Anal. Chem. 1981, 53, 523-528
523
Labeled Perchlorination Reagent for Determination of Polychlorinated Biphenyls Lawrence P. Burkhard" and David E. Armstrong Water Chemistry Laboratory, 660 North Park Street, University of Wisconsin, Madison, Wisconsin 53 706
Perchlorinating polychlorinated biphenyls with an isotopically labeled perchlorination reagent resuits in an altered isotopic abundance distribution in the decachlorobiphenyl formed. Analysis of this abundance distribution allows determination of the isomerides and Aroclor mixtures in the original sample. The theory and the mathematics for using this technique are described. A computer sensitivity analysis including both perchlorination and mass spectral measurement errors was performed. The computer sensitivity analysis indicated that extremely pure chlorine37 reagents, extremely accurate ion abundance measurements, and low perchlorlnatlon errors are required for a successful analysis. These requirements make this technique appear to be unsuitable for routine analyses of polychlorinated biphenyls.
Polychlorinated biphenyls (PCBs) are present in the environment as a complex mixture of approximately 125 chlorobiphenyls (I,2). In the United States, nine different mixtures of PCBs were sold under the trade name Aroclor (3) and each Aroclor mixture is composed of approximately 60 chlorobiphenyl compounds ( 2 ) . Thus, an environmental PCB sample may, also, be a blend of different Aroclor mixtures. In the analysis of environmental samples, determining the concentrations of the Aroclor mixtures, the individual chlorobiphenyl compounds, and the total PCB in the sample is of interest. Accurate quantification of PCBs is a difficult analytical problem ( 4 , 5 ) . The analytical problem may be simplified by converting all PCBs to decachlorobiphenyl (DCB) thereby allowing quantification of one compound (6, 7). The major disadvantages of this approach are the conversion of biphenyl to DCB and the loss of all structural information on the chlorobiphenyls in the original sample. The perchlorination technique (61, when performed using an isotopically labeled perchlorinating reagent, could eliminate these disadvantages. This technique would yield information on the percentages of the 11 isomerides (8)of PCBs (biphenyl, mono-, di-, tri-, ..., and decachlorobiphenyl), the Aroclor mixtures, and the total PCB as DCB (corrected for biphenyl) in the original sample. This paper is an examination of the feasibility of this technique for the analysis of PCBs. However, this analytical approach is applicable to many other environmentally significant mixtures. Some of these mixtures include polybrominated biphenyls (PBBs), toxaphene, and chlorinated terphenyls, dibenzo-p-dioxins, dibenzofurans, naphthalenes, phenols, and anisoles. The feasibility of this technique for routine analyses of PCBs is evaluated by use of a computer sensitivity analysis. This analysis creates experimental data with controlled amounts of error which mimic the actual performance of this technique in the laboratory. The isomeride or Aroclor mixture distributions calculated from the experimental data are compared to the known distributions. This comparison enables workable error limits for this technique to be predicted for 0003-2700/81/0353-0523$0 1.OO/O
a successful analysis. These error limits are then compared with known error confines to determine the feasibility and usefulness of this technique. EXPERIMENTAL SECTION Instrumentation. A Finnigan Model 1015 S/Lgas chromatograph-mass spectrometer coupled with a Finnigan Model 6110 data system was employed. The gas chromatograph was quipped with a 0.76 m x 2 mm i.d. glass column packed with 3% SE-30 on 80-100 mesh Chromosorb W-HP. A helium carrier gas flow rate of 25 mL/min, an injector temperature of 240 "C, and a column temperature of 225 O C were used. The separator and transfer line were maintained at 250 O C . The spectra were recorded by using a scan rate of 50 ms/amu, a scan range of m / e 494-514, a 70-eV ionization energy, and a 250-pA emission current. Chemicals, DCB (99% purity) was obtained from Aldrich Chemical Co., Milwaukee, WI. Data Analysis. An average mass spectrum for the DCB molecular ion mwea was computed by averaging all mass spectra above the half-height intensity in the DCB peak in the total ion chromatogram. Background subtraction was carried out on each mass spectrum prior to the averaging. THEORY AND MATHEMATICAL CONSIDERATIONS Perchlorination of PCBs with an isotopically labeled perchlorinating reagent results in an isotopically altered abundance distribution in the DCB formed. The altered abundance distribution in the DCB obtained from a sample is a linear combination of the isotopically altered DCB