EPA/600/R-21/237 ERASC-022F September 2021 UPDATE ON HI! BENEFITS OF PCB CONGENER-SPECIFIC ANALYSES Ecological Risk Assessment Support Center Office of Research and Development U.S. Environmental Protection Agency Cincinnati, OH ------- NOTICE Mention of trade names or commercial products does not constitute endorsement or recommendation for use. Note Relating to PCB Congeners by Low-Resolution GC-MS - Method 1628 (Not yet approved). The Office of Water of EPA is in the process of developing a low-resolution GC/MS method for congener specific PCB analysis: Method 1628, PotychlorinatedBiphenyl (P( weners in Water, Soil, Sediment, Biosolids, and Tissue by Low-Resolution GC/MS usinj Monitoring (EPA 82 l-R-21-002). The present document (Update on the Benefits of PCB Congener-Specific Analyses) was drafted while method 1628 was under development. The benefits of method 1628 are a) improved sensitivity over Methods 8082A and 608.3, b) total PCBs determined from the sum of the individual PCB congeners and not from the sum of Aroclors, and c) lower analysis costs in comparison to Method 1668C because of the use of low- resolution mass spectrometry instrumentation. Methods 1628 and 1668C are likely to provide similar total PCB determinations; however, for sites where the total dioxin toxicity equivalence (TEQ) of the PCBs are of interest, Method 1668C is likely to provide greater accuracy due to its ability to eliminate interferences from higher homologues for the dioxin-like PCB congeners. Preferred Citation: U.S. EPA (U.S. Environmental Protection Agency). 2021. Update on the Benefits of PCB Congener-specific Analyses. Center for Environmental Solutions and Emergency Response, Ecological Risk Assessment Support Center, Cincinnati, OH. EPA/600/R-21/237. 11 ------- Table of Contents AUTHORS, CONTRIBUTORS. AND REVIEWERS iv Background 1 Review 1 Update 1: Revised Toxicity Equivalence Factors (TEFs) 1 Update 2: Method 1668A migration to Method 1668C 3 Update 3: Discussion of chemical bioavailability 3 Discussion of ERASC Request No. 22 Question 4 Response 4 Conclusions 8 Appendix A 10 Appendix B 19 References 25 in ------- AUTHORS, CONTRIBUTORS, AND REVIEWERS AUTHOR Lawrence Burkhard U.S. Environmental Protection Agency Office of Research and Development Center for Computational Toxicology and Exposure Duluth, MN 55804 REVIEWERS Keri C. Hornbuckle Iowa Superfund Research Program Department of Civil and Environmental Engineering University of Iowa Iowa City, Iowa Kurunthachalam Kannan Department of Pediatrics, and Department of Environmental Medicine New York University School of Medicine New York, New York Rainer Lohmann Graduate School of Oceanography University of Rhode Island Narragansett, Rhode Island ACKNOWLEDGMENTS The first draft of this document was internally (within EPA) reviewed by Robert Burgess (EPA Office of Research and Development, Center for Environmental Measuring and Modeling) and Charles Nace (EPA Region 2, Superfund and Emergency Management Division). Programmatic review of the document was conducted by Andrea Latier (EPA Region 10, Laboratory Services and Applied Science Division) and Marc Greenberg (EPA Office of Land and Emergency Management, Office of Superfund Remediation and Technology Innovation). We would like to acknowledge the efforts of Bruce Pluta (EPA Region 3) in initiating the original request for this document. iv ------- Background: In April 2020, the Ecological Risk Assessment Forum (ERAF) submitted a request to the Ecological Risk Assessment Support Center (ERASC) to provide a review of ERASC Memorandum: Response to Ecological Risk Assessment Forum Request for Information on The Benefits ofPCB Congener-Specific Analyses NCE A- C-1315, ERASC-002F, developed in March 2005. Specifically, a review was needed to 1) ensure that the memorandum reflects the latest state of the science, and 2) update the information as necessary. Results of analyses for PCB contamination on environmental matrices may be expressed in terms ofPCB congener-specific, total PCB, and Aroclor equivalent concentrations. Given the cost ramifications and potential overlap in results from each analysis, should expressing results in terms of all three types of analyses be our standard approach? The product of this work, a few-page memo updating the information in the 2005 ERASC publication, is designed to assist risk assessment practitioners to choose, in a cost-efficient manner, analyses that meet the objectives of the assessment. Review: After reviewing the 2005 ERASC Memorandum: Response to Ecological Risk Assessment Forum Request for Information on The Benefits ofPCB Congener-Specific Analyses. three updates are needed: 1) Toxicity Equivalence Factors (TEFs) in Table 4 have been updated. 2) Method 1668A migration to Method 1668C 3) Discussion of chemical bioavailability Update 1: Revised Toxicity Equivalence Factors (TEFs) In 2006, the World Health Organization updated their mammalian TEFs for PCDD/Fs and PCBs [1,2]. The updated/revised mammalian TEFs are provided in Table 1. 1 ------- Table 1. Original [1] and revised [2] World Health Organization Toxicity Equivalence Factors (TEFs) for Mammals. Values for Birds and Fish were not changed. Congener TEF Mammals [1] Revised TEF Mammal sa [2] TEF Birds [1] TEF Fish [1] Chlorinated dibenzo-p-dioxins 2378-TCDD 1 1 1 1 12378-PeCDD 1 1 1 1 123478-HxCDD 0.1 0.1 0.05 0.5 123678-HxCDD 0.1 0.1 0.01 0.01 123789-HxCDD 0.1 0.1 0.1 0.01 1234678-HpCDD 0.01 0.01 0.001 0.001 OCDD 0.0001 0.0003 0.0001 0.0001 Chlorinated dibenzofurans 2378-TCDF 0.1 0.1 1 0.0001 12378-PeCDF 0.05 0.03 0.1 0.05 23478-PeCDF 0.5 0.3 1 0.5 123478-HxCDF 0.1 0.1 0.1 0.1 123678-HxCDF 0.1 0.1 0.1 0.1 123789-HxCDF 0.1 0.1 0.1 0.1 234678-HxCDF 0.1 0.1 0.1 0.1 1234678-HpCDF 0.01 0.01 0.01 0.01 1234789-HpCDF 0.01 0.01 0.01 0.01 OCDF 0.0001 0.0003 0.0001 0.0001 Non-ortho-substituted PCBs PCB-77 3,3',4,4'-TeCB 0.0001 0.0001 0.05 0.0001 PCB-81 3,4,4',5-TeCB 0.0001 0.0003 0.1 0.0005 PCB-126 