared error ( MSE) from the batch slopes model,

a random component associated with the random batch effects within each batch ggclasslg.

For a spiked target compound, the predicted average concentration at the jthspiked concentration SCj ( j = 1, 2) is given by A * cj = & + &* SCj , ( 5 10)

where & is the baseline average concentration and is the average estimated recovery across batches. Note that is the least squares estimate of the parameter QI and hVgis the average of the least squares estimates of pi, both resulting from fitting the batch slopes model in Table 5 9. The standard deviation of is computed as 5 35 ( 5 11)

where MSE is the mean squared error from the batch slopes model, and SD( P) is the sample standard deviation of the estimated batch xecoveries. Thus the standard deviation increases with the concentration of the sample; however, it is not necessarily proportional to the concentration. If the batch slopes model indicated that a significant batch effect existed, only recoveries from Batches 1 3 were used to estimate the parametersaVgand SD( P). Otherwise all five batches were used.

<IP_ADDRESS> Unspfked Compouhds. Although batch recoveries could not be estimated for the eight unspiked target compounds, batch effects and method contamination could still be characterized for these compounds. A two way analysis of variance approach was applied to these compounds containing effects representing the batch and the sample type ( control, low spike, high spike). The batch effect provided a test for significant differences in concentrations between batches. The effect for sample type allowed for tests between samples containing different spiking solutions. This latter test was a means of determining the presence of method contamination.

5.3.3 Results of Statistical Modellina of QC Data 5.3.3.' 1 Spiked Compounds. The results of fitting the batch slopes model in Table 5 9 to the QC data for spiked target compounds are summarized in Tables 5 10 through 5 12. Table 5 10 contains the estimated batch recoveries for each spiked target compound, as well as the estimated average recovery across all batches. Table 5 11 reports significance levels for tests of equal recoveries among sets of batches. Table 5 12 provides information on observed precision.

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 Pi r( 1 Q a l I Table 5 11. Tests for Significant Differences in Batch Slopes Among Selected Batches for Spiked Target Compounds p, p DDT Heptachlor epoxide Trans nonachlor Dieldrin 1,4 Dichlorobenzene He  xachlorobenzene Butyl benzyl phthalate 0 cymene D limonene Octamethyl cyclotetrasiloxane Pesticide8 0.0003* 0.023* 0.101 0.0018*

Chlorobenzenes 0.0067* 0.031*

PCBs 0.389 0I 812 0.210 0.094 0.083 0.205 0.0016* 0.016*

0.023* 0.0032*

0.254 0.410 0.0018* 0.0006* 0.0001* 0.0018* 0.027*

0.071 0.0013*

0 * 0059*

0* ooos*

0.0026*

0.066 0.077 ( I) p, p DDE not included in this table ( see discussion)

Significance occurs at the 0.05 level.

5 39 & lG ox 3  4 U ( d d d ( d U E  4 B 3 m I d m 4 u ( d A k w 0 xcy n 4 rl t d I h 0 hm 3 +, rn 0) c, 0  4u & al rl. Qk k R $ 0 u U u I   0) & Y n $ 2 Q v F: 0 l e  4 E:@ ( d a k oal k c11 a  E I rl  4 ( d fi pc Q) rlJJ  d tu Pt no t 5 40 For all but p, p DDE, the batch slopes model provided a good fit to the surrogate adjusted data. The estimate of average recovery for p, p DDE was outside of valid ranges, emphasizing the inappropriateness of estimating batch recoveries for this compound. Batch recoveries were not interpretable for p, p DDE due to large differences in batch intercepts. Thus no estimated batch recoveries were reported for p, p DDE in Table 5 10. For the other compounds, a t test was performed at the 0.05 significance level to determine if the average recovery was significantly different from 100%. All compounds except p, p DDE and octachlorobiphenyl had average recoveries significantly greater than 100%. For twelve of the compounds, the average recovery was significantly less than 100%. Five compounds had average recoveries less than 50%: o cymene ( 18.4%), octamethylcyclotetrasiloxane ( 29.5%), dieldrin ( 46.2%), D limonene ( 46.9%), and hexachlorobiphenyl ( 48.7%). Two compounds had average recoveries significantly greater than 100%: hexachlorobenzene ( 110%) and p, p DDT ( 124%). Estimates of the individual batch recoveries from the batch slopes model are shown in the remaining columns of Table 5 10. Also present are the results of an F test to determine if significant differences exist among the batch recoveries at the 0.05 significance level. This test determines the presence of batch effects. Significant differences among the five batch recoveries were observed for twelve compounds. For virtually all of these compounds; the differences seem to arise from the large recoveries in Batches 4 and 5 relative to the first three batches. For p, p DDT, the estimated recoveries in Batches 4 and 5 average a 65% increase over the first three batches. Similar results are observed for PCBs and other pesticides. F tests on linear combinations of the estimated batch recoveries were performed to determine significant differences Y among these recoveries. The significance levels for the test of equal recoveries among the five batches are listed in Table 5 10 for each spiked target compound except p, p DDE, where batch recoveries could not be accurately estimated. Because of the apparent difference in estimated batch recoveries between Batches 1 3 and Batches 4 5, Table 5 10 also contains significance levels for testing differences between these two groups of batches, as well as among the first three batches only. For eleven of the fifteen spiked target compounds in Table 5 10, the estimated recoveries in Batches 1 3 differ significantly ( at the 0.05 level) from the estimated recoveries in Batches 4 5. However, only three of these compounds have significant differences in estimated recoveries among Batches 1 3 only. Thus the following conclusions can be made from Table 5 10:

I The systematic difference in recoveries between Batches 1 3 and Batches 4 5 appears real,

I There appear to be no additional systematic batch effects beyond that observed in Batches 1 3 versus Batches 4 5.

The first conclusion states that it is not suitable to treat all batch effects as random as was done in the FY87 analysis of dioxins and furans. The presence of a systematic batch effect indicates that some batch correction is necessary when analyzing the composite data. However, any additional batch effects beyond the Batches 1 3 versus Batches 4 5 effect can be treated as random. For spiked target compounds, Table 5 12presents the predicted average concentration and estimated coefficient of variation ( CV) for each compound and spike level, as derived by the batch slopes model. These results were used to characterize the precision of the analytical method. Except for o cymene and octamethyl cyclotetrasiloxane( which had very low recoveries), all predicted concentrations at the zero spike level were significantly greater than zero at the 0.05 significance level.

5 42 This is consistent with the fact that thi target compounds were detected in nearly all of the QC control samples ( Table 5 5).

Whenever the batch slopes model indicated a significant batch effect present, average recoveries from only the first three batches were used to calculate predicted concentrations and CVs for the compound. This reflects the assumption that the primary trend in batch effects is due to Batches 4 and 5 having higher recoveries compared to the first three batches, leading to biases in the results from Batches 4 and 5. From Table 5 12, the relative precision of measured concentrations tends to be better for pesticides and PCBs compared with other groups of compounds. At the control level, the CVs for pesticides and PCBs range from 7.6% to 51.5%, with a CV of 71.9% for the more volatile 1,4 Dichlorobenzene. The CVs for all of the pesticides and PCBs are below 79% in the spiked samples. Meanwhile, except for D limonene ( whose CVs rival the pesticides and PCBs), the CVs for phthalates and other compounds are above 50% for control and spiked samples. Because batch recoveries could not be estimated for p, p DDE ( m/ z= 288 and m/ z= 316) based on the observed results and spike levels, the batch intercepts model was fit to this compound. The batch intercepts model provides for background levels to be estimated for each batch. Thus batch effects were determined by testing for equality of the batch background levels. Table 5 13 contains the results of fitting the batch intercepts model to p, p DDE. For both sets of p, p DDE results, the test for batch effects is highly significant. As apparent in the QC data plots, the estimated background levels for Batches 4 and 5 are over twice the level of the first three batches. This extreme difference in background levels contributes to the inability to estimate batch recoveries. Thus the results of the batch intercepts model fitting for p, p DDE indicate that differences between the two " batch classes" ( Batches 1 3 versus 4 51 are highly significant, as was seen for most of the spiked target compounds.

 4 CP", 5 43 6' t \ , j & at mo 40 CJm em d 8E 4 L) ww id tnm R at2 Q) drl 5 E:  rl h ul. 3 . z bl  3 id A uw 2 d) JJ i! k id a rnw  W 0 alwfd a m 5 m al rl hh +, W .

I cow a 0 Em cod  4( urn u al H 11 Ul NN al \\   a x E k YV cd ww aa aa A Y 5 44 <IP_ADDRESS> Unepiked Cosapounds. Results of statistical analysis of unspiked target compound concentrations in QC samples are presented in Table 5 14. This table presents significance levels for differences between batches and between sample types. Batch effects were significant at the 0.05 level for oxychlordane and Di n butyl phthalate. Significant batch effects, for oxychlordane are attributed to the large number of not detected readings in Batch 1. A very high percentage of not detected readings for oxychlordane in Batch 1 is also present among the composite samples. Since the frequency of not detected oxychlordane   readings substantially decreases after Batch 1, the Batch 1 oxychlordane results tend to be suspect. None of the unspiked target compounds showed a significant effect due to the sample type. Thus these data can be considered as control sample results for the unspiked compounds. All of these samples are used to determine within batch measurement error. Precision was estimated for the unspiked compounds at the control level based on the above analysis of variance model. The precision summary is presented in Table 5 15. The predicted control level reflects all QC samples, as it was determined that no sample type effect existed. Because data exist for all sample  types within each batch, the predicted concentration is equal to the average concentration across the 15 QC samples. The standard deviation of the predicted concentration is equal to the mean? squared error estimated by the model. The precision summary in Table 5 15 indicates that two compounds ( Bis ( 2 ethylhexyl) phthalate and hexyl acetate) have CVs above 100%. These compounds have one extreme observation in at least one batch, at levels up to four times the value of the other results within the batch. Other compounds also show high variability in the data within each batch, especially between not detected results and detected results.

5 45 Table 5 14. Reeults of Statisthzal Analysis of QC Data on Unspiked Target Compounds Beta BHC I 0.626 I 0.643 Oxychlordane I 0.009 0.634 PAHS 11 Naphthalene I 0.545 I 0.698 Phthalate Esters Di n butyl phthalate 0.050 0.073 . Bis ( 2 ethylhexyl) phthalate 0.496 0.119 Other ( qualitative) Pesticides 1 nonene 0.488 0.613 II 1,2,4 Trimethylbenzene I 0.144 1 0.199 Hexvl. acetate I 0.771 1 0 342 n ,

5 46 s li Table 5 15. Predicted Concentration8 and Coefficients of  Variationfor Unspiked Target Compounde at the Control Level Pesticides Beta BHC 213.4 76.0 Oxychlordane ( all batches) 105.9 34.1 Oxychlordane ( Batch 1 removed) 129.2 31.8 pm8 Naphthalene 1 9.731 I 44.9 Phthalate Esters Di N Butyl phthalate I 39.79 47.3 Bis ( 2 ethylhexyl) phthalate 309.1 126.0 0ther ( qualitative1 1 nonene 452.0 75.6 1,2,4 trimethylbenzene 36.00 77.5 Hexyl acetate 120.6 116 1 Note: These statistics reflect results for all QC samples.