abundance distributions obtained for the individual isomerides. In matrix form, this linear combination is D = ZIC (1) where D is the data matrix containing the isotopic abundances for the DCB formed by perchlorination of the PCB mixture, IC is the isomeride composition matrix for the perchlorinated PCB mixture, and I is the coefficient matrix composed of the isotopic abundances of DCB formed by perchlorinating each isomeride individually for all isomerides. Matrix D can be measured by taking a mass spectrum of the DCB molecular ion cluster of the perchlorinated sample. Coefficient matrix Z can be calculated by using the formula of Genty (9) or Margrave and Polansky (IO). Matrix IC, the unknown, is found by solving this equation. The isomeride distribution in the sample, possibly a blend of Aroclor mixtures, is a linear combination of the isomeride distributions in the individual Aroclor mixtures. In matrix form, this linear combination is IC = A A C (2) where IC is the isomeride composition matrix, A is the coefficient matrix composed of the isomeride compositions for all Aroclor mixtures, and AC is the Aroclor mixture composition matrix for the perchlorinated PCBs. Matrix IC is obtained from eq 1. Coefficient matrix A must be determined experimentally. Capillary column techniques similar to Webb and McCall(4) or the labeled perchlorination technique could be used to determine the isomeride distribution for each Aroclor mixture. Matrix AC, the unknown, is found by solving this equation. 0 1981 American Chemical Society
524
ANALYTICAL CHEMISTRY, VOL. 53, NO. 3, MARCH 1981
-
Table I. Measured Isotopic Abundances for Xenon and Decachlorobiphenyl
mle 124 126 128 129 130 131 132 134 136
e
xenon relative abundance theoretical measured 0.357 0.335 7.14 98.33 15.17 78.76 100.00 38.82 32.99
0.354" 0.336 7.38 98.81 15.29 78.52 100.00 39.13 33.16
0.367b 0.351 7.32 97.63 15.45 79.00 100.00 38.26 31.53
av random error 0.17 0.38 Reference 15, GC inlet. Reference 15, reservoir inlet. Reference 16. resolution 1 0 000.
In this investigation, coefficient matrix A values for Aroclor mixtures 1221, 1242, 1016, and 1254 were obtained from Mieure et al. (11). For Aroclor mixtures 1232,1248,1260,1262, and 1268 the isomeride compositions were estimated from the data of Webb and McCall (4)and Hutzinger et al. (3). For completeness, biphenyl and DCB were included in the coefficient matrix A. The computer algorithm for solving eq 1and 2 must constrain the solution to nonnegative values. Hilmer and Taylor (12) investigated two techniques for solving this type of problem for calculating chemical species from mass spectral data. The multivariate regression technique (13)was found to be more accurate than the simplex method (14). Thus, a step-down multivariate regression technique was selected for solving both eq 1 and 2. The optimal solution for both matrixes, AC or IC, is the matrix with the most nonnegative and nonzero components. The regression analysis does not find the equation for calculating the solution but rather is used to find the solution; i.e., given D, find IC (eq 1). The accuracy of this calculation is extremely dependent on the accuracy of the cluster ion abundances but extremely independent of the number of cluster ions used (13). Thus, the largest 11 cluster ion abundances were used. For all calculations, abundances were taken from masses 494,496,498, ..., and 514, except for the chlorine-36 calculations which employed masses 494,495,496, and 504. Three different mathematical steps are necessary for calculating the solution. These are (1)calculating matrix I , (2) regressing matrix D on matrix I and obtaining matrix IC, and (3) regressing matrix IC on matrix A and obtaining matrix AC. Henceforth, this set of operations will be called the (mathematical) model. #..,
S E N S I T I V I T Y A N A L Y S I S PROGRAM
Two different input errors are possible in the labeled PCB perchlorination analysis: mass spectral measurement and perchlorination recovery errors. To provide realistic sensitivity predictions, we devised error models for both input errors. These models enable different amounts of input error to be evaluated for both errors simultaneously. Caprioli et al. (15)performed an analysis of mass spectral measurement errors on xenon gas by using mass fragmentography on a Finnigan Model 3100 quadrupole mass spectrometer (Table I). The relationship between the absolute error (difference between the measured and theoretical relative ion abundances) and relative ion abundance in these data is
m/e 494 495 496 497 498 499 500 501 502 503 504 505 506
decachlorobiphenyl relative abundance theoretical measured 21.05 2.83 68.38 9.19 100.00 13.41 86.73 11.60 49.41 6.59 19.34 2.57 5.27
This investigation.