3,3',4,4',5-PeCB 0.1 0.1 0.1 0.005 PCB-169 3,3',4,4',5,5'-HxCB 0.01 0.03 0.001 0.00005 Mono-ortho-substituted PCBs PCB-105 2,3,3',4,4'-PeCB 0.0001 0.00003 0.0001 0.000005 PCB-114 2,3,4,4',5-PeCB 0.0005 0.00003 0.0001 0.000005 PCB-118 2,3',4,4',5-PeCB 0.0001 0.00003 0.00001 0.000005 PCB-123 2',3,4,4',5-PeCB 0.0001 0.00003 0.00001 0.000005 PCB-156 2,3,3'4,4',5-HxCB 0.0005 0.00003 0.0001 0.000005 PCB-157 2,3,3'4,4',5'-HxCB 0.0005 0.00003 0.0001 0.000005 PCB-167 2,3',4,4'5,5'-HxCB 0.00001 0.00003 0.00001 0.000005 PCB-189 2,3,3',4,4',5,5'-HpCB 0.0001 0.00003 0.00001 0.000005 a Revised values are in bold font. 2 ------- Update 2: Method 1668A migration to Method 1668C EPA has updated Method 1668A: Chlorinated Biphenyl Congeners in Water, Soil, Sediment, Biosolids, and Tissue by HRGC/HRMS [3] twice since the publication of the 2005 ERASC Memorandum. With Revision B [4], the key change was updating the quality control (QC) acceptance criteria based upon validation studies of the method. The revision also pointed the user to the updated mammalian TEFs [2], With Revision C [5], the QC acceptance criteria were revised slightly. With both revisions, minor clarifications and corrections for typos in the method were provided. With all three versions of the method, reports contain: Concentrations of the 12 polychlorinated biphenyls (PCBs) designated as the most toxic by the World Health Organization (WHO): congeners 77, 81, 105, 114, 118, 123, 126, 156, 157, 167, 169, and 189. Concentrations of the remaining 197 PCBs, approximately 125 of which are resolved adequately on an SPB-octyl gas chromatographic column to be determined as individual congeners. The remaining approximately 70 congeners are determined as mixtures of isomers (i.e., co-eluting isomers). Estimates of homolog totals by level of chlorination (LOC), and estimates of total PCBs in a sample by summation of the concentrations of the PCB congeners and congener groups. Update 3: Discussion of chemical bioavailability In the 2005 ERASC Memorandum, there is a fair amount of discussion on the weathering of PCBs from the standpoint of PCB mixtures in environmental samples not resembling the commercial Aroclor mixtures. Clearly, environmental fate and transport processes change the composition of the mixtures after their release into the environment. There is also some discussion of the impacts of bioaccumulation processes on the patterns of PCB congeners in biota where higher chlorinated congeners bioaccumulate more than lower chlorinated PCB congeners, and some congeners are biotransformed more quickly than others. Since the publication of the 2005 ERASC Memorandum, much has been learned about the partitioning behavior of 3 ------- PCBs and the importance of different types of organic carbon in sediments and soils. These other organic carbon phases, beyond that arising from the diagenesis of plant materials, are black carbon (e.g., soot, chars, charcoal, coke, lampblack) and coal. When present, black carbon dramatically lowers the bioavailability of the PCB congeners in sediments and soils [6], Therefore, when present, these other phases will further alter compositions of the congeners accumulated in biota. One should note that the addition of activated carbon phases to sediments have been evaluated as remedial options at some contaminated sediment sites [7], The addition of the activated carbon lowers bioavailability of the contaminants, and subsequently, lowers the residues in the biota exposed to the sediments, either directly or indirectly via their diet. Most advances in understanding bioavailability of PCBs arises from the use of passive samplers to directly measure the bioavailable PCBs in sediments and if further details on passive sampling are needed, the reader should consult US-EPA (2012) [8], Discussion of ERASC Request No. 22 Question The 2005 ERASC Memorandum recommends "... results of analyses for PCB contamination in environmental matrices be expressed in terms of PCB congener-specific, total PCB, and Aroclor equivalent concentrations." The question in the review request is "Given the cost ramifications and potential overlap in results from each analysis, does the recommendation really reflect what should be our standard approach?" Response: EPA's congener specific Methods 1668A, 1668B, and 1668C (henceforth referred to as Method 1668C) reports concentrations of PCBs on a congener-specific basis, by total PCB, and by homologue totals. In addition, concentrations of the 12 polychlorinated biphenyls (PCBs) designated as toxic by the World Health Organization (WHO) and their total dioxin toxicity equivalence (TEQ) are reported. Note, the TEQ is computed by summing, for all 12 congeners, the products of their individual concentrations and individual TEF values. Method 1668C does not report concentrations on an Aroclor basis. However, as described later 4 ------- in the document, Aroclor equivalent concentrations can be calculated from the congener-specific PCB concentrations. Method 1668C requires sample cleanup and analysis using high resolution gas chromatography with high resolution mass spectrometry detection. Minimum levels of detection for sediments and soils are approximately 1-2 ng/kg-dw per congener and rise to 7.4 ng/kg-dw for several co- eluting congeners [5], SW-846 Method 8082A [9] provides quantifications of total PCB based upon Aroclors. In the method, Aroclors most representative of the PCBs in the sample are determined and are subsequently used to quantify the amount of PCB as Aroclors in the sample. Identification of the representative Aroclors can be problematic and their determination depends upon the experience of the analyst, complexity of the samples from the site, technique used to identify the composition, and the degree of similarity of the PCBs in the sample to Aroclors as sold. Once identified, individual Aroclors are quantified using 3 to 5 characteristic peaks in the mixture and one of the