Coefficient of variation = Sauare root of mean suuared error .   h Predicted concentration : I :< . 5::. . 9 .,

il .. ,

5 47 5.3.4 Concluaions The following summarizes the conclusions and findings of the QC data analysis ( courses of action formulated fromthese conclusions are underlined):

1. Significant batch effects appear among the 16 spiked target compound concentrations. The primary batch effect is due to the high recovery and background in Batches 4 and 5 compared to the other three batches. Because this difference between " batch classes" is prevalent in nearly all of the spiked target compounds, it is necessaw to include an effect for Batches 4 5 versus Batches 1 3 in the model used to analvze the comDosite samoles. Any other batch effects were assumed to be random and thus were not considered in model adjustments.

2. The difference between " batch classes" was not as significant among the eight unspiked target'compounds. However, differences in control level concentrations between batches were noted for oxychlordane and di nbutyl phthalate. In particular, nearly every QC and composite sample indicated a not detected result for oxychlordane in Batch 1. As a result, all Batch 1 concentrations for oxvchlordane will be deleted Drior to comDosite'data analvsis.

3.: Seven of the target compounds were detected among the five method blanks.. All three target phthalates were included among these seven compounds. In particular, bis ( 2 ethylhexyl) phthalate was detected in all four method blanks wh. ichwere analyzed for phthalates. Dlimonene and octamethyl cyclotetrasiloxane, also detected in the method blanks, were among those compounds with relatively low recoveries.

4. High background levels relative to the spiking levels were observed for a few spiked target compounds. In particular, the spike levels for p, p DDE were no more than 10% of the observed background level. For this reason, and because of large differences in background level among the batches, batch recoveries could not be estimated for p, p DDE. Other compounds with high background levels relative to spiking levels were p, p DDT, heptachlor epoxide, trans nonachlor, hexachlorobenzene, and D limonene.

5. Estimated average recoveries for spiked target compounds were significantly below 100% for all but p, p DDTand hexachlorobenzene, where they were significantly above 100%. 0 cymene, D limonene, %. I octamethyl cyclotetrasiloxane, dieldrin, and hexachlorobiphenyl had average recoveries below 50%. Most estimated batch recoveries for all compounds were less than 100% for Batches 1 3, while many compounds had estimated batch recoveries above 100% for Batches 4 and 5.

6. Characterization of measurement precision for spiked target compounds indicated that better precision was observed for pesticides and PCBs. Precision was worse for phthalates and " other" compounds, with coefficients of variation ( CVs) exceeding 50%. For unspiked target compounds, CVs ranged from 32 to 126 percent.

7. Except for o. cymene and octamethyl cyclotetrasiloxane ( which had very low recoveries), all predicted concentrations at the zero spike level were greater than zero at the 0.05 significance level.

8. The relationship between measured and spiked concentrations for spiked target compounds was generally linear over the range of spiked concentrations, but the variability within each batch was high.

The above findings in the QC data were'used to reevaluate the status of each target compound prior to composite data aqalysis. Several compounds had recovery and cantamination problems as summarized above. As a result of findinas from the statistical analysis on OC data, the followina comrsounds have been removed from the list of taraet comrsounds on which statistical analvsis of comrsosite data is rserformed:

Bis ( 2 ethvlhexvl) rshthalate

*+

3 w detected in all method blanks analyzed for this &%

24 compound. I:< .+ v low precision results. & +*

.&* Di n butvl Phthalate detected in two of the four analyzed method blanks.

I high levels of not detected results among the composite samples in Batches 3 and 5 make these batch results suspect.

5 49 c 61( 2p38 Butvl benzvl phthalate m detected in two of the four method blanks analyzed for this compound.

* rn the low spiked result in Batch 3 was not detected, although spiked amounts were not below estimated background.

1.2,4 Trimethylbenzene R detected in the method blank for Batch 3.

rn percent detected among composite samples in Batches 4 and 5 is very low compared to the other three batches, making these batch results suspect.

0 cvmene rn recoveries extremely low for all spiked QC samples, even though the spiked amounts were above estimated background. All results for spiked sampled failed to meet DQOs.

rn percent detected among composite samples in Batch 1 is low compared to the other batches.

D limonene rn detected in two of the five method blanks.

 I R recoveries extremely low for spiked QC compounds.

Octamethvl cvclotetrasiloxane m detected in the method blank for Batch 3.

m recoveries extremely low for all spiked QC samples, even though the spiked amounts were above estimated background. All results for spiked samples failed to meet DQOs.

M percent detected among composite samples in Batch 1 is low compared to the other batches. The percentage of detected results increased with the batch ID number.

A total of 17 compounds remained classified as target compounds for statistical analysis following analysis of the QC data. However, only limited analyses were performed on the qualitative compounds hexyl acetate and 1 nonene.

5 50 6.0 STATISTICAL METHODOLOGY This section discusses the statistical methodology applied in the FY86 NHATS composite sample data analysis. The statistical analysis of FY86 " ATS data had three objectives:

Estimate average concentration levels of target semivolatile compounds in the adipose tissue of individuals in the U. S. population as well as in various demographic subpopulations.

8 Estimate standard errors and construct confidence intervals for these average levels.

H Perform statistical hypothesis tests to determine if average concentration levels of target semivolatiles in the U. S. population differ significantly by any of four demographic factors ( geographic region, age group, race group, and sex group).

The " additive model", a statistical model developed to estimate average concentration levels in ind, ividualspecimens by analyzing NHATS composite data, was fit to the FY86 data to address each of the above objectives. The additive model involves an iterative weighted generalized least squares method to estimate model parameters representing demographic effects. The resulting parameter estimates are approximately normally distributed for large samples. This approximate normality is used to construct confidence intervals and hypothesis tests. Derivation and validation of the additive model is presented in Orban and Lordo ( 1989). \

Section 6.1 briefly presents the additive model and its necessary modifications in analyzing the FY86 data. The methods used to obtain estimates of average concentrations for target compounds, standard errors for these estimates, and hypothesis tests for the significance of demographic effects on the concentrations are presented in Section 6.2.

6 1 6.3. THE 24DDITIVE MODEL In order to expand the " ATS to address a broader range of compounds, it was necessary to develop'mass spectrometry based analytical methods that provided detailed chemical information and supported method specificity. These analytical methods required larger tissue samples than the available samples from individual patients. As a result, the individual adipose tissue specimens were composited prior to chemical analysis. The additive model was developed to achieve the " ATS statistical objectives under the sample compositing scenario. The additive model was used to analyze the FY87 mTS dioxin and furan concentrations in composite samples ( USEPA, 1991). The FY86 " ATS was the first study in which the additive model was applied to semivolatile composite data. Orban and Lordo ( 1989) have shown that the additive model has the following attractive features: I w Under very general assumptions, the additive model produces asymptotically unbiased estimates of average concentration levels in the population.

W The additive model establishes a more tractable relationship between the distribution of analyte concentrations in. individuals and the distribution of measured concentrations from the composite samples.

The latter feature is particularly important because individual specimens are collected, but the chemical analysis is performed on composite samples. Table 6 1lists the categories of the four analysis factors of interest to the NHATS. The additive model assumes that the four analysis factors have fixed additive effects on the average concentrations in specimens. This assumption subdivides the population into 48 alsubpopulationsll defined by the 4x3x2x2= 48 unique combinations of categories for the four factors.

6 2 Table 6 1. " ATS Analysis Factors and Categories Census region Northeast North Central South West 4 Age group 0 14years 3 15 44 years 45+ years Race group Caucasian 2 Noncaucasian  Male 2Sex group I . Female I Total Number of Subpopulations ( combinations of the four analysis factors): 48 In addition to the four analysis factors, there are three ancillary factors that have random effects on NHATS data. Two of these factors have random effects on the actual concentration in individual specimens. They are:

effect of MSA sampling 1 effect of sampling individuals within MSAs ( and selecting specimens from individual donors)

The third has a random effect on the measured composite concentrations:

measurement error of compound concentrations in the composite samples.

A fourth ancillary factor applied specifically to the FY86 composite data is the fixed effect of laboratory batches 4 and 5 on the measured composite concentrations. Analysis of FY86 6 3 . QC sample data ( Section 5.3) found significant differences for a majority of target compounds in the measured concentrations for Batches 4 and 5 versus those in the first three batches. Thus a " batch class" factor has been included in the, additivemodel for analysis of FY86 NHATS semivolatile data on composite samples, From these assumptions, the actual concentration CijkCm in a ., sbecimenfrom the ithdonor in MSA j, census region k, age group e, sex m, and race group n, is represented by where p is a constant,

CRk is the fixed effect of census region k ( k= 1,2,3,4),

At is the fixed effect of age group 1! ( 1!= 1,2,3),

S, is the fixed effect of sex group m ( m= l, 2),

% is the fixed effect of race group n ( n= 1,2)#

MSA, is the random effect of selecting MSA j ( j= 1,2,... I,

cij is the random effect of selecting individual i in MSA j .

To uniquely define the fixed effect parameters, let Thus CR,, A,, S2, and R2 are defined as a linear combination of other effects, leaving eight fixed parameters in ( 6 1) which can be uniquely estimated. The effect MSAj in ( 6 1) is a random effect due to the selection of MSAs prior to selecting individual specimens. This effect is assumed to have mean zero and variance u:. Meanwhile, the effect eij in ( 6 1) is random due to selecting individuals randomly within an MSA. The distribution of eij has mean zero and variance a2Eand is independent from the distribution of MS%. Data analysis results through the history of the " ATS program have concluded that variation in specimen concentrations is proportional to the average concentration level. This finding is generally true in most environmental monitoring programs where chemical concentrations are measured. Thus if ps is the average concentration level in subpopulation s, then ' it is assumed that for subpopulation s ( s= l,..., 48), there exists a positive number b such that:

For notational simplicity we let where the combination of indices k, C, m, and n define subpopulation s.