22.82c 3.27 71.52 9.18 100.00 13.07 85.48 10.47 48.10 6.37 19.11 3.19 5.22
22.06d 2.74 70.76 9.47 100.00 12.91 85.40 11.48 47.36 6.30 19.37 2.74 5.01
21.06e 2.74 68.84 8.53 100.00 12.52 86.00 10.64 46.90 5.83 18.15 2.04 4.78
0.80
0.65
0.72
Reference 16, resolution 40 000.
linear and is almost independent of the relative ion abundance. Thus, a conservative error model maintaining a constant error in units of relative ion abundance was used for all ion abundances. Data obtained subsequently to this decision for DCB in this laboratory using Finnigan 1015 S/L GC-MS (quadrupole) and by Kilburn et al. (16) using a Kratos MS5074 mass spectrometer (double focusing) (Table I) revealed a slight correlation between relative ion abundance and the absolute error. The adoption of this conservative model ensures against this possible bias if this correlation does exist. The measurement errors of Caprioli et al. (15) were determined to be normally distributed. Thus, the errors were assumed to be normally distributed in the sensitivity analysis. The mass spectral measurement model for constant units of error is HEi = HPi Ei (3)
+
where HEi is the errored cluster ion relative intensity, HPi is the perfect unerrored cluster ion relative intensity, and Ei is the error calculated as described below. T o obtain the errors used in the model, we employed an average random error function. The function creates 11errors with their absolute sum equaling 11times the average random error (ARE). This error function permits the individual errors to be different, simulating real mass spectra data while maintaining a controlled total quantity of error in the ion abundances. The ARE function for creating the input variable errors generates 11normally distributed random numbers and then, the errors are calculated by using the expression 11
Ei = Ri 11ARE/ C lRil i=l
(4)
where Ri is the ith random number, Ei is the ith input variable error, and ARE is the average random error. The ARE is equal to 0.7979 times the standard deviation (a) in a normal distribution. Specifying the ARE is equivalent to specifying the standard deviation of the error. The ARE may be calculated for mass spectral data by summing the absolute differences between the measured and theoretical ion abundances and then dividing by the total number of ion abundances used. In Table I, the ARES for xenon and DCB abundances are reported. It is important to remember relative abundance was used in this analysis. The perchlorination recovery error model is a two-step model accounting for average recovery and then recovery precision. The average recovery for each isomeride must be
ANALYTICAL CHEMISTRY, VOL. 53, NO. 3, MARCH 1981
experimentally determined for the reagents and conditions employed in the perchlorination reaction. The average recovery varies between different perchlorination techniques and knowledge of the correct recovery accuracy errors is imperative. The differences between the average recovery and the actual recovery for biphenyl, Aroclor mixture 1221, Aroclor mixture 1242, and Arolcor mixture 1254 were examined (17)to model the perchlorination precision errors. The standard deviations were very similar and, thus, the precision errors were independent of the number of chlorines on the biphenyl structure. Consequently, precision errors were assumed to be independent of isomeride chlorine content in the sensitivity analysis. The precision errors for biphenyl and Aroclor PCB mixtures were found to be normally distributed. Thus, the precision errors in the sensitivity analysis were assumed to be normally distributed. The perchlorination recovery error model is step 1 IRAi = IIiRAJ 100 step 2
IIEi = IRAi + IIiEi/lOO
where IRAi is the input percentage of isomeride i after correction for average recovery, IIi is the input percentage of isomeride i, RAi is the average recovery accuracy for isomeride i (in percent), IIEi is the input percentage of isomeride i after correction for both average recovery and recovery precision, and Ei is the error calculated as described previously. In this analysis, “typical” perchlorination recoveries were not used due to the uncertainty of the “true” recoveries. Thus, an average recovery of 95% was arbitrarily chosen for all isomerides for all analyses. The error in the sensitivities caused by this choice will be small. The error models devised enable one to create mass spectra with realistic errors. These errors should simulate the laboratory perchlorination and measurement of a sample. Each (created) pseudo-real-mass spectrum will have a slightly different sensitivity since the individual errors are randomly created. Thus, to determine the sensitivity for a desired ARE, the sensitivities of many data sets (mass spectra) with the same ARE must be averaged. This sensitivity analysis will indicate the expected average output error for a known average input error. Sensitivity in this analysis is expressed as K
i=I