characteristic peaks is considered "unique" to the individual Aroclor. The quantifications from the 3 to 5 characteristic peaks in each Aroclor are averaged to yield a reported concentration for the individual Aroclor and the sum of the quantifications for the individual Aroclors yields total PCB. Depending upon Aroclors selected as being representative and the characteristic peaks used by the laboratory, analyses by different laboratories can yield different compositions of the Aroclors. For example, one laboratory may report a total PCB concentration of XX ug/kg with AA:BB composition of Aroclors 1242:1254 while another laboratory might report total PCB concentration of YY ug/kg with CC:DD:EE composition of Aroclors 1242:1248:1254. Further, there are cases where one laboratory reports the PCBs in the sample as Aroclor 1242 and another laboratory reports the PCBs as Aroclor 1248. Method 8082A uses gas chromatography (GC) with electron capture detection (ECD) and is subject to interferences from other contaminants including organochlorine pesticides, phthalates, and other chemicals as well as some sediment constituents (e.g., sulfides). When PCBs are weathered such that the Aroclor mixtures (as sold) don't match the sample's actual pattern, accurate assignment of the representative Aroclor(s) can become challenging [10-12], Minimum levels of detection for sediments and soils are typically 50-70 ug/kg per Aroclor using Method 8082A [10], Depending upon what is selected when ordering analyses with Method 8082A, analyses may be performed with or without sample cleanup. Without sample cleanup, the sample is simply extracted with an organic solvent and the extract concentrated to volume that enables analysis with GC/ECD. With sample cleanup 5 ------- (see SW-846 3600 methods [13]), the sample is extracted, cleaned-up by removing interferences, and then, analyzed using GC/ECD. Data from Method 8082A with cleanup will be more robust and dependable than those from the method without cleanup. Additionally, lower detection limits will be available when sample cleanup is performed. Uncertainties in the analytical results are larger with Method 8082A in comparison to Method 1668C due to several factors. First, Method 1668C measures mass of the individual PCBs accurately, i.e., minimum resolving power of 10,000 (10% valley) at mass 330.9792 (permits measurement of ions with of mass 330.9461 with 10 % overlap in responses). This level of resolution eliminates interferences from chemicals such as chlorinated pesticides, brominated organics, chlorinated diphenyl ethers and other chemicals because of their differences in mass from PCBs. In contrast, Method 8082A uses ECD detection and all halogenated organics respond strongly and yield interfering responses in the quantification data. Second, Method 1668C has much lower detection limits in comparison to Method 8082C, and Method 1668C, with lower detection limits provides more certainty in the results especially when concentrations become low. Third, there is no need to determine the representative Aroclors in the sample with Method 1668C because the quantifications are based upon responses of the individual congeners. With Method 8082A, selection of representative Aroclors can be problematic and can be a source of uncertainty. Fourth, with Method 8082A, if the representative Aroclors don't exactly match the PCB pattern in the sample, there will be some inaccuracy in the quantification. From Table 9 in Method 8082A, accuracy in quantification of soils spiked with Aroclor 1254 at 50 ug/kg ranged from 38% to 144% of the spiked level. As discussed by Erickson [11] and in the method itself [9], Method 8082A provides quantifications of total PCBs and the representative Aroclors used in the quantification. Method 8082A should not, except in some extremely rare cases, be used for source identification or for forensic purposes at contaminated sites [11, 14, 15]. The recommendation in the 2005 ERASC Memorandum states: "It is recommended that the results of analyses for PCB contamination in environmental matrices be expressed in terms of PCB congener-specific, total PCB, and Aroclor equivalent concentrations". The recommendation does not inherently require analysis in the laboratory by a method that quantifies PCBs in the samples on an Aroclor basis but rather an expression of "Aroclor equivalent concentrations". Aroclor equivalents could be determined by post- 6 ------- processing of the laboratory data using the compositions of the Aroclor mixtures (see Frame et al [16, 17] for composition of Aroclors) and some type of regression/pattern matching approach to yield total PCBs on an Aroclor basis. This type of determination could be performed on a congener basis or homologue basis (Note, Table 3 in the 2005 ERASC Memorandum provides homologue compositions of 5 Aroclor mixtures). Currently, this type of post-processing is not done. An example of this type of processing is provided in Appendix A of this document. From a cost perspective, Method 1668C when performed for all congeners is the most expensive analysis. Other options are available depending upon laboratory abilities and these include limiting the Method 1668C analyses to just the 12 PCBs with dioxin-like activity or to the homologue totals for mono- through deca- homologue groups. The latter two analyses will be less expensive, e.g., V2 to % the cost of the complete Method 1668C for all congeners. Analyses by Method 8082A will be the cheapest, and analyses without cleanup being lower in cost in comparison to those with cleanup. The major differences between Methods 8082A and 1668C are 1) minimum detection levels and 2) accuracy of the total PCBs quantifications. For samples with concentrations greater than approximately 1 mg/kg total PCBs, Methods 8082A with sample cleanup and 1668C will provide similar quantifications of total PCB. When concentrations become less than approximately 1 mg/kg, Method 1668, with