Equation ( 6 1) defines the model for the actual concentration in a specimen collected in the FY86 NHATS. However, as specimens are composited prior to chemical analysis, measured specimen concentrations Cijktm are not observed. Instead, data are obtained from the chemical analysis of comDosite samples. Assuming data exist for C composites, and letting Yh represent the measured concentration of composite h ( h= l,..., C), the natural additive effects of cornpositing imply that where Cijs is the actual concentration in specimen i from MSA j and subpopulation s,

Ch( i, j, s) is equal to 1 if specimen i from MSA j and subpopulation s is in composite, h, and is equal to 6 5 . .. .. .. . .. .... . . .... . . .. .. . . .__ . . . .. .. . zero otherwise,

Mh is the number of specimens in composite h,

B4, is the fixed effect of analysis in Batches 4 and 5 on the Composite concentration,

Ih is equal to 1 if composite h was analyzed in Batches 4 or 5, and is equal to zero otherwise, and yfi is random measurement error associated with composite h, assumed to have mean zero and variance a;.

Because Cijs is associated with demographic effects as specified in equation ( 6 11, equation ( 6 2) relates the measured composite concentrations with the demographic effects in Table 6 1. Note that the term B,, has been placed in the model in ( 6 2) as a result of the QC data analysis on FY86 NHATS data. It is not a standard term in the additive model for all NHATS applications. The statistical analysis performed on the additive'model in ( 6 2) will be explained in terms of matrix notation. Matrices are denoted by capital letters. Matrices and vectors are denoted in bold. Let be the 9x1 vector of fixed effects from equations ( 6 1) and ( 6 2) on the vector of composite concentrations y = ( Yl, Y,, ..., Yc) I.

Fixed effects omitted from @ can be specified as a linear combination of the effects in 8. Let I.( = ( pI,*.., p48)' be a 48x1 vector containing the unknown average concentrations from the 48 subpopulations. Then ~ c is calculated as p = X@ for some 48x9 design matrix X.

If the QC data analysis ( Section 5.3) found the average concentration in Batches 4 5 to be significantly different from that for the first three batches, the matrix X is constructed so that p will depend on the effect B45. In this situation, two average concentrations will be associated with each + subpopulation, one for Batches 4 5 and one for Batches 1 3, This is due to potential biases attributed to the results in Batches 4 and 5.

The expected value of the composite concentrations y is given by where 2 is a Cx48 composite design matrix. Thus, according to the additive model, both the actual concentrations of the individual specimens and the measured concentrations of the composite samples have expected values that are linear combinations of the additive effects of the fixed analysis factors in 8. Orban and Lordo ( 1989) also show that the variance covariance matrix of y { denoted by Vy) is a block diagonal matrix that depends on a:, a:, and at.

6.2 STATISTICAL ANALYSIS OF COMPOSITE SAMPLES This section describes the specific methods used to achieve the statistical objectives. The estimation methods are discussed in Section 6.2.1, and the hypothesis testing procedures are presented in Section 6.2.2. This section refers to terms and symbols presented in Section 6.1.

6.2.1 Estimation <IP_ADDRESS> Estimating Native Compound Levels. The specific quantities estimated for the FY86 " ATS are the average concentrations in the adipose tissue of the U. S. population and *

the average concentrations for each of the eleven " marginalt1 demographic populations defined by the categories listed in Table 6 1. These estimates were calculated in three steps:

6 7 .. ._..... .. ... .___. 1. The additive model parameters ( vector ( s in Section 6.1) were estimated using a method called iterative weighted , generalized least squares ( IWGLS).

2. Estimates of average concentration levels in the 48 subpopulations defined by the four analysis factors ( vectorp in Section 6.1) were calculated from the parameter estimates.

3. National and marginal population estimates were obtained by taking weighted averages of the appropriate . subpopulation estimates in p. Weights were proportional to the population counts from the 1980 U. S. Census.

To obtain asymptotically unbiased estimates of the fixed effects in B, it is not necessary to make any assumptions about the form of the distributions of the random effects in equation ( 6 2). If the variance covariance matrix Vy of the vector of measured composite sample concentrations y were known, the method of generalized least squares ( GLS) produces estimates of . P that are unbiased and have minimum variance among all unbiased estimates. Furthermore, if the errors are normally distributed, the GLS estimates are equivalent to the maximum likelihood estimates. The GLS estimate of @ is given by 5 = ( DIV,~ D)~ DT,~~ I ( 6 4)

Awhere D is defined in ( 6 3). The variance covariance matrix of 0 is given by Unfortunately, V,, depends on three unknown variance components 2 ( urn, u2,, and 0%) from ( 6 1) and ( 6 2), as well as on the vector 8. Therefore, Orban and Lordo ( 1989) proposed a method involving iterative weighting. Thus the method is called iterative weighted generalized least squares ( IWGLS). The IWGLS procedure requires starting values for the unknown parameters. These starting values were calculated using the P3V program of the BMDPw software package. This program uses a maximum likelihood procedure in fitting a mixed model. The resulting estimate of Vu was then used in the GLS formula to produce a revised estimate of #. The IWGLS procedure provided continual updating of the estimates for Vu, continuing until convergence criteria on the estimate of # and the error sum or squares were met. Orban and Lordo ( 1989) discuss this method in more detail and describe special computer programs in the SAS" System for implementing IWGLS. They also provide formulas for calculating the stafldard errors of the estimates. If 5 denotes the final estimate of fl from the IWGLS procedure, then an estimate of the average concentration level in each of the 48 subpopulations is calculated by A where X is a design matrix. The variance covariancematrix of p is given by The estimates in are affected whenever batch class effects are present.

. Weighted averages of the appropriate subpopulation A concentrations ps are calculated to estimate fImargina1" averages for the categories of each analysis factor. For example, if the set of 12 of the 48 subpopulations found in the Northeast census region is represented by NE, then the estimated average concentration in the Northeast census region is given by where ws is the proportion of total population in the Northeast census region that is found in subpopulation s ( as determined by  I,

1980 U. S. Census figures). Marginal estimates were calculated for four census regions, three age groups, two race groups, and two sex groups. The U. S. population estimate was calculated in the same way, with weights corresponding to the proportion of the U. S. population in each subpopulation. Standard errors for the marginal estimates were calculated based on the standard errors of the subpopulation A estimates ps. If Var( k) indicates the estimated variance of is, then the standard error of the marginal estimate of & in ( 6 5)

is given by where NE and ws are as defined in ( 6 5). An approximate 95%

confidence interval for each estimate was calculated by adding and subtracting two times the standard error of the estimates.

<IP_ADDRESS> Characterizing PCB Results. Laboratory analysis in the FY86 " ATS measured the concentrations of each of the ten PCB homologs in the composite samples. These concentration estimates were integrated to characterize the nature of PCBs detected in adipose tissue. If pi is the average concentration level ( ng/ g) of the ithPCB homolog ( only p4 through p8 were estimated in the statistical analysis), then the characterization considered the .,

following three sets of information:

U Total PCB concentration ( ns/ cr) the sum of the * estimated. concentrations for each homolog:

6 10

 w Chlorobinhenvl distribution across homoloss (% I the percentage of the total PCB concentration attributed to the ith homolog ( ipl,..., 101 :

rn Chlorination level (% I the sum of the chlorobiphenyl distribution percentages, each weighted by the homolog's chlorine mass fraction ( ClMF):

These PCB parameters were estimated by substituting estimates of the homolog concentrations pi in the hove equations, as obtained from the statistical analysis ( Section <IP_ADDRESS>). However, statistical analysis was performed only on five of the ten PCB homologs ( tetra through octa CB). The remaining five homologs were each detected in no more than 30% of the FY86 " ATS composite samples, Thus in estimating the above PCB parameters, it is assumed that pi10 for i= 1,2,3,9,10. While this approach may lead to an underestimate of total PCB concentration, the extent of underestimation is expected to be very low. To estimate the level of chlorination, the value of the ClMF is 0.4856 for tetra CB, 0.5430 for penta CB, 0.5893 for hexa CB, 0,6277 for hepta CB, and 0.6598 for octa CB. The standard errors of the above PCB parameters were calculated from the variability estimates in the average concentration levels far the individual PCB homologs ( Section <IP_ADDRESS>). If pi is the estimate of pi as obtained from the statistical analysis, then standard error estimates are given as:

standard error of total PCB: ( 6 10)

( 6 11) standard error of chlorobiDhenv1 distribution oercentases: standard error of level of chlorination:

( 6 12)

Approximate 95% confidence bounds for the PCB parameters were taken as plus and minus two standard errors.

6.2.2 Hwothea3. s Teatinq Hypothesis tests were performed to determine if average concentration levels differ significantly by any of the geographic or demographic factors. The specific hypotheses tested were Ha : CR1 = CR2 = CR, = CR, = 0 HAGS: A, = A2 = A3 = 0 HSEX   s,= s2= o, HRACE: R, = R2 = 0 I HB45 : B4, = 0 .

The hypothesis HCRtfor example, states that there are no differences in average concentration levels among the four census regions. Each hypothesis was two tailed; that is, each alternative was that at least one effect was nonzero and different from the others. In order to test these hypotheses, it was necessary to make specific distribution assumptions for the random effects. It was assumed that the errors associated with sampling MSAs,

6 12 sampling individuals within MSAs, and measuring concentrations were independent and normally distributed. The additive effect of compositing specimens suggests that the normality assumption is reasonable because specimen sampling errors are averaged in the composite sample. Statistical theory states that averages and sums are approximately normally distributed. Distributional assumptions were tested for all target compounds using probability plots and residual analysis. The likelihood ratio method was used to test the above hypotheses. In this process, the additive model is fit to the observed data both including and excluding the effects to be tested. According to asymptotic theory, the log of the ratio of the likelihood functions from these two fits has approximately a chi squared distribution, with degrees of freedom equal to the number of independent parameters constrained under the null hypothesis. Orban and Lordo ( 1989) developed programs in the SAS@ System to perform these tests.

::

?.

6 13 7.0 RESULTS This section contains the results of the statistical analysis of the FY86 " ATS for sernivolatiles in human adipose tissue. The applied statistical methods were discussed in Chapter 6. The objectives of the statistical analysis were as follows:

m Estimate average concentration levels of target compounds for individuals in the U. S. population and in ' various subpopulations;

m Calculate standard errors and confidence bounds on these average levels;

m Perform statistical hypothesis tests to determine if average levels differ significantly between various levels of demographic factors of interest.

statistical analysis was performed on data obtained from laboratory analysis of 50 composite samples. The composites were prepared using a total of 671 adipose tissue specimens from sampled cadavers and surgical patients. Each composite contained from three to 24 specimens, with an average of 13.4 specimens per composite. The specimens within each sample originated from a common census division and age group but may have differed among sex and race groups. Additional information on sample and composite design is presented in Chapters 2 and 3.