where SenAREis the sensitivity for the specified average random error, K is the total number of data sets, and SDSi is the sensitivity for the ith data set. The data sensitivities are calculated from an average isomeride or Aroclor mixture composition. These average compositions are computed from N pseudo-real-mass spectra. When N equals 2, the sensitivity calculated represents the sensitivity expected for a sample analyzed in duplicate. Other replicate sensitivities can be found similarly. In equation form, the isomeride data set sensitivity is 11
SDSi =
N
1 I ( I P m - j=l EIEmj/MI m=l
(8)
where IP, is the unerrored value for the mth isomeride, N is the number of pseudo-real-mass spectra in the data set, and IE,j is the calculated errored value for the mth isomeride for the j t h pseudo-real-mass spectra. In equation form, the Aroclor data set sensitivity is L
SDSi = C \(AI’, m=l
N
- CAEmj/MJ j=l
(9)
where AP, is the unerrored value for the mth Aroclor mixture,
525
N is the number of pseudo-real-mass spectra in each data set, AE, is the calculated errored value for the mth Aroclor mixture for the jth pseudo-real-mass spectra, and L is the total number of Aroclor mixtures possible (11 in this analysis).
RESULTS AND DISCUSSION Calculationswith the Model. The model was successfully implemented by using Gauss elimination with partial pivoting, double precision real numbers with 18 significant digits, and relative abundance data rounded to the nearest hundredth. This rounding simulates real measuring accuracy. Calculations performed with the model using rounded relative abundance data should result in answers almost identical with the original distribution from which the abundances were calculated. However, they do not. It was found that increasing the chlorine-37 or chlorine-36 percentages in the perchlorinating reagent decreased the error in the calculations. Also, chlorine-37 compositions were found to introduce less error into the computations than the chlorine-36 compositions. These relationships are caused by illconditioning in the mathematics of the model (18),and will be true in all calculations with the model. Chlorine isotopic ratios greater than 17.5/82.5 (35C1/37C1)and all chlorine-36 compositions are severely ill-conditioned and were found to have large computational errors. Consequently, these compositions are unsuitable for this analysis technique. Isomeride Composition Calculation Sensitivity Analysis. The sensitivity analysis for the isomeride calculation was performed by using Aroclor mixture 1254 and an all isomeride equal PCB mixture. An examination of Aroclor mixture 1221, Aroclor mixture 1242, and a 75:25 mixture of Aroclor mixtures 1242 and 1254 revealed lower sensitivities than the above PCB mixture. This analysis was performed by using 36 different perchlorination precision-mass spectral measurement error combinations at four different chlorine compositions for each mixture. Each sensitivity represents an average of 50 data set sensitivities. Data set sensitivities containing one and three pseudo-real-mass spectra were calculated. From the sensitivities obtained, 10% sensitivity contour plots were drawn (Figures 1 and 2) for both mixtures. If a perchlorination error-measurement error combination lies to the left of a sensitivity contour, the expected sensitivity (totalerror) would be less than 10%. Conversely, if it lies to the right, the expected sensitivity (total error) would be greater than 10%. These contours are composed of average sensitivities, and thus, some variability could be expeded. The 95% confidence limits reported for these contours provide a good indication of this variability. The variances which permitted calculation of the confidence limits were obtained by plotting the variance against the sensitivity for all error combinations for the 99.9 and 90.0% chlorine-37 compositions. The variance-sensitivity relationship was linear for the individual and triplicate analyses. By use of least-squares regressions, the variance (the predicted Yi) was obtained for the desired sensitivity. The most striking feature of these contours is the dependence of the sensitivity on the mass spectral measurement error. Consequently, these contour plots will be examined primarily in terms of the mass spectral measurement error. Comparing reported perchlorination precision errors with the perchlorination errors used in this analysis is difficult. Recoveries have been reported predominantly as the mean and percent average deviation (6, 7, 19-21). The sensitivity analysis was performed by using perchlorination errors defined in terms of the standard deviation. Comparison of the two different methods for reporting the perchlorination error for the “worst case” reveals these two methods are roughly equivalent. For the purposes of this discussion, it will be
526
ANALYTICAL CHEMISTRY, VOL. 53, NO. 3, MARCH 1981
Table 11. Minimum 37ClCompositions Permitting a Successful Analysis 37c1,a % ~~
mass spectral measurement ARE
isomeride analysis all isomerides Aroclor 1254 equal mixture 1 0 % sensitivity 10%sensitivity for successful anal. for successful anal. 1
3
1
3
analysis
analyses
analysis
90 97 NP NP NP
90 90 95 95 99
95 NP NP NP NP
0.17b 0.38 0.65d 0.72d 0.80e a
100%- 37C1%= %1%.