its much lower detection limit, is superior to Method 8082A and will provide better accuracy in the quantification of total PCB. Because of the cost differences and time for results to be reported by laboratories, can defensible correlations between total PCBs determined by Methods 8082A and 1668C be developed? Yes, a defensible correlation at most sites can be developed. With the correlation, one could potentially have a majority of analyses performed using the less expensive method, i.e., Method 8082A, and then, adjusted using the correlation to total PCBs determined using the Method 1668. To develop a correlation between total PCBs by Methods 8082A and 1668C, one would need run sufficient analyses with both methods to develop a strong relationship. The correlation/relationship will be site specific and will need to be developed for each site. Paramount in developing the correlation is that the mixture of PCBs across the site is relatively constant, and for most sites, this will be true. In Appendix B, a detailed 7 ------- discussion is provided using data from Portland Harbor Superfund site on developing a correlation/relationship between total PCBs measured using Methods 8082A and 1668C. The above discussion has been focused on EPA's Methods 8082A and 1668C. However, site managers/risk assessors have the flexibility to use other analytical methods for PCB analysis on Superfund site samples. The critical points in using other methods are that they need to be consensus-based, e.g., ASTM, EPA, Standard Methods, or European HORIZONTAL [18] official methods. These methods must provide appropriate and applicable QA/QC, respond to documented DQO needs, undergo interlaboratory validations/round robin trials, formal peer review, and be officially approved and published. Methods using benchtop GC/MS systems instead of high-resolution GC/MS systems like Method 1668C could easily be developed and provide data with more accuracy and reliable results in comparison to Method 8082A. Conclusions At Superfund sites, use of Method 1668C is recommended on samples used in a site's risk assessment. It is not a requirement that all samples be analyzed using Method 1668C but enough samples need to be analyzed to adequately perform the risk assessment and characterize the PCB congener distribution at the site. As discussed above, Method 1668C provides congener level data for the 12 PCBs designated as toxic by the World Health Organization (WHO) and allows determination of the dioxin PCB toxic equivalents (TEQpcb) using their TEFs. Method 1668C also provides total PCBs values. With these data, risks can be evaluated on a total PCB basis, on a total dioxin TEQ basis, and if desired, for any other PCB toxicological endpoint using the appropriate PCB congeners. Following the risk assessment, preliminary remediation goals (PRGs) are developed. PRGs for PCBs are expressed on a total PCB basis. As the site transitions into site cleanup, site mangers must choose between Methods 1668C and 8082A for measuring total PCBs. Total PCBs are measured because PRGs are expressed on a total PCB basis. Use of Method 1668C is recommended because of its lower detection limits, accuracy, and reliability in comparison to Method 8082A. However, Method 8082A is acceptable for the determination of total PCBs, and the method provides quicker laboratory response times and lower analytical costs in comparison to Method 1668C. Selection of the appropriate analytical method will be a site-specific decision. 8 ------- In developing and establishing a long-term monitoring plan for a site, careful consideration of analytical methods is suggested for measuring total PCBs. Use of Method 1668C for the monitoring plan is recommended because of its lower detection limits. Method 8082A can also be used for monitoring quite successfully provided the method is applied similarly over time, e.g., same Aroclors are quantified and sample cleanup is used. What one wants to avoid is switching analytical methods after long term trends are established post remedial completion, e.g., after 15 years of monitoring, switching from Methods 8082Ato 1668C because of detection limit issues. When switching occurs, difficulties in understanding long-term trends can occur and careful consideration of analytical methods initially can avoid this issue. If switching methods from Method 8082A to Method 1668C is required, analyzing samples in parallel for one or two years and then continuing with Method 1668C is recommended. 9 ------- Appendix A: Examples of data processing to derive Aroclor equivalences from congener specific PCB Data To compute the amounts of individual Aroclors best matching the distribution of PCBs in a sample, a linear regression can be performed using the equation: Y. = Yj(Aroclormx fmJ) Where Aroclorm is the amount of the Aroclor "m", Y, is the amount of the "i" homologue in the sample, and fm,i is the fraction of homologue "i" in Aroclor "m". Nine Aroclor mixtures were manufactured, i.e., Aroclor 1221, 1232, 1016, 1242, 1248, 1254, 1260, 1262, and 1268. Table 2A presents the homologue distributions provided in Table 3 in the 2005 ERASC Memorandum for five Aroclors. These values were abstracted from the analyses of Frame et al. [16], In Table 2B, homologue distributions for eight Aroclor mixtures are provided and these values were obtained by averaging all values for an individual Aroclor mixture in the report by Frame et al [16], Frame et al. [16] did not analyze the composition of Aroclor 1668 and amounts of decachlorobiphenyl (homologue 10) were not reported as it was used as an internal standard in the method. The regressions need to constrain the amount of individual Aroclors to be nonnegative and have the intercept set to zero. Further, significance testing on the coefficients is recommended. To illustrate the approach, linear regressions were performed for eight sediment samples from Portland Harbor Superfund site (Table 2C; from [19], as cited in [20]). The regressions provide "Aroclor equivalence" for the eight samples without having separate analyses by method 8082A for the Aroclor quantification. In Table 2D: First Regression, regression results using all eight Aroclor mixtures in Table 2B are provided for the eight sediment samples. All eight sediment samples have negative amounts for a few individual Aroclor mixtures. For each sample, the Aroclor with largest negative amount was removed, and regression was redone with the remaining seven Aroclor mixtures. These regression results are in Table 2E: Second Regression, and the removed Aroclor (not included in the regression) are marked with symbol in Table 2E. This process of removing the most negative Aroclor mixture and redoing the regressions with fewer Aroclors in the regression was repeated for each sediment sample sequentially until all regression 10 ------- coefficients (amounts for each Aroclor mixture) were non-negative (summarized in Table 2F). The number of regressions ranged from 3 to 6 depending upon the individual samples. Once all regression coefficients (amounts for each Aroclor mixture) were non-negative, additional regressions were done by eliminating coefficients (Aroclor amounts) not significantly different from zero (a = 0.05). The least significant coefficient (smallest t-value) was removed (marked with ">" symbol in Tables 2), and the regression redone. The coefficients were checked again, and if another Aroclor coefficient was not significantly different from zero, the process was redone by removing the least significant coefficient (Aroclor). This process was redone until all regression coefficients (amounts for individual Aroclors) were significantly greater than zero, summarized in Table 2G. For clarity, the third, fourth, ... and eighth regressions are provided in Tables 2H through Table 2M to show each regression in the process. As shown in Table 2G, the predicted total PCB from the regressions using homologue amounts align very well with the measured values; i.e., 90 to 104% for regression results using only non-negative criteria for the regression coefficients and 84% to 99% for regression results using non-negative and coefficients significantly greater than zero criteria for the regression coefficients. The Aroclor proportions/equivalences are provided in Table 2G and the standard errors for the regression coefficients averaged approximately 13% of their coefficient values. In Table 4, quantifications using method 8082A are provided for the samples in Table 2. With Method 8082A, the most representative Aroclors were Aroclors 1254 and 1260, and these Aroclors were used in quantification of total PCB in the samples. The regressions with homologues yielded slightly different Aroclor compositions, e.g., some combination of Aroclors 1248, 1254 and 1262 (Table 2G). A more refined analysis of the eight samples was done using individual PCB congener amounts and Frame et al. [16] composition data with a similar regression technique with outlier detection [21], These more refined results (Table 3) suggested compositions similar to the those determined as most representative with method 8082A, i.e., predominantly Aroclors 1254 and 1260. Based on this analysis, when determining Aroclors equivalences from Method 1668C congener data, regressions with congener data is recommended/preferred to use of homologues. These results might or might not be illustrative of other sites and samples. 11 ------- Table 2. Measured and predicted homologue concentrations in Portland Harbor Superfund site samples using homologue Aroclor composition data Table 2A. Homologue distributions from Table 3 in the 2005 ERASC Memorandum Aroclor A1221 A1232 A1242 A1248 A1254 A1260 A1262 A1016 homologue 1 0.008 0 0 0 0.007 2 0.15 0.004 0.002 0.001 0.175 3 0.449 0.22 0.013 0.002 0.547 4 0.326 0.566 0.164 0.005 0.266 5 0.064 0.186 0.53 0.086 0.005 6 0.003 0.02 0.268 0.434 0 7 0 0.006 0.027 0.385 0 8 0 0 0 0.083 0 9 0 0 0 0.007 0 10 0 0 0 0 0 Table 2B. Homologue distributions from Frame et al. [161, average values for each Aroclor Aroclor A1221 A1232 A1242 A1248 A1254 A1260 A1262 A1016 homologue 1 0.69947 0.35236 0.01029 0.00058 0.00015 0.00025 0.00021 0.00859 2 0.24193 0.20984 0.12741 0.00840 0.00141 0.00047 0.00094 0.21839 3 0.04145 0.27673 0.52527 0.27588 0.01035 0.00179 0.00426 0.56202 4 0.01354 0.13413 0.27435 0.49656 0.15213 0.00293 0.01627 0.20789 5 0.00361 0.02532 0.06035 0.19803 0.57952 0.05028 0.01114 0.00312 6 0 0.00146 0.00233 0.01663 0.22723 0.32112 0.48070 0 7 0 0.00016 0 0.00393 0.02841 0.38672 0.31230 0 8 0 0 0 0 0.00022 0.22889 0.16226 0 9 0 0 0 0 0.00059 0.00755 0.01192 0 10 0 0 0 0 0 0 0 0 Table 2C. Measured concentrations in Portland Harbor sediments by homologue group [191 Sample ID LW3- GCA05 W-COO LW3- GCA10 W-COO LW3- GCA11E -COO LW3- GCA12E -C00-R LW3- GCA12 W-COO LW3- GCRO IE- ALT LW3- GCR05W LW3- GCR10W homologue Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw 1 0.0801 3.088 0.4102 0.02977 0.00519 0 0 0.2362 2 0.3384 2.65 5.5577 0.3123 0.0949 0 0.1659 0.4665 3 1.1388 2.4952 41.2783 2.0806 0.2078 0.0834 0.6365 1.9626 4 3.0038 6.4452 226.8955 17.9069 0.7501 0.3808 2.2747 9.2717 5 4.1368 11.5658 663.4454 51.993 2.9382 0.3377 3.6040 20.1122 6 4.01765 15.018 2610.276 222.6493 7.0997 0.1702 2.736 21.4176 7 2.5436 12.0966 2360.599 243.2407 24.1603 0.0538 1.5431 13.8986 8 0.7285 3.5583 685.869 67.786 23.427 0 0.3881 4.2331 9 0.1837 0.4994 41.6 3.392 3.695 0 0.0679 0.9201 10 0.0878 0.246 0.525 0.0564 0.107 0 0.0537 0.454 total 16.2592 57.6625 6636.456 609.4470 62.4853 1.0259 11.4699 72.9726 12 ------- Table 2D. First Regression: Amounts of Aroclors determined using linear regression with measured homologue concentrations in sediments [191 Sample ID LW3- GCA05 W-COO LW3- GCA10 W-COO LW3- GCA11E -COO LW3- GCA12E -C00-R LW3- GCA12 W-COO LW3- GCR0 IE- ALT LW3- GCR05W LW3- GCR10W Aroclor Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw A1221 2088 