A descriptive summary of the observed concentrations for the 111 semivolatiles is provided in Section 7.1. Statistical analysis was performed only on " target" semivolatiles ( identified in chapter 5) that were detected in a majority of the 50 composite samples and which met specific data quality objectives. Resulting from this statistical analysis, estimates of average concentrations are presented in Section 7.2, along with standard errors and confidence bounds on these estimates.

c cection 7.3 presents the results of statistical hypothesis testing to identify significant effects of demographic factors on sverage concentration levels. Section 7.4 describes the outlier 7 1 I detectian procedures that identified potential data errors to be corrected prior to conducting the statistical analysis. Finally, as part of the commitment to overall data quality in this program, procedures were implemented to demonstrate the validity of the statistical methodology applied to the FY86 NHATS data. The results of this data validation procedure are presented in Section 7.5.

Unless otherwise specified, all statistical analyses were performed on composite concentrations adjusted for recoveries of surrogate compounds. This adjustment, discussed in Section 5.2, corrected for systematic error identifiable through the surrogate recovery data.

7.1 DESCRIPTIVE STATISTICS Prior to statistical modelling of target compour; ds, simple descriptive statistics were generated on the measured concentrations for all 111 semivolatiles analyzed in the NHATS FY86 campaign. These statistics summarized the laboratory results across all 50 FY86 composite samples and consisted of the following:

1 arithmetic average; rn standard deviation; rn standard error of the average; rn percent of samples with detected results ( duplicated from Table 5 11 ; rn average level of detection ( LOD).

Table 7 1presents these statistics across the 111 semivolatiles for measured concentrations adjusted for surrogate recoveries, as well as on the unadjusted concentrations. A compound is detected within a composite sample if the result is classified as either a trace or positive quantifiable reading. Prior to summarizing the data for a given compound, the measured concentrations for all samples with not detected outcomes were replaced by one half of the reported LOD. While ;?{

the LOD itself was not adjusted for surrogate recoveries, the 7 2 c 9 t (", 1 4 .......

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9 x . Q 3 JJ k 0 8 k a fa 6" 0  rl u fa k u 9 i u modified measured concentration was adjusted. No LOD was The percentage reported for detected compounds within a sample. of samples in each of three qualifier classifications ( not detected, trace, and positive quantifiable) was summarized in Table 5 1 of Chapter 5.

Appendix E contains the minimum, median, and maximum reported concentrations across the 50 composite samples for each of the 111 compounds. These statistics are based on concentrations which are unadjusted for surrogate recoveries. The descriptive statistics in Table 7 1are based on simple averages of the measured concentrations within the 50 composite samples. As such they only summarize the observed data. They should not be used to estimate concentration levels within the population. Statistical analyses were implemented to obtain population average estimates for seventeen target semivolatiles meeting specific data quality objectives. results of these analyses are presented in the following sections. The 7.2 POPULATION ESTIMATES FROM STATISTICAL MODELLING The statistical modelling techniques presented in Chapter 6 were used to determine'estimatesof average concentrations for selected semivolatiles within subpopulations as well as for the entire nation, to obtain estimates of uncertainty inherent in these estimates, and to identify where significant differences in average concentration were present among subpopulations. These techniques centered around the additive model, which was used to estimate average concentration for individuals as a function of several demographic factors. The results from fitting the additive model to the NHATS FY86 composite data are presented in this section. Not all of the compounds analyzed in the FY86 NHATS analysis provided sufficient composite concentration data to warrant a meaningful statistical analysis. Seventeen of the 111 compounds were identified as containing a sufficient number of 7 a p ? I,:/ g 4; 0 detected samples and whose analytical measurements were deemed accurate in reflecting the true concentration level. Having a sufficient number of composite samples with detected results ensured that only minimal bias was generated by substituting one half of the detection limit for the measured concentration whenever the compound was not detected by the analytical method. Method performance was determined from analysis of the QC data ( Section 5.3), which indicated the presence of batch effects and the extent that anomalous analytical results were reported. The compounds selected for statistical modelling, as well as the criteria used to select them, were identified in Chapter 5.

Fitting the additive model to the " ATS FY86 data for 17 semivolatiles resulted in average concentration estimates for the entire U. S. population, as well as ' Imarginalf1estimates for each of the categories defined by the four analysis factors presented in Table 6 1 ( census region, age group, race group, and sex group). The formula for calculating marginal estimates was given in equation ( 6 5) of Section 6.2.1. The estimates are presented in Table 7 2 for the four census regions, Table 7 3 for the three age groups, Table 7 4 for the two race groups, and Table 7 5 for the two sex groups. Table 7 6 presents estimated concentration estimates for the entire nation. The estimates are asymptotically unbiased and were adjusted for the presence of laboratory batch ef Eects( Batches 1 3 versus 4 5) and for population percentages based on the 1980 U. S. Census. Accompanying the marginal estimates based on the additive model, standard errors and approximate 95% confidence intewals of these estimates are displayed in Tables 7 2 through 7 6. The standard errors were calculated using equation ( 6 6) of Section 6.2.1 and are used to characterize the statistical uncertainty in the estimated average concentrations. The standard errors are presented in both absolute and relative terms. The confidence intervals represent the marginal estimate, The actual plus and minus. approxirnatelytwo standard errors. ,

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PIk .. ln ad U VQ dd k s" 0 ._ . Table 7 6. Estimates of Average Concentrations(') for Selected Semivolatiles, With Standard Errors and Approximate 95% Confidence Intervals, for the Nation from " ATS  86 Composite Samples Pesticides PrP DDT 177. 19.7 11.2 ( 137., 217.)

~,~ DDE@) 2340. 270. 11.6 ( 1792., 2884.)

Beta BHC 157. 24.9 15.9 ( PO?., 207.)

Heptachlor epoxide 57.6 4.19 7.3 ( 49.2, 66.1)

Oxychl~ rdane(~) 114. 7.52 6.6 ( 98.4, 129.)

Trans nonachlor 130. 15.3 11.7 ( 99.6, 161.)

Dieldrint4) 47.0 7.95 16.9 ( 31.0, 63.1)

Chlorobenzenes 1,4 Dichlorobenzene 90.9 15.2 16.7 ( 60.2, 122.)

Hexachlorobenzene 51.3 3.97 7.7 ( 43.3, 59.3)

PAHS Naphthalene 1 20.7 2.37 11.4 1 ( 15.9, 25.4) PCBs Tetrachlorobiphenyl 56.4 4.70 8.3 Pentachlorobiphenyl 135. 15.3 11.4 Hexachlorobiphenyl 314. 18.4 5.9 Xeptachlorobiphenyl 125. I 21.9 17.5 ~

( 46.9, 65.9)

~

( 104., 165.) I 276., 351.)

Octachlorobiphenyl 42.7 11.6 27.1 ( 80.7, 169.)

( 19.3, 66.1)

( 603., 742.)

( 51.2, 65.4) Total PCBs@) 672. I 34.6 5.2 Level of 58.3% 3.54 6.1 Chlorination(@ <

Other ( qualitative)

1 Nonene 124.) 51.0 41.3 ( 20.6, 227.)

Hexyl acetate 123. 21.5 17.5 ( 79.5, 166.)

7 23 Table 7 6. ( cant.)

Notes for Table 7 6: 0' Data adjusted for surrogate recoveries ( see Section 5.2). Estimates are based on 1980 U. S. Census figures. ( 2) p, p DDE concentrations use the following response ion: m/ z= 316. Data results from Batch 1 not included in calculations. c4) Corrected ( see Section S 1.2). ( 9 The estimate for Total PCBs is the sum of the estimated averages over the five homologs included in this table ( i. e., homologs detected in at least 44% of the " ATS M86 composite samples). ( 6) Estimated percent level of chlorination is calculated as follows:

8 where =: estimate of the percent of total PCBs for homolog i, and Bi = mass fraction of chlorine for homolog i. ( 0nl. y the five PCB homologs included in the table are considered in calculating level oE chlorination.)

7 24 number of standard errors in the confidence interval is determined by the Student t distribution. The 17 target compounds for statistical analysis included five PCB homologs ( tetra through octa chlorobiphenyl). Using the average estimates for these five homologs, estimates of total PCBs and level of chlorination were calculated based on the approach documented in Section <IP_ADDRESS>. The estimates . ofthese two PCB parameters are also included in Tables 7 2 through 7 6.

In addition, the chlorobiphenyl distribution across the five PCB homologs, corresponding to the percentage of the total PCB concentration represented within each homolog, is presented in Table 7 7. This table illustrates that the penta, hexa, and hepta chlorobiphenyls represent over 80% of the national average PCB concentration across the five homologs, with hexachlorobiphenyl representing 47% of the total. As will be seen in Chapter 8, similar distributions were observed in previous " ATS campaigns. Appendix F contains plots of the estimated average concentrations and their associated 95% confidence intervals for the 17 target compounds, as documented in Tables 7 2 through 7 6. One plot exists for each compound and contains statistics for each of the four analysis factors and the entire nation. These plots illustrate the trends observed in the average concentrations across the subpopulations and the variability associated with these trends. Considerable overlapping of the confidence intervals indicate that while average concentrations may differ between subpopulations, they may not differ statistically. The chlorobiphenyl distributions presented in Table 7 7 are also plotted in Appendix F. Estimates of the average concentrations in the population categories defined by the four demographic factors are presented in Tables 7 2 through 7 6 even if the effects of those factors were not found to be statistically significant. through hypothesis testing. For example, regional estimates of average ~

concentration for Beta BHC range from 151 ng/ g in the North 7 25 I ,

i Table 7 7. Chlorobiphenyl Distribution Across the Five Target PCB Homologs in the FY86 " ATS 1 I 15.6% North Central 10.2% 25.5% 43.4% 5.2%

North East 7.9% 20.2% 43.1% 21.3% 7.5%

South Note: Homologs not represented in this table were detected in no more than 30% of the NHATS M86 composite samples. The omitted homologs were not included in calculating total PCBs, and thus the percentages in a given row add to 100%.

7 26 Central census region to 177 ng/ g in thc South census region. However, as further documented in Section 7.3, this difference was not found'tobe statistically Significant. Table 7 6 indicates that the standard errors of the national estimates among the 17 semivolatiles ranged from 5.9 to 41.3 percent of the estimates. The highest relative standard error was observed with 1 Nonene, which is a qualitative semivolatile compound. Among the four analysis factors, higher relative standard errors were generally noted among subfactors associated with fewer composites, such as the West census region, the 0 14 year age group, and the non Caucasian race group. The estimated concentrations for most of the 17 semivolatile compounds appear to increase with age group according to Table 7 3. This result has been observed in data analyses on other " ATS datasets ( e. g., FY82 and FY87). Similar trends consistent across the analyzed compounds are not as apparent among census regions, race groups, and sex groups. Statistical conclusions on these effects are based on the hypothesis tests in the next section.