Reference 15.
e
10.0W LT
a
z a
M
a z
Aroclor analysis 33:33:34 blend of Aroclors 1 2 4 2 , 1 2 4 8 , 1 2 5 4 10% sensitivity 20% sensitivity
for successful anal.
analyses
3
1
3
analysis
analyses
analysis
analyses
90 95 NP NP NP
99 NP NP NP NP
97 NP NP NP NP
94 NP NP NP NP
90 97 NP NP NP
Reference 16.
1 I
3
3
3
for successful anal,
1
NP = not possible.
c
~
This investigation.
e
7
€
3
c 1.5-
-
5.0-
M
LT
a J
8 U LT W
2.5-
n.
.o-
c
L 1 .oo u .25
.O
.2
.4
.6
-8
MEASUREMENT ARE
Figure 1. Sensitivity contours (10%)for the isomeride composition calculation as a function of perchlorination and mass spectral measurement ARE for single (1) and triplicate (3) analyses of the all isomerides equal PCB mixture. The 95% confidence limits are 10 f 1.2% for single and 10 f 1.2% for triplicate analyses. Chlorine (-) 0.1:99.9, (---) 1.0:99.0, compositions are as follows: (%I:~~CI): (- -) 5.0:95.0, (- -) 10.0:90.0.
assumed that these two different precision measurements are the same. Examination of the literature (6, 7,19-22) reveals a range of 2.4-5.6 for perchlorination ARES. Combining this range with the known mass spectral measurement errors (Table I) enables a range of chlorine-37 compositions to be formulated from Figures 1 and 2 for a successful analysis (Table 11). A successful analysis is defined to be a total error equal to or less than 10%. In Table 111, suggested working conditions obtained from the contour plots are reported for individual and triplicate analyses. If a particular mass spectrometer can equal the suggested ARE values, a wide range of chlorine compositions and perchlorination errors would provide a sensitivity of 10% or less. From the data in Tables I1 and 111,three important conclusions about the isomeride composition determination can be made. (1)Performance of the technique with a 10% sensitivity or less is possible. However, analyses must be performed in triplicate, in most cases, for a successful analysis. (2) Extremely pure chlorine-37 reagents are most desirable. (3) Extremely small mass spectral measurement errors are most desirable. Aroclor Composition Calculation Sensitivity Analysis. The sensitivity analysis and the analysis of the data for the Aroclor composition calculation were performed by using the methods described for the isomeride analysis.
.50
I
J
.75 1.00
WEASUREMENT RRE
Sensitivity contours (10%) for the isomeride composition calculation as a function of perchlorination and mass spectral measurement ARE for single (1) and triplicate (3) analyses of the Aroclor 1254 PCB mixture. The 95% confMence limits are 10 f 1.4% for single and 10 1.4% for triplicate analyses. See Figure 1 for legend. Flgure 2.
*
Table 111. Suggested Working Conditions for a Successful Analysis
PCB mixture
sensitivity for a successful analysis, % no. of analyses mass spectral measurement ARE min 37Cl,% a max perchlorination ARE a
100
isomeride analysis
Aroclor analysis
all mixtures
all blends
10
10
10
10
20
20
1
3
1
3
1
3
0.25
0.50
0.10
0.10
0.15 0.25
91
97
95
95
92
5.0
5.0
2.0
3.0
3.0
93 5.0
3 7 ~ 1 %= 3 5 ~ 1 % .