12891 2976508 339230 -52805 98 1611 11008 A1232 -4189 -25856 -5972242 -680651 105950 -196 -3232 -22085 A1242 170 1058 250087 28483 -4448 7.5 131 914 A1248 302 1846 423580 48286 -7505 14.8 233 1578 A1254 46 266 58346 6603 -1027 2.2 36 243 A1260 24 152 33907 3960 -430 0.9 18 128 A1262 -28 -186 -42458 -5037 746 -1.3 -22 -147 A1016 1603 9887 2278740 259716 -40419 75.1 1236 8434 total 16 57 6804 597 56 0.990 11 73 Table 2E. Second Regression: Amounts of Aroclors determined using linear regression with measured homologue concentrations in sediments [191 Sample ID LW3- GCA05 W-COO LW3- GCA10 W-COO LW3- GCA11E -COO LW3- GCA12E -C00-R LW3- GCA12 W-COO LW3- GCRO IE- ALT LW3- GCR05W LW3- GCR10W Aroclor Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw A1221 0.133 4.500 -1.733 -0.037 - 0.005 0.020 0.382 A1232 - - - - 0.124 - - - A1242 -5.459 -25.616 -125.982 -33.633 -8.657 -0.728 -4.282 -11.595 A1248 5.389 12.456 116.499 23.982 7.563 0.893 4.020 11.756 A1254 5.590 16.585 831.130 47.729 -6.375 0.356 5.126 30.358 A1260 1.823 15.579 2458.322 376.120 127.618 -0.107 1.118 11.769 A1262 4.429 13.147 3483.006 199.171 -68.625 0.233 2.461 22.462 A1016 4.328 21.461 80.862 19.352 4.966 0.382 3.041 7.746 total 16.233 58.114 6842.102 632.684 56.615 1.034 11.503 72.878 13 ------- Table 2F. Regression results after elimination of all coefficients with negative values: Amounts of Aroclors determined using linear regression with measured homologue concentrations in sediments [191 Sample ID LW3- GCA05 W-COO LW3- GCA10 W-COO LW3- GCA11E -COO LW3- GCA12E -C00-R LW3- GCA12 W-COO LW3- gcro ie- alt LW3- GCR05W LW3- GCR10W Regression Coefficients Aroclor Hg/kg-dw Hg/kg-dw Hg/kg-dw Hg/kg-dw Hg/kg-dw Hg/kg-dw Hg/kg-dw Hg/kg-dw A1221 0.210 4.861 - - - - 0.044 0.389 A1232 - - - - - - - - A1242 - - - - 0.485 - - - A1248 4.037 6.116 69.545 9.427 0.099 0.545 2.695 7.724 A1254 5.534 16.323 836.242 49.916 - 0.416 5.190 30.712 A1260 1.736 15.171 2458.084 376.285 55.773 - 1.080 11.715 A1262 4.544 13.685 3482.431 198.603 - 0.126 2.497 22.467 A1016 0.079 1.523 - - - - - - total 16.140 57.678 6846.301 634.232 56.357 1.087 11.505 73.006 Standard error of regression coefficients A1221 0.402 2.007 — — — -- 0.248 1.671 A1232 -- — — — — -- -- -- A1242 -- — — — 16.395 -- -- -- A1248 0.851 4.252 426.478 48.893 16.620 0.065 0.358 2.416 A1254 0.619 3.095 423.591 48.562 — 0.065 0.355 2.397 A1260 1.623 8.114 1220.474 139.919 10.765 -- 1.024 6.907 A1262 1.523 7.612 1141.937 130.916 — 0.060 0.958 6.462 A1016 0.698 3.488 - - — -- -- -- total 0.811 4.053 525.646 60.262 14.649 0.077 0.499 3.369 14 ------- Table 2G. Regression results after elimination of all coefficients not significantly great than zero: Amounts of Aroclors determined using linear regression with measured homologue concentrations in sediments [191 LW3- LW3- LW3- LW3- LW3- LW3- LW3- GCR05W LW3- GCR10W Sample ID GCA05 W-COO GCA10 W-COO GCA11E -COO GCA12E -C00-R GCA12 W-COO GCR0 IE- ALT Regression coefficients Aroclor Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw A1221 > > - - - - > > A1232 - - - - - - - - A1242 - - - - > - - - A1248 4.122 > > > > 0.526 2.697 7.737 A1254 5.509 20.120 > > - 0.464 5.195 30.764 A1260 > 28.083 > 597.333 55.788 - > > A1262 6.078 > 5949.706 > - > 3.445 32.763 A1016 > > - - - - - - Total 15.709 48.202 5949.706 597.333 55.788 0.990 11.337 71.264 Standard error of regression coefficients Aroclor Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw A1221 -- — — — — -- -- -- A1232 -- — — — — -- -- -- A1242 -- — — — — -- -- -- A1248 0.501 — — — — 0.077 0.335 2.569 A1254 0.497 3.916 — — — 0.072 0.333 2.551 A1260 -- 4.529 — 48.818 9.470 -- -- -- A1262 0.461 — 489.835 — — -- 0.308 2.364 A1016 -- — — — — -- -- -- Total 0.594 4.952 489.835 48.818 9.470 0.074 0.572 3.046 R-Squared 0.991 0.922 0.943 0.943 0.794 0.957 0.993 0.990 F value 254.516 47.437 147.534 149.720 34.701 89.590 320.856 221.688 Proportions by Aroclor mixture A1221 > > - - - - > > A1232 - - - - - - - - A1242 - - - - > - - - A1248 26% > > > > 53% 24% 11% A1254 35% 42% > > - 47% 46% 43% A1260 > 58% > 100% 100% - > > A1262 39% > 100% > - > 30% 46% A1016 > > - - - - - - Total PCB: Method 1668C (ug/kg-dw) 16.2592 57.6625 6636.456 609.4470 62.4853 1.0259 11.4699 72.9726 Total PCB: Regression results after elimination of all coefficients with negative values (ug/kg-dw) 16.140 57.678 6846.301 634.232 56.357 1.087 11.505 73.006 %of Method 99% 100% 103% 104% 90% 106% 100% 100% 1668C Total PCB: Regression results after elimination of all coefficients with negative values and after elimination of all coefficients not significantly great than zero (ug/kg -dw) 15.709 48.202 5949.706 597.333 55.788 0.990 11.337 71.264 %of Method 97% 84% 90% 98% 89% 97% 99% 98% 1668C 15 ------- Table 2H. Third Regression: Amounts of Aroclors determined using linear regression with measured homologue concentrations in sediments [19] Sample ID LW3- GCA05 W-COO LW3- GCA10 W-COO LW3- GCA11E -COO LW3- GCA12E -C00-R LW3- GCA12 W-COO LW3- GCR0 IE- ALT LW3- GCR05W LW3- GCR10W Aroclor Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw A1221 0.210 4.861 0.038 0.436 - 0.015 0.080 0.545 A1232 - - - - 1.446 - - - A1242 - - - - 13.030 - - - A1248 4.037 6.116 85.315 15.657 5.643 0.713 2.960 8.886 A1254 5.534 16.323 829.837 47.384 -10.290 0.348 5.082 30.239 A1260 1.736 15.171 2456.315 375.584 59.086 -0.119 1.050 11.584 A1262 4.544 13.685 3485.651 199.877 - 0.248 2.551 22.705 A1016 0.079 1.523 -17.201 -6.828 -15.151 -0.185 -0.292 -1.280 total 16.140 57.678 6839.956 632.111 53.763 1.021 11.430 72.680 Table 21. Fourth Regression: Amounts of Aroclors determined using linear regression with measured homologue concentrations in sediments [191 Sample ID LW3- GCA05 W-COO LW3- GCA10 W-COO LW3- GCA11E -COO LW3- GCA12E -C00-R LW3- GCA12 W-COO LW3- GCRO IE- ALT LW3- GCR05W LW3- GCR10W Aroclor Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw A1221 0.220 5.047 -2.070 -0.401 - -0.007 0.044 0.389 A1232 - - - - 0.674 - - - A1242 - - - - -5.580 - - - A1248 4.109 7.499 69.693 9.456 10.012 0.545 2.695 7.724 A1254 5.505 15.760 836.192 49.907 -9.984 0.417 5.190 30.712 A1260 1.728 15.015 2458.076 376.283 58.959 -0.100 1.080 11.715 A1262 4.558 13.968 3482.452 198.607 - 0.214 2.497 22.467 A1016 > > - - - - - - total 16.120 57.290 6844.342 633.852 54.081 1.068 11.505 73.006 Table 2J. Fifth Regression: Amounts of Aroclors determined using linear regression with measured homologue concentrations in sediments [ 191 Sample ID LW3- GCA05 W-COO LW3- GCA10 W-COO LW3- GCA11E -COO LW3- GCA12E -C00-R LW3- GCA12 W-COO LW3- GCRO IE- ALT LW3- GCR05W LW3- GCR10W Aroclor Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw A1221 > 5.057 - - - -0.007 > > A1232 - - - - -0.079 - - - A1242 - - - - 0.540 - - - A1248 4.124 7.268 69.545 9.427 0.087 0.545 2.698 7.752 A1254 5.500 16.315 836.242 49.916 - 0.416 5.189 30.702 A1260 1.727 29.035 2458.084 376.285 55.773 - 1.079 11.714 A1262 4.560 > 3482.431 198.603 - 0.126 2.497 22.471 A1016 > > - - - - - - total 15.912 57.674 6846.301 634.232 56.322 1.080 11.463 72.639 16 ------- Table 2K. Sixth Regression: Amounts of Aroclors determined using linear regression with measured homologue concentrations in sediments [191 Sample ID LW3- GCA05 W-COO LW3- GCA10 W-COO LW3- GCA11E -COO LW3- GCA12E -C00-R LW3- GCA12 W-COO LW3- GCR0 IE- ALT LW3- GCR05W LW3- GCR10W Aroclor Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw A1221 > 5.305 - - - - > > A1232 - - - - - - - - A1242 - - - - 0.485 - - - A1248 4.122 > > > 0.099 0.545 2.697 7.737 A1254 5.509 20.051 872.275 54.801 - 0.416 5.195 30.764 A1260 > 28.098 2457.383 376.190 55.773 - > > A1262 6.078 > 3474.210 197.489 - 0.126 3.445 32.763 A1016 > > - - - - - - Total 15.709 53.454 6803.869 628.480 56.357 1.087 11.337 71.264 Table 2L. Seventh Regression: Amounts of Aroclors determined using linear regression with measured homologue concentrations in sediments [191 Sample ID LW3- GCA05 W-COO LW3- GCA10 W-COO LW3- GCA11E -COO LW3- GCA12E -C00-R LW3- GCA12 W-COO LW3- GCRO IE- ALT LW3- GCR05W LW3- GCR10W Aroclor Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw A1221 > - - - - A1232 - - - - - A1242 - - - 0.564 - A1248 > > > > 0.526 A1254 20.120 883.611 > - 0.464 A1260 28.083 > 379.073 55.778 - A1262 > 5633.731 214.547 - > A1016 > - - - - total 48.202 6517.343 593.620 56.341 0.990 Table 2M. Eighth Regression: Amounts of Aroclors determined using linear regression with measured homologue concentrations in sediments [191 Sample ID LW3- GCA05 W-COO LW3- GCA10 W-COO LW3- GCA11E -COO LW3- GCA12E -C00-R LW3- GCA12 W-COO LW3- GCRO IE- ALT LW3- GCR05W LW3- GCR10W Aroclor Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw Ug/kg-dw A1221 . - - A1232 - - - A1242 - - > A1248 > > > A1254 > > - A1260 > 597.333 55.788 A1262 5949.706 > - A1016 - - - Total 5949.706 597.333 55.788 17 ------- Table 3. Regression with congener data and outlier detection Sample ID LW3- GCA05 W-COO LW3- GCA10 W-COO LW3- GCA11E -COO LW3- GCA12E -C00-R LW3- GCA12 W-COO LW3- GCR01E- ALT LW3- GCR05W LW3- GCR10W Aroclor Hg/kg-dw Hg/kg-dw Hg/kg-dw Hg/kg-dw Hg/kg-dw Hg/kg-dw Hg/kg-dw Hg/kg-dw A1221 - > - - - - - > A1232 > 8.91 - - - - - > A1242 - - - - > - - > A1248 3.26 > > > > 0.60 1.90 3.93 A1254 4.92 15.16 154.80 - 3.56 0.48 4.92 23.95 A1260 5.22 26.50 4666.74 504.73 10.45 - 3.28 29.87 A1262 > - > 35.73 - - > > Total 13.40 50.57 4821.54 540.47 14.00 1.08 10.11 57.75 Standard error of regression coefficients Aroclor Hg/kg-dw Hg/kg-dw Hg/kg-dw Hg/kg-dw Hg/kg-dw Hg/kg-dw Hg/kg-dw Hg/kg-dw A1221 - - - - - - - - A1232 - 0.49 - - - - - - A1242 - - - - - - - - A1248 0.29 - - - - 0.12 0.22 1.00 A1254 0.31 0.74 58.98 - 0.18 0.10 0.23 1.04 A1260 0.31 0.74 66.43 16.40 0.25 - 0.20 1.04 A1262 - - - 16.15 - - - - Total 0.40 0.99 76.07 15.66 0.28 0.11 0.27 1.38 18 ------- Appendix B: Developing a relationship between total PCBs by Methods 8082A and 1668C To develop a correlation/relationship between total PCBs by Methods 8082A and 1668C, one would need to ran sufficient analyses with both methods to develop a strong relationship. The number of samples analyzed by both methods will be a function of a number of factors including the quality of the analytical data for Method 8082A, range in total PCB concentrations in the samples, and variabilities in composition of the PCBs across the site. If the range in concentrations is too narrow, e.g., less than two orders of magnitude, developing a useful relationship will be difficult. Minimum number of analysis pairs is difficult to assign a priori without background information on the site, and it is recommended that a statistician be consulted in developing a relationship. To develop a relationship, the measurement pairs should be visually examined using an X-Y plot. The relationship between the two total PCB values might be linear, quadratic, or require some type of transformation. Once the form of the relationship is determined, fitting of an appropriate curve to the data would be done using standard regression techniques. One should develop a regression equation with all coefficients being significant (e.g., a = 0.05) and the regression itself being highly significant. As mentioned in the preceding text, developing a relationship will not be a trivial effort, will take a large amount of analytical effort, and potentially, be costly because of the duplication in sample analyses. To illustrate some of the complexities in developing a relationship, data for sediments from Round 3 sampling in Portland Harbor Superfund RI/FS [19] are shown in Table 4. One of the remarkable points in the data set are the low detection limits for 8082A, i.e., approximately 1.5 ug/kg-dw, and these detection limits are much lower than those traditionally observed with Method 8082A [10], In the data set, 4 of the 30 samples were nondetects for all Aroclors and the range in total PCB spans a little over three orders of magnitude. Regression was performed with and without an intercept using logio transformed