7.3 HYPOTHESZS TESTING Statistical hypothesis tests were conducted for each of the 17 semivolatile compounds included in the statistical analysis to determine if there are statistically significant differences in average concentrations between individuals from different geographic regions, age groups, race groups, and sex groups. The tests were based on likelihood ratio tests using the additive model analysis and were described in Section 6.2.2.

Table 7 8 lists the attained significance levels for the tests associated with the four analysis factors. In addition, a test was performed to note significance of the effect that being in Batches 4 and 5 has on the measured concentration; this factor was significant among the QC sample data. The attained signifi. cancelevel is t. hesmallest level at which the test can result in rejection of the hypothesis th . atno differences are 7 27 1 Table 7 8. Significance Levels from Hypothesis Tests for Differences Between Demo raphic Groups for " ATS FY86 Sdvolatiles8)

1 Nonene 0.702 0.751 0.764 0.695 Hexyl acetate 0.301 0.826 0.672 0.445 Data adjusted for surrogate recoveries ( see Section 5.2). ( 2) Likelihood ratio tests based on the x2 distribution. ( 3) Likelihood ratio tests based on the x4' distribution. t4) Likelihood ratio tests based on the x$ distribution. ( 5) p, p DDE concentrations use the following response ion: m/ z= 316. ( 6) Data results from Batch 1 not included. Corrected ( see Section 5.1.21 !

* Significant at the 0.05 level. *.* Significant at the 0.01 level.

7 28 I present between the population averages. For example, the differences among estimated averages of Beta BHC in the four census regions could only be considered significant at the 0.947 ( 94.7%) level of significance, while the differences in age group average is significant at the 0.015 ( 1.5%) level. A significance level of less than 0.05 ( 5%) is generally required to declare statistical significance.

An apparent conclusion from Table 7 8 is the presence of significantly different estimated average concentrations among the age groups for pesticides, hexachlorobenzene, and PCBs. From Table 7 3, the older age group ( 45+ years) had the highest estimated average concentration for these compounds, and the youngest age group ( 0 14 years) had the lowest estimate. The disparity between the older age group and the others is more apparent for the PCBs. Statistical significance was also observed among census regions for three pesticides, hexachlorobenzene, naphthalene, and three PCB congeners. Levels of p, p DDT and hexachlorobenzene were highest in the West census region, while for some PCB congeners, levels were lowest in the West census region. However, a consistent trend across the compounds was not observed with census region as was observed with age groups. The difference in estimated average concentration between Caucasian and non Caucasian and between male and female donors were not statistically significant for any of the modelled compounds. The effect of Batches 1 3 versus 4 5 on the measured concentrations in composite samples was also not significant for any of the compounds.

7.4 OUTLIER DETECTION Prior to conducting the statistical analysis of the FY86 " ATS data, outlier detection procedures were performed to identify possible data entry errors and errors associated with the analytical method. The outlier detection process was performed in multiple stages by Westat, Battelle, and EPA. MRI reviewed all findings of this process, identified a list of changes to data values resulting from their review, and notified the " ATS project team of these changes. Battelle corrected the database according to MRI's review prior to performing the final statistical analysis. Westat performed statistical outlier analysis on the following types of data:

I measured concentrations of native analytes,

I internal quantitation standard recoveries,

I LODs, and rn percent lipid values for composite and QC samples.

The methods and findings of these analyses are presented in Rogers ( 1991). The procedure consisted of three approaches: logic checks, formal outlier identification procedures, and informal outlier identification procedures. Logic checks were performed prior to database completion, to identify obvious data inconsistencies or coding errors. For example, by printing records with inconsistent entries, the logic check procedure would reveal records having recorded concentrations but a data qualifier of " not detected". The formal approach to outlier identification in Rogers ( 1991) assumed that the concentrations and recovery data followed a lognormal distribution, and the percent lipid data followed a normal distribution. A mathematical model was fit to the data, and the extreme studentized deviate ( ESD) test was applied to the residuals of the model. This test considered the ratio of the maximum residual to the standard deviation of the residuals. Outliers were identified if this ratio exceeded the appropriate critical value given the significance level ( 1% or 5%). The form of the simple linear regression models varied. amongthe different types of data ( see Table 2 in Rogers ( 1991)). Once formal outlier identification procedures were completed, informal identification procedures noted any 7 30 additional data which may be in question. These procedures included normality tests on residuals for individual compounds, multivariate tests across multiple compounds ( identifyingdata points which do not conform with a multivariate normal distribution), boxplots to compare measurements of different types, and special outlier comparison tests for the LODs. In addition to the approach documented in Rogers ( 1991) to identify outliers among native compound concentrations in composite samples, Battelle identified additional potential outliers by fitting the additive model ( Chapter6) to the preliminary FY86 semivolatile data. Residuals exceeding two standard deviations from zero were reported. To illustrate patterns due to analysis order and batch, time series plots of the FY86 data were produced. Any outliers and questionable data points were highlighted in these plots. These data plots and listings of statistical outliers were delivered to EPA and to MRI for review. A total. of 50 data points were identified as outliers from the procedures in Rogers ( 1991). These data points included 24 quantitative concentrations, 6 qualitative concentrations, and 20 recoveries. Of these points, eight were changed as a result of review by MRI. The findings of the outlier analysis identified unusually low surrogate recoveries for two samples, implying that the reported concentrations were suspect for these samples. The outlier report also noted that recoveries in Batch 1 were lower than in later batches, apparently due to changes in lab procedures. These findings supported the need to consider effects of batch in statistical analyses and to correct data for surrogate recoveries. Forty four additional data points were identified as potential statistical outliers as a result of fitting the additive model to target compound data. Review of these data by MRI resulted in changes to 16 of the data points. Battelle made all data corrections to the master database before proceeding with the statistical analysis.

7 31 ( JC? i; 1333 However, as a result of the data review, some of the data points identified in the outlier detection procedure either did not require modification or remained influential after modification. Thus these data points contributed to increased error in fitting the additive model and to inflated variability in parameter estimation and hypothesis testing. The most influential data points are documented in the following section.

7.5 MODEL VALIDATION As part of the commitment to overall data quality, three types of analyses were performed to evaluate the adequacy of the additive model for use on the FY86 " ATS semivolatile data on the seventeen target compounds. All three analyses were based on comparisons of the observed ( i. e., measured) and predicted concentrations for the composite samples. Predicted concentrations were calculated using the IWGLS method applied to the additive model ( Chapter 6). Residuals, which were also used in the model validation analysis, were calculated by taking the differences between the observed and predicted concentrations. Model validation analyses included: a residual plots,

i I noma1 probability plots, and a R squared analysis. The use of Shapiro Wilk tests for normality was also considered. However, in this application, the Shapiro Wilk test was not appropriate because the data were correlated and variances increased with increasing concentrations. In several of the target compounds, the residual plots ( residuals versus predicted concentration) confirmed the model assumption that the variance of the measured concentrations increases with the average concentration. In addition, these plots showed that the distribution of residuals tended to be symmetric about zero across all predicted concentrations. For some compounds, the extent to which residuals were symmetric about zero was less evident at low concentrations, where predicted levels tended to be larger than the observed level. This finding indicates that the relationship between measured concentration and the model predictors may not be as linear in low concentration ranges relative to larger concentration ranges.

Also, the low concentration range can include a substantial number of measured concentrations at or below the detection limit. For compounds whose non detect percentage approached 50%

( such as octachlorobiphenyl, l nonene, dieldrin, and tetrachlorobiphenyl), the predicted concentrations in areas close to the detection limit may be more biased in portraying the true concentration than predicted concentrations in higher detectable ranges. The presence of unusually high or low data points also contributed to an overall lack of fit of the model to the observed data. The data points observed to be among the most " influential1Ito the model fitting are presented in Table 7 9. The result of fitting the model while including the influential data points is either an underestimate or overestimate by the fitted model in certain concentration ranges. Normal probability plots for most target compounds resembled a linear pattern, supporting the normality assumption for the errors. However, the linearity assumption for some compounds did not hold in areas of extremely large or small concentrations. This is explained by the larger variances associated with these concentrations, and by the presence of influential data points with large positive or negative residuals. Table 7 10 lists the R squared correlations between the observed and predicted concentrations calculated for each target compound. R squared can be interpreted as the percent of the total variability in the observed concentrations that can be explained by the additive model. The correlations range from 12% ( naphthalene) to 65% ( tetrachlorobiphenyl). The qualitative compounds have low R squared values, indicating that their categorical concentrations are not highly correlated with 7 33 e". W<'$?> S Table 7 9. Measured Concentrations with High Influence on Detedning the Additive Model Fit ACS8600270 1 ACS8600065 I I ACS8600163 1 ACS8600163 ACS8600314 ACS8600207 ACS8600350 ACS8600332 ACS8600225 ACS8600421 ACS8600127 ACS8600092 ACS8600092 I ACS8600289 ACS8600289 . p8p DDT 17922 1 1214 . OxychIor'dane@)

17942 I ' 39.2 . I 17946 I 306 Trans nonachlor 17946 510 Hexachlorobenzene 17986 123 17968 176 17948 192 Naphthalene 17965 66.9 17939 99.0 17924 70.5 Tetrachlorobiphenyl 17959 249 17909 217 Hexachlorobiphenyl 17909 I 1123 Heptachlorobiphenyl 17919 888 Octachlorobiphenyl 17919 322 7 34 I . 886 1 148.7 I I 139 I 264 67.5 81.1 96.5 23.5 24 6 I 26.3 1 124 146 I 493 I 376 1 142 . Table 7 9, ( cont,)

1 Nonene ACS8600181 17960 72 8 I 261 Hexyl acetate ACS8600458 3.7938 459 45.8 ACS8600252 17926 369 74.2 ACS8600430 17921 729 233 p, p DDE~)

ACS8600163 17946 10716 4062 ACS8600341 17958 11859 5599 Dieldrin(*)

ACS8600458 I 17925 I 212 I 58.8 ACS8600225 p 17939 1 278 I 194 Data adjusted for surrogate recoveries ( see Section 5.2). ( 2) Batch 1 results not included in statistical analysis. p, p DDE concentrations use the following response ion: m/ z= 316. ( 4) Corrected ( see Section 5.1.2).

7 35 Table 7 10. R Squared Correlation Between Observed Concentrations and Concentrations Predicted by the Additive Model for " ATS FY86 Semivolatiledl)

Pesticides PIP DDT Beta BHC Heptachlor epoxide Oxychlordane Trans nonachlor Dieldrin 8 Chlorobenzmea 1,4 Dichlorobenzene Hexachlorobenzene I PAIls INaphthalene Hexachlorobiphenyl Heptachlorobiphenyl Octachlorobiphenyl 1 Nonene I Hexyl acetate , 1 31 43 55 43 55 13 29 46 12 61 47 37 23 14 ( I) R squared is the square of the Pearson correlation coefficient. It *

represents the percent of variability in the data that is explained by the additive model. Data adjusted for surrogate recoveries ( see Section 5.2).