The sensitivity analysis, when initially performed with Aroclor mixture 1016 included in the regression model, failed to provide reasonable sensitivities for any of the blends of Aroclor mixtures containing Aroclor mixtures 1016 and/or 1242. Aroclor mixtures 1016 and 1242 have almost identical isomeride compositions. Consequently, the model was unable to consistently choose the correct Aroclor mixture. Thus, Aroclor mixture 1016 was eliminated rather than Aroclor
ANALYTICAL CHEMISTRY, VOL. 53, NO. 3, MARCH 1981 I
527
-
I
i
-
3
W LL
a z
Q
I-
a
z
LL Q
1 X
u K
W
e I 3 I
MEASUREMENT ARE
Flgure 3. Sensitivity contours ( 1 0 % ) for the Aroclor composition
calculation as a function of perchlorination and mass spectral measurement ARE for single (1) and triplicate (3) analyses of a blend of Aroclor mixtures 1242, 1248, and 1254 (33:33:34). The 9 5 % confidence limits are 10 f 2 . 0 % for single and 10 f 1.0% for triplicate analyses. See Figure 1 for legend. mixture 1242, since the total amount manufactured for Aroclor mixture 1016 is small in comparison to Aroclor mixture 1242 (3)* The complete analysis was performed on a blend of Aroclor mixtures 1242, 1248, and 1254 (33:33:34). This blend, usually, is the most complex blend of Aroclor mixtures found in the environment. A cursory analysis of three other environmentally important blends, Aroclor mixtures 1242 and 1248 (50:50), 1242 and 1254 (50:50), and 1248 and 1254 (50:50), revealed lower sensitivities than the blend of Aroclor mixtures 1242, 1248, and 1254. The sensitivity contour plots for individual and triplicate analyses of a sample show similar shape and form a t sensitivities of 10 and 20% (Figures 3 and 4). These contours are similar to the isomeride sensitivity contours in that the sensitivities are primarily dependent on the mass spectral measurement error. Thus, these contours will also be interpreted primarily in terms of the mass spectral measurement error. From the sensitivity contour plots, suggested working conditions and the minimum chlorine compositions which maintain a 10% or less and 20% or less sensitivity were drawn up for individual and triplicate analyses (Tables I1 and 111). The suggested working conditions and minimum chlorine compositions have the same interpretation as presented for the isomeride analysis. Comparison of the Aroclor and isomeride data in Table I1 reveals the Aroclor composition calculation is far more sensitive. A realistic value for the sensitivity of the Aroclor composition calculation is difficult to discern from the literature. All currently available techniques for determining the blend of Aroclor mixtures in a sample employ some scheme of matching the gas chromatographic pattern of the unknown to patterns of known blends of Aroclor mixtures. With typical packed GC columns, sensitivities of 3 0 4 0 % or higher would be expected. With high-resolution WCOT or SCOT capillary columns, sensitivities of 10-20% are possible (23). The separation obtained on the GC column directly affects the overall sensitivity of these techniques. Capillary column GC analysis is common in environmental laboratories. Thus, a sensitivity in the range of 0-20% will be considered necessary for a successful analysis. By use of this criteria, only the Caprioli et al. (15) mass spectral measurement errors permit a successful analysis (Table 11). Furthermore, since extremely small mass spectral measurement errors are required, small perchlorination errors are also a requirement. Consequently, the
.O . 1
3 3
I13
.2 . 3
.4
.5
.6
nEA5UREMENT ARE
Flgure 4. Sensitivity contours ( 2 0 % ) for the Aroclor composition calculation as a function of perchiorination and mass spectral measurement ARE for single (1) and triplicate (3) analyses of a blend of Aroclor mixtures 1242, 1248, and 1254 (33:33:34). The 95% confidence limits are 20 f 4 . 0 % for single and 20 f 3 . 4 % for triplicate analyses. See Figure 1 for legend.
suggested working mass spectral measurement errors for individual and triplicate analyses are small for both 10 and 20% sensitivities. The conclusions stated for the isomeride analysis are also valid in this analysis. However, even more stringent mass spectral measurement errors are required for a successful analysis.