data with the nondetect values excluded (Table 5). The intercept was not significantly different from zero, and after eliminating the intercept, the resulting relationship for this data set is: l°gl0 Cm-1668C = 0.9496538 X log1Q CM_8082A r2 = 0.905, n = 26 (1) 19 ------- where Cm-i668c is the total PCB concentration measured using Method 1668C and Cm-8082a is the total PCB concentration measured using Method 8082A. Equation 1 is shown in Figure 1 with the 95% confidence and prediction levels for the regression. The 95% prediction levels are slightly larger than an order of magnitude, factor of approximately 13.4, while the confidence levels for the regression are much smaller and range from 1.2 to 2.8-fold for the regression. Eleven of 26 data points (excluding non-detects in Method 8082A) reside within the narrow confidence limits for the regression line, and 25 of the 26 data points reside with the prediction limits for the regression. In Figure 2, the data and equation 1 are shown with lines that are 3-fold and 10-fold above and below the regression line. Twenty-one and 24 of the 26 data points reside within ±3- fold and ±10 of the regression line, respectively. Closer examination of the data reveals two potential outliers in this data set. At your site, such outliers would require further work to resolve and understand such observations. In applying a developed regression equation at your site, you must know the level of uncertainty acceptable for predictions of total PCB from the measurements performed with method 8082A. This will be a site- specific decision and require some internal discussion. With the Portland Harbor data set, the Methods 8082A and 1668C provide very similar results since the coefficient in Equation 1 is not significantly different from 1.00. 20 ------- Table 4. Quantification Results for Total PCBs by Method 8082A and Method 1668C for Portland Harbor Superfund site Method 8082A (ug/kg-dw)+ Method 1668C Sample ID Aroclor Aroclor Aroclor Aroclor Aroclor Aroclor Aroclor Aroclor Aroclor Total PCB Total PCB# 1016 1221 1232 1242 1248 1254 1260 1262 1268 (ug/kg-dw) (ug/kg-dw) LW3-GCA01E-C00 2.3 1.7 4 3.26099 LW3-GCA02W-C10 3.3 3.3 6.6 3.44917 LW3-GCA03W-C00 7.9 9.3 17.2 15.36144 LW3-GCA04W-C00 9.9 8.5 18.4 11.21967 LW3-GCA05E-C00 16 15 31 19.38426 LW3-GCA05W-C00 20 8.6 28.6 16.25923 L W 3 -GC A10 W-COO 29 36 65 57.66246 LW3-GCA11E-C00 3500 3500 6636.4567 L W 3 -GC A12E-C00-R 31 140 171 609.44696 L W 3 -GC A12 W-COO 3.2 2.3 5.5 62.4853 LW3-GCR01E-ALT 0.8* 1.02594 LW3-GCR05W 16 8.6 24.6 11.46993 LW3-GCR10W 120 98 218 72.97256 LW3-GCR12W 21 21 6.83068 LW3-GCRSP01W-1 5.1 5.1 6.55823 LW3-GCRSP06W 11 11 11.88543 LW3-GCRSP08W 82 47 34 163 133.1186 LW3-GCRSP11E 900 900 10.65876 LW3-GCRSP12E 42 92 134 912.14302 LW3-GSP01E 0.7* 2.95495 LW3-GSP03E 31 31 32.13624 LW3-GSP04W 0.75* 160.26001 LW3-GSP05E 14 14 28 18.52396 LW3-GSP07E 16 16 26.24116 LW3-GSP07W 0.65* 1.20213 LW3-GSP08E 7.9 7.9 8.79936 LW3-GSP09W 190 190 699.4293 LW3-GSP10E 16 16 7.73636 LW3-GSP10W 32 32 52.47157 LW3-GSP12W-ALT 29 29 12.19023 + Aroclors with no values were not detected. * Vi Method detection limit. # Total PCB found by summing only the detected congeners. Congeners not detected were not included in the total PCB value. 21 ------- 100000 10000 1000 100 10 1 0.1 0.1 10000 Portland Harbor sediment samples 1 10 100 1000 Method S082A (ug/kg-dw) Figure 1. Concentrations of total PCBs measured using Methods 8082A and 1668C for sediment samples from Portland Superfund site. Blue circle - measurements are above detection limits for Methods 1668C and 8082A. Red circles - measurements are above detection limits for Method 1668C and less than the detection limits for Method 8082A. For Method 8082A, % of the detection limit are plotted for samples where Aroclors were not detected. The solid line is the regression fit to the data, the dotted lines are the 95% confidence limits for the regression lines, and the dashed lines are 95% prediction lines for the regression. 22 ------- Portland Harbor sediment samples 100000 10000 1000 100 10 1 0,1 Oil 10 100 1000 Method 8082A (ug/kg-dw) 10000 Figure 2. Concentrations of total PCBs measured using Methods 8082A and 1668C for sediment samples from Portland Superfund site. Blue circle - measurements are above detection limits for Methods 1668C and 8082A. Red circles - measurements are above detection limits for Method 1668C and less than the detection limits for Method 8082A. For Method 8082A, % of the detection limit are plotted for samples where Aroclors were not detected. The solid line is the regression fit to the data, the dotted lines are 3-fold from the regression line, and the dashed lines are 10-fold from the regression line. 23 ------- Table 5. Regression statistics for Portland Harbor total PCB data by Method 1668C and Method 8082A. SUMMARY OUTPUT Regression Statistics Multiple R 0. .9513213 R Square 0. .9050122 Adjusted R Square 0. .8650122 Standard Error 0. .5378003 Observations 26 ANOVA df ¦ SS ' MS Significance ¦ F ¦ F Regression 1 68.891946 68.891946 238.1916 5.829E-14 Residual 25 7.2307277 0.2892291 Total 26 76.122673 Standard Lower Upper Coefficients Error t Stat P-value Lower 95% Upper 95% 99.0% 99.0% Intercept 0 #N/A #N/A #N/A #N/A #N/A #N/A #N/A X Variable 1 0.9496538 0.0615321 15.433457 2.751E-14 0.822926 1.0763816 0.7781369 1.1211707 SUMMARY OUTPUT Regression Statistics Multiple R 0.7528105 R Square 0.5667237 Adjusted R Square 0.5486705 Standard Error 0.5450046 Observations 26 ANOVA df SS MS F Significance F Regression 1 9.3243382 9.3243382 31.391906 9.116E-06 Residual 24 7.1287203 0.29703 Total 25 16.453058 Standard Lower Upper Coefficients Error t Stat P-value Lower 95% Upper 95% 99.0% 99.0% Intercept 0.1553836 0.2651486 0.5860246 0.5633302 -0.3918562 0.7026235 -0.586221 0.8969882 X Variable 1 0.8666943 0.1546882 5.602848 9.116E-06 0.5474336 1.185955 0.4340409 1.2993478 24 ------- References 1. 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