7 36 predicted values. Note that these R squared values are not as high as seen with dioxins and furans in the FY87 " ATS ( USEPq, 1991). This does not necessarily imply, however, that the additive model is an inadequate fit to the semivolatile compound data. Instead, low R squared values may indicate that the estimated model effects are small relative to the random error observed in the measured concentrations. The random error is increased by the presence of influential observations such as those in Table 7 9.

7 37 I ,

8.0 COMPARISON WITH RESULTS FROM PREVIOUS SURVEYS IN THl3 NHATS PROGRAM The FY86 " ATS is one of three surveys in the " ATS program to use HRGC/ MS analytical methods in measuring the prevalence and levels of semivolatile organic compounds in composited adipose tissue samples. Prior to the FY86 survey, the FY82 and FY84 surveys also performed analysis of semivolatiles on composite samples using HRGC/ MS methods. The " ATS FY86 sampling and data analysis approach was designed to allow valid statistical comparisons to be made between the FY86 results and the results from these two surveys. The NHATS FY82 Broadscan Analysis Study ( Mack and Panebianco, 1986) was the first " ATS campaign to employ the HRGC/ MS method in characterizing an expanded chemicals list. The objective of the FY82 " ATS was to identify and characterize additional compounds that persist in human adipose tissue but could not be measured with less selective analytical techniques. The FY84 NHATS was designed to establish the comparability of the HRGC/ MS and PGC/ ECD analytical methods ( Westat, 1990). The FY84 " ATS revealed that issues in method comparability were not totally resolved for many of the target semivolatile compounds. This chapter presents comparison of the FY86 " ATS results with the results from the NHATS FY82 and FY84 semivolatile analyses. There are several differences in the designs and analytical procedures used in these three surveys. These differences are documented in Section 8.1. Only the semivolatile compounds analyzed in the FY86 NHATS and in at least one of the FY82 and FY84 NHATS are included in comparisons. For each of these compounds within each survey, Section 8.2 presents average limits of detection ( LODs) and the percentages of detected results among the samples. Statistical procedures were used to compare these detection percentages across surveys. Section 8.3 presents two approaches to calculating descriptive statistics in summarizing measured concentration data within each of the three surveys at the national level. Finally, statistical comparisons were performed on only " hose compounds detected in at least 50%

of the composite samples within each survey. Section 8.4 presents results of fitting the additive model to these compounds within each survey.

8.1 COMPARISON OF DESIGN AND ANALYTICAL PROCEDURES 8.1.1 CornParison of Studv Desicms Similar sampling designs were used for collecting tissue specimens in the FY82, FY84, and FY86 NHATS. A discussion of the FY86 sampling design is found in Chapter 2 of this report. The primary difference in sampling designs between these three ' surveys is the method of stratification. Prior to the FY8S NHATS, MSAs were selected from strata defined by the nine U. S. Census divisions. Beginning with the FY85 " HATS, sampling strata were redefined to be the seventeen geographic areas that resulted from the intersection of the Census divisions and the ten EPA regions ( Table 2 2).

A controlled selection technique ( Mack et. al., 1984) was used to maximize the probability of retaining MSAs from one survey design to another. Table 8 1displays the number of specimens and composites associated with each MSA for each survey. Except for double collection MSAs, no MSA contributed more than the quota of 27 specimens to the FY86 NHATS design. This was not true for the FY82 and FY84 surveys, where as many as 72 specimens originated from a single collection MSA. Only five MSAs sampled in the FY82 and FY84 NHATS were not represented in the FY86 NHATS, while only four MSAs were sampled in the FY86 NHATS but not in the other two surveys. It is expected that differences in MSA sampling across the three surveys contribute to only minor differences in concentration estimates. For each census region, age group, sex group, and race group, Tables 8 2 and 8 3 present summaries of the number of specimens and composites, respectively, originating within these 8 2 Q6 1 c'; 131 i 1 Table 8 1. Number of Spechen8 and Composites Within the FY82, FY84* and FY86 " ATS According to MSA 5200 Number of Number of Specimens Composites(')

MSA ( code and location) FY82 FY84 FY86 FY82 FY84 FY86 800 AKRON, OH 0 6 18 0 1 5 ATLANTA, GA 0 0 27 0 0 8 10000 BIRMINGHAM, AL 40 27 0 5 4 0 11200 BOSTON, MA 0 0 25 0 0 4 16000 CHICAGO, IL 17 37 45 8 6 6 16800 CLEVELAND, OH 44 40 27 8 6  6 18400 COLUMBUS, OH 0 0 14 0 0 3 19200 DALLAS FORTWORTH, TX 38 26 27 4 4 3 19600 DAVENPORT ROCK ISLAND MOLINE, IA IL 12 9 0 5 2 0 20000 DAYTON, OH 24 24 9 7 6 3 20800 DENVER BOULDER, CO 10 10 10 2 3 3 21600 DETROIT, MI 9 15 54 3 2,4 23350 ELMIRA, NY 0 17 27 0 4 5 31600 GREEMIIUE SPARTANBURG, SC 14 39 27 9 10 7 42800 LEXINGTON FAYETTE, KY 45 38 27 5 4 4 44800 LOS ANGELES LONG BEACH, CA 0 8 4 0 2 2 46000 LUBBOCK, TX 35 12 0 4 4 0 47200 MpiDISON, WI 40 29 27 8 6 4 49200 MEMPHIS, TN AR MS 0 0 23 0 0 4 50000 MIAMI, FL 26 16 27 9 8 8 56000 NEW YORK, NY NJ 76 0 25 6 0 5 57200 NORFOLK VABEACH PORTSMOUTH, VA NC 72 43 27 10 9 8 59200 OMAHA, NE IA 19 60 27 4 5 5 59600 ORLANDO, FL 43 33 0 9 8 0 61600 PHILADELPHIA, PA NJ 5 7 7 2 1 4 62800 PITTSBURGH, PA 28 25 21 4 4 4 64400 PORTLAND, OR WA 27 15 16 3 4 3 68200 ROCHESTER, MN 41 29 27 4 4 5 69200 SACRAMENTO, CA 4 0 2 1 0 2 71600 SALT LAKE CITY OGDEN, UT 19 22 24 3 3 4 72400 SAN ANTONIO, TX 0 27 0 0 4 0 73600 SAN mcIsco oAIcLAM), CA 0 0 27 0 0 5 78400 SPOKANE, WA 0 15  12 0 3 3 80000 SPRINGFIELD CHICOPEE HOLYOICG, MA CT 56 37 18 3 4 4 82800 TAMPA STPETERSBURG, FL 0 7 8 0 4 3 88400 WASHINGTON, DC MD VA 19 16 12 8 6 5  Totals: 763 689 671 46 46 50 (' 1 Column entries indicate the number of composites having at least one specimen from the given MSA. The total at the bottom of each column indicates the total number of analyzed composites in the survey. Since specimens within a composite can originate from more than one MSA, this total is not equal to the sum of the column entries.

8 3 Table 8 2. Total Number of Specheas Included in Compusite Samples Analyzed ip the FY82, FY84, and FY86 NEATS, by Subpopulation and Across the  tire Study Subpopulation Northeast North Central South West 0 14 years 15 44 years 45+ years a Male Female White Nonwhite Total # of Specimens Number of Specimens (%

FY82 FY84 Census Region 166 ( 22%) 86 ( 12%) 206 ( 27%) 249 ( 36%) 331 ( 43%) 284 ( 41%) 60 ( 8%) 70 ( 10%)

Age Group 178 ( 23%) 142 ( 21%) 312 ( 41%) 2' 66 ( 39%) 273 ( 36%) 281 ( 41%)

412 ( 54%) 352 ( 51%) 351 ( 46%) 337 ( 49%)

Race 632 ( 83%) 579 { 84%) 131 ( 17%) 110 ( 16%)

763 689 of Total) 1980 Census FY86 123 ( 18%) 248 ( 37%) 205 ( 31%) 95 ( 14%)

108 ( 16%) 221 ( 33%) 342 ( 51%)

315 ( 47%) 356 { 53%)

526 ( 785;) 145 ( 22%)

671 %

26% 22% 33% 19%

23% 46% 31%

49% 51%

83% 17% Table 8 3. Total Number of Composite Samples Analyzed in the FY82, FY84, and FY86 " ATS, by Subpopulation and Across the Entire Survey Subpopulation Northeast North Central South West 0 14 years 15 44 years 45+ years Mixedt3) Male only Female only Mixed( 3) White only Nonwhite only Number of Composites (%

FY82 FY84 Census Region(')

9 ( 20%) 8 ( 17%) 12 ( 26%) 13 ( 28%) 19 ( 41%) 18 ( 39%) 6 ( 13%) 7 ( 15%)

Age Group(')

12 ( 26%) 10 ( 22%) 17 ( 37%) 19 ( 41%) 17 ( 37%) 17 ( 37%)

.35 29 6 ( 55%) 8 ( 47%) 5 ( 45%) 9 ( 53%)

Race@)

29 25 11 ( 65%) 16 ( 76%) . 6 ( 35%) 5 ( 24%)

+

Total # of Composites 46 46 of Total)

FY86 9 ( 18%) 16 ( 32%) 15 ( 30%) 10 ( 20%)

10 ( 20%) 16 ( 32%) 24 ( 48%)

18 14 ( 44%) 18 ( 56%)

29 16 ( 76%) 5 ( 24%)

50 1980 Census %

26% 22% 33% 19%

23% 46% 31%

49% 51%

83% 17%

( I) All specimens within a given composite originated from the same census region and age group.

(*) The percentages for sex and race groups are calculated as the total number of pure composites within each study design. For example, 6 of the 11 ( 55%) pure sex composites in the FY82 study design were composed of specimens from ,

males only.

0) Composites containing specimens from both sex ( or race) groups.

8 5 , groups. The distributions of specimens among the geographic and demographic groups were relativ. elysimilar across the three surveys. The FY86 survey had higher percentages of specimens from the. Westcensus region and the nonwhite race group: two groups in which specimens are generally less procurable than other groups. The FY82, FY84, and FY86 NHATS also had comparable composite designs ( Table 8 3). One of the design criteria for compositing FY84 and FY86 specimens was to maintain similarity to the FY82 design ( see Section 3.1). However, the FY86 design stipulated more pure sex composites ( i. e., all male or all female) than the FY82 and FY84 designs in order to more accurately estimate differences in concentrations among the sexes. Sixty fourpercent of the FY86 composites were pure sex composites, compared to less than forty percent of the composites in the FY82 and FY84 surveys. Overall, the percentages of composites within each population group were similar across the three surveys and with the 1980 Census percentages.