CONCLUSIONS Performance of the labeled PCB perchlorination technique is possible with total errors (sensitivities) equal to or better than other currently available techniques. However, optimum conditions are needed for this technique to be successful. For routine analyses of environmental PCB samples, conditions are frequently not optimal. Due to the requirement of optimality, this technique is probably not useful for routine analyses. Optirnality depends on both the mass spectrometer and the perchlorination reaction errors. With newer, state of the art GC-MS systems and better optimization of systems similar to Caprioli et al. (15),mass spectral measurement errors a t or below the suggested working conditions may be possible. However, the perchlorination reaction (6) for perchlorinating PCBs quantitatively gives erratic recoveries of decachlorobiphenyl. The consistency reported in the literature is low and may not reflect the “true” reliability of this technique. Variations in the precision and accuracy of the perchlorination reaction may strongly bias the results of the analysis. If a low recovery was obtained, it would be difficult to know whether all isomerides were recovered in proportions similar to that for a perchlorination reaction with normal recovery. Consequently, this reaction is the “weak link” in the labeled PCB perchlorination technique. An improved perchlorination technique is needed for this technique to be truly successful. The predictions for the labeled PCB perchlorination technique were made by examining the most sensitive PCB mixtures. Other PCB mixtures will be less sensitive. Thus, the suggested working conditions will ensure that the total absolute error for these mixtures will be equal to or less than the cases examined. All predictions in this investigation are based upon average total absolute errors for specified mass spectral measurement-perchlorination error combinations. These errors provide the error (sensitivity) expected on the average and
528
Anal. Chem. 1981, 53, 528-532
not the error in a specified analysis. These averages are computed from a limited number of sample cases, and thus, these sensitivities may contain some error. The reported 95% confidence limits provide an indication of the accuracy of these averages. The calculated sensitivities are only as good as the error models in the sensitivity analysis computer program. In general, the error models employed are conservative and should overestimate the total error. Nevertheless, the predictions based on this computer analysis must be evaluated with the error models in proper context.
ACKNOWLEDGMENT The authors thank M. Stolzenburg and D. Weininger for their assistance in developing the computer programs. We also acknowledge the suggestions by G. Glass and A. W. Andren to explore this technique.
LITERATURE CITED Lee, M. C.; Chian. E. S. K.; Griffin, R. A. Water Res. 1979, 13, 1249-1258. Zeii, M.; Nev, H. J.; Baiischmiter, K. 2. Anal. Chem. 1978, 292, 97-107. Hutzinger, 0.;Safe, S.; Zitko, V. "The Chemistry of PCBs"; CRC Press: Cleveland, OH, 1974; Chapter 2. Webb, R. G.; McCaii, A. C. J . Chromatogr. Scl. 1973, 1 1 , 366-373. de Lappe, B. W.; Risebrough, R. W.; Springer, A. M.; Schmidt, T. T.; Shropshire, J. C.; Letterman, E. F.; Payne, J. R. "Hydrocarbons and Halogenated Hydrocarbons in the Aquatic Environment"; Afghan, E. K., Mackay, D., Eds.; Plenum Press: New York, 1980; pp 19-68. Armour, J. A. J . Assoc. Off. Anal. Chem. 1973, 56, 987-993. Trotter, W. J.; Young, S. J. V. J . Assoc. Off. Anal. Chem. 1976, 58, 466-468. Grant, J. "Hack's Chemical Dictionary", 4th ed.; McGraw-Hili: New York, 1969; p 361. Genty, C. Anal. Chem. 1973, 45, 505-511.