8.1.2 Comnarison of Analytical Procedures To interpret differences in estimated concentrations between the three surveys, it is necessary to consider differences in their analytical methods. While some major differences do exist, the methods were otherwise similar between the three surveys. One analytical factor having a large potential effect on data comparability between the three surveys is the type and number of internal quantitation standards ( IQS) and how these standards are assigned to semivolatile compounds. Native compound concentrations were quantified relative to the IQS findings. Only one IQS was used to quantify the semivolatiles in FY82: anthracene dlo. The FY84 and FY86 surveys included three IQS for quantification of semivolatiles: anthracene dlo, benzo( a) anthracene d12, and naphthalene d8. In addition to differences caused by the number and type of IQS assigned to each survey, the method of assigning an IQS to each semivolatile differed between the FY84 and FY86 NHATS. Table 8 4 lists those semivolatiles analyzed in both FY84 and FY86 for which the same IQS'was assigned in both surveys. Table 8 5 lists the semivolatiles with differing IQS between FY84 and FY86.

Differing IQS assignments between surveys must be considered when interpreting differences observed in results from one survey to another. Average concentration estimates in the FY86 " HATS were based on measured concentrations adjusted for surrogate recoveries ( Chapter 7). The adjusted concentrations are more likely to resemble actual concentrations in the sample than unadjusted measured concentrations. Thus for comparison purposes, it was necessary to obtain average concentration estimates in the FY82 and FY84 surveys based on surrogate adjusted concentrations. Like the IQS, surrogate compounds were matched to specific semivolatile compounds within each survey ( Table 5 2) for adjustment purposes. However, the types of surrogate compounds included in each survey also differed. Thus in conducting the comparison, it is noted when surrogate compounds differed among the surveys. Another issue to consider is that the FY82 and FY86 analyses were conducted at Midwest Research Institute, while the FY84 analysis was performed at Colorado State University. Thus interlaboratory variation is also introduced when comparing FY84 results with the other two surveys. Other than the differences noted above, the techniques in the analytical methods for semivolatile analyses were essentially equivalent between the, three surveys. The flow diagram in Figure 4 1 ( Chapter 4) illustrat. esthe order of activities in each campaign. Each procedure required fortification with IQS and surrogate compounds, extraction, \

removal of bulk lipid, separation, cleanup, and quantification. Extraction was achieved with methylene chloride using a Tekmar a 7 C, fVfEf36 Table 8 4. Semivolatile Compounds Quantitated Using the Same Internal Quantitation Standards ( IQS) in NEATS FY84 and FY86.

IQS: Benzo ( a) anthracene du PIP DDT O, p DDT PIP DDE O, p DDD TRANS NONACHLOR MIREX CHRYSENE HEXACHLOROBIPHENYL HEPTACHLOROBIPHENYL OCTACHLOROBIPHENYL' NONACHLOROBIPHENYL DECACHLOROBIPHENYL IQS: Anthracene dlo ALPHA BHC BETA BHC DELTA BHC GAMMA BHC( L1NDANE) ALDRIN HEPTACHLOR HEPTACHLOR EPOXIDE OXYCHLORDANE GAMMA CHLORDANE PENTACHLOROBENZENE HEXACHLOROBENZENE ACENAPHTHALENE FLUORENE PHENANTHRENE FLUORANTHENE MONOCHLOROBIPHENYL DICHLOROBIPHENYL TRICHLOROBIPHENYL TETRACHLOROBIPHENYL IQS: Nanhthalene 4 1,2,3 TRICHLOROBENZENE 1,2,4 TRICHLOROBENZENE 1,3,5 TRICHLOROBENZENE NAPHTHALENE Note: Anthracene d10was the only IQS used in the FY82 " ATS.

8 8 Table 8 5, Semivolatile Compounds Quantitated Using Different Internal Quantitation Standards ( IQS) in " ATS  84 and FY86,

o, p DDE 1,2,3,4 TETRACHLOROBENZENE l, 2,3,5 TETRACHLOROBENZENE 1,2,4,5 TETRACHLOROBENZENE I ACENAPHTHENE PENTACHLOROBIPHENYL (') Legend: A = Anthracene dli B = Benzo( a) anthracene d12 N = Naphthalene de B A N A N A N A N A I B I A Note: Anthracene dlowas the only IQS used in the FY82 " ATS.

8 9 Tissuemizer to promote thorough extraction of lipids. Extracts were filtered through anhydrous sodium sulfate. Gel permeation chromatography was applied to separate target analytes from lipid material. Interference separation was achieved through Florisil column fraction procedures.

8.2. LODa AND PERCENT DETECTION SI7MMARIES A total of 54 quantitative semivolatile compounds were analyzed in the FY86 " ATS and also analyzed in one or both of the FY82 and FY84 NHATS. These compounds form the basis of the , descriptiveand statistical comparisons in measured concentrations of target compounds across the three surveys. This subsection summarizes the LODs and the percentages of detected results for these compounds in the FY82, FY84, and FY86 NHATS. An LOD was reported for a compound whenever a trace or not detected reading was reported for the sample. These LODs ( ng/ g lipid weight) are averaged and presented in Table 8 6 for the 54 semivolatile compounds. The LODs were not adjusted for surrogate recoveries prior to averaging. Table 8 6 also documents the percent of composite samples with detected readings within each survey for the 54 compounds. Only compounds with at least 50% detected readings within each of the three surveys were considered for further statistical comparisons. For most compounds, the percentage of samples with Low detected results was consistent across the surveys. detection percentages were reported for most chlorobenzenes ( with the exception of hexachlorobenzene), phosphate triesters, and PAHs, while some pesticides ( such as p, p DDEand beta BHC) had very high detection percentages. To identify those compounds in Table 8 6 where significant differences were present ( at the 0.05 level) in the percent detected value between the three surveys, a chi square test for homogeneity was used. Among pesticides, significant differences in the percent detected value were present for p, p n n hn nnh nn nn nn Et 6 A A~ C. &&&&&&&&&&& C&&& C&&& cv&& C&*&&& U 0000000000000000000 ................... 000000000 ......... a" wooooocu* ooN( Yo( YQ) qlocuo 000000004) 0 m d o m du, Q\* m m YVYYYVWWV m Y Y YYWYWYYYYYYYYYYYYY Eu, ! QfomcumQ) ...... u, u) 0 rl In 0 0'

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dcda YYY ( v Q E am ... mmdc dc * o d a, 2 U G:: a, a  rl u U ar u a, a 8 13 DDT, dieldrin, trans nonachlor, heptachlor epoxide, and mirex, For each of these pesticides, significance was primarily the result of low detection percentages observed in the FY82 survey. For p, p DDT, the detection percentage increased from 67.6% in FY82 to 96% in FY86. This increase may be partially explained by a substantial reduction in the average LOD for p, p DDT in the FY86 survey. Percent detection also increased in FY86 for mirex, from below 15% in both FY82 and FY84 to 32% in FY86, while accompanied by a gradual reduction in the average LOL) across these surveys. Percent detection of hexachlorobenzene increased across the FY82 to FY86 surveys, from 79.1% to 98%. These differences across surveys were statistically significant, but were not accompanied by corresponding reductions in the average LOD. The average percent detection declined in FY86 to 4% for triphenyl phosphate from above 38% in the other two surveys; this decline was statistically significant. Naphthalene was the only PAH with a high percent detection in Fy86 ( 84%), leading to statistically significant differences in the percentages across surveys. Significant differences in percent detection were also observed for diethyl phthalate ( where the average percentage dropped substantially from the FY82 value of 47.6%), di n butyl phthalate ( where the average percentage increased from 50% in FY82 to 100% in FY84), and bis ( 2 ethylhexyl) phthalate ( 0% in FY84 to 78% in FY86). However for di ethyl phthalate, the decreasing percentages were accompanied by decreases in the J average LOD. This indicates that overall measured concentrations have decreased across the surveys for this compound, despite tential Contaminations in the phthalates for the FY86 survey as ested by the QC data analysis. The contamination issue was ident for bis ( 2 ethylhexyl) phthalate, which was detected thod blanks in the FY86 analysis. nificant differences in percent detection across lso observed in the higher order PCB homologs.

8 14 0i,? 203  Average percent detection was low in FY82 compared to the other two surveys for hexa, hepta, and deca chlorobiphenyls, leading to statistically significant differences in percent detection across the surveys. However, a corresponding reduction in the average LOD from FY82 to FY84 did not hold for FY86. In fact, the average detection limit in FY86 for these homologs exceeded that for FY82 in hexa and hepta chlorobiphenyls. This result appears to agree with other findings indicating unusually high concentrations for these homologs in FY86, which may derive from analytical sources rather than environmental sources. Thus while average percent detection in FY86 remained at levels consistent with earlier surveys, occasional increases were observed for some compounds. However, the differences in analytical methods and recoveries observed from one survey to another imply that the differences may be the result of analytical rather than environmental effects.

8.3 DESCRIPTIVE STATISTICS ON MEASURED CONCENTRATIONS A total of 54 semivolatile organic compounds were analyzed in the FY86 NHATS and in at least one of the FY82 and FY84 " ATS. The extent to which statistical comparison of measured concentrations was appropriate among these 54 compounds was determined by initially summarizing the analytical results within each survey through simple descriptive statistics. Some basic differences in the results across surveys were apparent when reviewing summary statistics. The summaries also assisted in interpreting comparison findings. Initially, scatterplots were produced for each of these compounds in order to identify any large differences or patternistic behavior in the measured concentrations between and within the three surveys. Then, two approaches to calculating descriptive statistics were applied to the concentrations. In the first approach, simple arithmetic averages and standard errors of the measured concentrations were calculated. While these statistics summarize the measured concentrations across 8 15 * analytical samples, they are not necessarily good estimates of the national average concentration. A better approximation of national average concentration can result by, takingweighted averages of the observed concentrations. Thus the second approach was to partition the nation into subpopulations, calculate average concentrations within each subpopulation, and weight each average by the 1980 Census population percentage for its respective subpopulation. The second approach can lead to a improved estimate of national average concentration for each compound, regardless of whether further statistical analysis was warranted on the compound concentrations. In the descriptive summaries from both approaches, measured concentrations were defined as the total mass detected, divided by the sample lipid weight. Whenever a compound was not detected within a sample, measured concentrations were taken to be one half of the LOD ( as was done in the statistical analyses). The percent detected values in Table 8 6 indicate the frequency with which not detected results were observed within each compound. The descriptive statistics presented in the following subsections were calculated on measured concentration both adjusted and unadjusted for surrogate recoveries.