(10) Margrave, J. L.; Pohnsky, R. E. J . Chem. Educ. 1982, 39, 335-337. (1 1) Mieure, J. P.; Hicks, 0.; Kaley, R. G.; Saeger, V. W. In National COW ference on Polychlorinated Biphenyls (Nov 19-21, 1975, Chicago, IL), EPA-560/6-75-004; Environmental Protection Agency, Office of Toxic Substances: Washington, DC, 1976; pp 84-93. (12) Hiimer. R. M.; Taylor, J. W. Anal. Chem. 1974, 46, 1038-1044. (13) Snedecor, G. W.; Cochran, W. G. "Statistical Methods", 6th ed.; The Iowa State University Press: Ames, IA, 1967; Chapters 6 and 13. (14) Painter, R. J.; Yantis, R. P. "Elementary Matrix Algebra wlth Linear Programming"; Prlndle, Weber, and Schmidt: Boston, MA, 1971; Chapter 7. (15) Caprioii, R. M.; Fies, W. F.; Story, M. S. Anal. Chem. 1974, 46, 453A-462A. (16) Kiiburn, K. D.; Lewis, P. H.; Underwood, J. G.; Evans, S.; Holmes, J.; Dean, M. Anal. Chem. 1979, 51. 1420-1425. (17) Burkhard, L. P.; Armstrong, D. E., unpublished work, University of Wisconsin, Madison, 1979. (18) Noble, E. "Applied Linear Algebra", Prentice Hall: Englewood Cliff, NJ, 1989; Chapter 8. (19) Berg, 0.W.; Diosad~,P. L.: Rees, 0. A. V. Bull. Environ. Contam. Todcol. 1972, 7 , 338-347. (20) Crist, H. L.; Moseman, R. F. J . Assoc. Off. Anal. Chem. 1877, 60, 1277-1281 .-. . .-- .. (21) Huckins, J. N.; Swanson, J. E.; Stalling, D. L. J . Assoc. Off. Anal. Chem. 1974, 57, 416-419. (22) Stratton, C. L.; Allan, J. M.; Whitlock, S. A. Bull. Envlron. Contam. Toxlcol. 1979, 21, 230-237. (23) Weininger, D.; Burkhard, L. P.; Armstrong, D. E., unpublished work, University of Wisconsin, Madison, Wisconsin and Environmental Research Laboratory, US-EPA, Duiuth, MN, 1979.
RECEIVED for review July 14, 1980. Accepted December 15, 1980. This investigation was supported in part by the Office of Water Research and Technology Grant A-067-WIS and by the National Oceanic and Atmospheric Administration Office of Sea Grant. The support of the Department of Civil and Environmental Engineering and the Engineering Experiment Station, University of Wisconsin, Madison, is also acknowledged.
Spatial Mapping of Concentrations in Pulsed and Continuous Atom Sources Larry
E. Steenhoek
and Edward S. Yeung"
Ames Laboratory and Department of Chemistry, Iowa State University, Ames, Iowa 5001 1
The high collimation of a laser beam is used to provide spatial information when it is imaged on a silicon vidicon, the high monochromaticity of the laser light is used to selectively monitor atomic and molecular species, and a Bragg cell is used to provide temporal resolution. These principles combined led to a versatile diagnostic scheme for studying pulsed and continuous sources for atomic and molecular spectrometry.
The ability to measure spatially resolved concentrations of molecular and atomic species in real time is of great importance to understanding the dynamics of many physical systems. In atomic spectroscopy, the distribution of species and the local temperatures can be used to optimize the atomization process (1-4) as well as to understand chemical and spectral interference effects so that they can be eliminated. In mass spectroscopy, concentration profiles can be used to characterize pulsed sources such as the laser microprobe (5). In combustion research, the identification and the quantitation of reaction intermediates such as radicals are of substantial interest (6). Of equal importance is a knowledge of the spa0003-2700/81/0353-0528$01 .OO/O
tially resolved temperature profiles in these systems, so that thermodynamic properties can be used to interpret the results. Because of the typically nonintrusive nature of optical methods, they are most often employed to make these measurements. To this end, techniques such as atomic and molecular emission (7,8),absorption (9),fluorescence (IO),and scattering (11) have been applied to study a wide range of systems and problems. To date, nearly all spatial information is obtained one point at a time by some sort of rastering mechanism. Aside from the tedious nature of this approach, there are problems with reproducibility since the system must maintain its integrity over the entire duration of the measurements. Furthermore, dynamic systems are in general not reproducible from one trial to the next, so that the "average" system behavior may not be meaningful a t all. These measurements become even more difficult when temporal information is desired. The obvious solution to these problems is the use of a multichannel imaging detector. Although photographic detection provides such capabilities, the narrow dynamic range, low sensitivity, difficult calibration, and tedious densitometry in two dimensions severely limit its practicality. TV-type digital image processors have received much attention recently 0 1981 American Chemical Society