8.3.1. Scatternlots of the Sample Concentrations Prior to calculating descriptive statistics, scatterplots of measured concentrations were generated for all compounds detected in at least 50% of the FY86 samples and which were analyzed in the FY82 " ATS and/ or the FY84 NHATS. The scatterplots illustrate any general differences or trends in the concentrations between surveys and between batches within surveys. Plots were generated for concentrations both unadjusted and adjusted for surrogate recoveries. These plots are located in Appendices G and H, respectively. The concentrations are plotted as a function of the analysis date in these scatterplots. Therefore any trends in the concentrations over time or batches are highlighted in these 8 16 BP tl; 2$ 5 plots. In addition, the plotting symbols indicate the age group represented by the result ( 1 = 0 14years, 2 = 15 44 years, 3 =

45+ years). Results in Chapter 7 indicated that age group had a sign'ificant effect on the values of the measured concentrations. These plots illustrate the large extent to which increasing concentrations were associated with increasing age for most compounds. Also, the unadjusted concentrations for the FY86 " ATS appeared to be more variable than in the previous surveys, excluding the effects of occasional outliers. This is especially apparent in plots of PCBs and some pesticides. However, variability appears to be more consistent across surveys when considering surrogate adjusted concentrations. The plots suggest that this is the result of an increase in variability associated with the surrogate adjusted concentrations across all surveys. These scatterplots also illustrate apparent trends from batch to batch within a survey. For example, unadjusted concentrations of beta BHC tend to decrease in later batches in the FY84 analysis. The difference between Batch 1 and the other batches in FY86 oxychlordane concentrations is also evident ( recall that Batch 1 data were qxcluded from statistical analysis for oxychlordane1 . The primary purpose of reviewing scatterplots prior to further statistical summaries or analyses was to depict any obvious differences in results across surveys. Extreme differences in the values of the concentrations between surveys would indicate that statistical techniques may not be necessary in making such conclusions. Extreme differences from one survey to another were not apparent for these compounds based on the scatterplots.

8.3.2. Unweicrhted National Averacres Appendix I presents simple arithmetic averages ( with their standard errors) of the measured concentrations among the 54 compounds for each of the three surveys. The averages were 8 17 calculated across all composite samples in Table 8 3 where measured concentrations were reported for the given compound. Averages were calculated for two endpoints: on measured concentrations adjusted for surrogate recoveries ( Table 1 11, and on unadjusted concentrations ( i. e., the recorded concentrations) ( Table 1 21. The adjustment for surrogate recoveries was performed to more accurately estimate actual concentrations within each sample. The adjustment was described in Section 5.2.

With some exceptions, concentrations or LODs were reported for all composites for a given compound analyzed within a survey. However in the FY84 survey, results for dieldrin, endrin, the phthalate esters, and the phosphate triesters were reported in only 13 of the 46 composite samples. The descriptive statistics in Appendix I were calculated only to summarize the results of the three surveys. Because these summaries ignore demographic effects which were determined to be significantly associated with measured concentration, the descriptive statistics do not necessarily estimate national average concentrations in the respective surveys. Such estimates were obtained from statistical modelling techniques for a limited number of compounds.

8.3.3. Weicrhted National Averacres Estimates of the national average concentration estimates were obtained in this study through statistical modelling procedures rather than from simple descriptive statistics as discussed above. However, statistical modelling was reserved only for those compounds with sufficiently high detection percentages within each survey. Thus an approach was necessary for calculating more accurate national estimates than the simple descriptive statistics, regardless of detection percentages. To do this, averages of composite concentrations were calculated within each of the three age groups ( 0 14years, 15 44 years, 45+ years) and were weighted by the population proportions within each group. Age group was selected for the 8 18 weighting criterion because its effect on measured concentrations was most commonly significant across the demographic groups within each survey. In addition, sufficient numbers of sample results existed to provide sufficient accuracy in averages within each age group. Calculating the weighted national averages was a multistage process. First, unweighted arithmetic averages were calculated for each of the three age groups. Then each age group average was multiplied by the population proportion in that age group ( based on the 1980 Census). These three results were then summed to obtain the final estimate. Tables 8 7 and 8 8 present the weighted national averages for the 54 compounds analyzed in the FY86 and in the FY82 and/ or FY84 NHATS. The results in Table 8 7 are based on the actual measured concentrations, while the results in Table 8 8 are calculated from concentrations adjusted for surrogate recoveries. Results from these two tables indicate that for some compounds, the values of descriptive statistics differ greatly between surveys. Some of these differences may be more likely due to differences in laboratory methods and instrumentation than to differences rooted in environmental effects. For example, the LODs for some of the phthalate esters and phosphate triesters were found to average much higher in the FY82   ATS than in the other surveys ( Table 8 61, leading to higher average measured concentrations among the FY82 composites for these compounds. The largest difference in average concentration occurred with triphenyl phosphate, where the FY82 weighted average was two orders of magnitude higher than in the other two surveys. Most FY82 composite samples report high concentrations for this compound relative to the other surveys. The weighted average concentration for bis ( 2 ethylhexyl) phthalate also increased nearly two orders of magnitude from FY84 to FY86, primarily due to the presence of samples with detected results in FY86 ( 78%, versus no detected 8 19 rlInrtddrldQ) .................. ( Y00000wm000m( Y0ded0 EIo ' Cpm d d dd ( Y YYYYYYYYYYYYYYYYYI 4 * mw atma 40 w oatw atmat( Yw wwmm .................. mmOewmmatwmmd( Y( Ymm( Ym o m Q3 me* dPdd ( Ym d rld P4 * E YYYYYVYYYYYYYYYVYY .................. Q3wc0c mooatatw( YQ)( Ymeatew Q) at qrlm ( Ydt dcye ri AA h AAA A m ON d .. ... c 0 d mrarl rl N Nu) ( Y N( Yd WQ * 9 E . rl Y YYY Y d oat at ... v)' ul Q) at Ob* d W E 8 20 hhnhn hh Q3dOwoatcym c0. a* c a c ewcn ......... OOOOOOOO~

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0 V 8 24 8 25 The findings in this subsection imply that differences between the surveys may not be environmental in nature but may ' dataresults in Table 8 7. One should remember that no general conclusions on true national concentrations should be made from these tables of descriptive statistics unless results of QC data analysis and statistical modelling agree with the findings.

The results from the FY82, FY84, and FY86 NHATS for semivolatiles in composite samples were statistically compared by fitting the additive model ( Chapter 6) . ondata for each survey 8 26 C'CB6% 2. azj separately, calculating marginal estimates and standard errors, and comparing these estimates across surveys through approximate 95% confidence intervale. In order to compensate for differences in recoveries existing across the three surveys, the additive model was fit to the surrogate adjusted concentrations within each survey ( see Section 5.2 on the adjustment method). Previou slypublished results from the FY82 and FY84   HATS may differ from those presented in this section as the additive model and the adjustment for surrogate recoveries were not previously considered in these surveys. While adjusting for surrogate recoveries attempted to remove effects of differing recoveries across surveys and to better estimate actual sample concentrations, other differences in analytical method and design ( documented in Section 8.1) may contribute greatly toward overall differences in the marginal estimates between the surveys.

8.4.1. Semivolatile ComDounds Included in Statistical Coxmarison Statistical comparisons yield useful conclusions only when sufficient numbers of detectable results are available from each survey. Specifically, statistical analyses were performed on only those compounds detected in at least 50% of the composites within each survey. In addition, comparisons were made only on compounds which were not removed from consideration for statistical analysis in FY86 as a result of the QC data analysis ( Section 5.3); thus no phthalates were considered in the statistical comparison. Based on these criteria, the compounds considered for statistical analysis across surveys were the following:

p, p DDT p, p DDE Beta BHC Trans nonachlor Heptachlor epoxide Hexachlorobenzene Tetrachlorobiphenyl Pentachlorobiphenyl 1I j 8 Hexachlorobiphenyl m Heptachlorabiphenyl.

Thus the statistical comparisons were limited to only ten of the most prevalent pesticides, PCB homologs, and chlorobenzenes found in the " ATS over the years. In addition, the PCB parameters introduced in Section <IP_ADDRESS> ( total PCB concentration, chlorobiphenyl distribution across homologs, and chlorination level) were estimated for FY82, M84, and FY86 from the estimated average concentration levels for five PCB homologs resulting from fitting the additive model, The additive model was fitted to data for each of these five homologs ( tetra through octa CB) since these homologs had high detection percentage8 in FY86. The method for estimating these parameters and their standard errors was documented in Section <IP_ADDRESS>.

8.4.2. Fittinu the Additive Mode& The method for fitting the additive model, as well as the form of the model itself, was essentially similar between the three surveys. The primary differences in the model fitting approaches across surveys were as follows:

m The FY86 model fitting included an effect for Batches 1 3 versus 4 5 ( Section 6.1). This effect was not included in the model for either N82 or FY84.

8 For FY82 and FY84, the errors attributable to measurement error and specimen sampling error were combined into one error term, rather than individually estimated as in the M86 analysis, An estimate of measurement error was not determined for these two surveys because FY82 QC data were not readily available, and  84 QC data were not statistically analyzed. Preliminary analyses indicated that measurement error from the FY86 QC data analysis VJ~ S not appropriate for use in the FY82 or FY84 analyses.

8 28 One note should be made in reporting standard errors resulting from the additive model fitting to the FY84 " ATS data. Large absolute error attributable to MSA sampling was observed for p, p DDE, beta BHC, pentachlorobiphenyl, and hexachlorobiphenyl in this survey. When thiserror was included in the formulas for calculating standard errors in the marginal estimates, these standard errors were inflated by two to three orders of magnitude relative to the marginal estimates. Because these errors were likely not an accurate portrayal of the true error, the MSA error was not considered in the additive model fitting in this survey. Thus the calculated standard errors may / J be somewhat underestimated for these four compounds in FY84.

<IP_ADDRESS>. National E8thatee. For the above ten semivolatiles and total PCB concentration, Table 8 9 presents the estimated national average concentrations ( and standard errors) for each of the three surveys, based on fitting the additive model to surrogate adjusted concentrations within each survey. This table also contains the estimated overall chlorination percentage for PCBs within each survey. Along with these estimates, Table 8 9 includes the estimated difference from the FY86 estimate for both the FY82 and FY84 surveys and the significance level for testing that this difference differs from zero. The test was based on the approximate t statistic of the form ( i= S2, 841, where NA,, NASQ, and " 86 are the FY82, FY84, and FY86 national average estimates and SEar SEU, and SE86 are their standard errors, respectively. Approximate significance levels were calculated using the standard normal distribution. More exact significance levels based on the Student t distribution ( with degrees of freedom obtained through Satterthwaite's approximation) was deemed too complex to use in this application;

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