Anal. Chem. 2006, 78, 5412-5418
Air-Water Henry’s Law Constants for PCB Congeners: Experimental Determination and Modeling of Structure-Property Relationship Fu Fang,† Shaogang Chu,† and Chia-Swee Hong*,†,‡
Wadsworth Center, New York State Department of Health, Albany, New York 12201-0509, Department of Environmental Health Sciences, School of Public Health, State University of New York at Albany, Albany, New York 12201-0509
A modified gas-purging technique was used for the determination of Henry’s law constants (HLCs) for four nonortho- and eight mono-ortho-substituted polychlorinated biphenyls (PCBs). The method involves measurement of a compound’s concentration in only the water phase while that compound is being stripped isothermally from the solution at a known gas flow rate. HLCs were calculated from the slope of a plot of ln(Cn) versus (∑1/V)n, where (Σ1/V)n ) 1/V0 + 1/V1 + ... + 1/Vn-1. The HLCs ranged from 5.6 to 21.8 Pa m3/mol, with an average precision of 13%, and they are comparable to values in the literature. Meta-analysis technique and principal component regression (PCR) were applied to model the relationship between experimentally determined HLC values of 94 PCB congeners and the congeners’ structures. Crossvalidation yields an optimal model with two principal components. Statistical analysis suggests that HLCs of PCBs are primarily affected by meta-chlorine substitution, a relationship which has never been discussed in the literature. The substitution of chlorines on the biphenyl rings generally leads to smaller HLCs. The predicted HLCs are in good agreement with the experimentally determined values. Studies of structure-activity relationships of polychlorinated biphenyls (PCBs) have elucidated the profound effect of the chlorine substitution patterns, both on the toxic response and on induction of drug-metabolizing enzymes in mammalian and avian species.1,2 PCB congeners having zero or one chlorine in the ortho position of the phenyl rings are particularly toxic and are potent inducers of hepatic aryl hydrocarbon hydroxylase (AHH).3 PCB congeners with no ortho-, two para-, and two or more metachlorines are approximate isostereomers of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), and they have the ability to induce mixed-function oxidase activity that qualitatively resembles the effects observed in organisms exposed to TCDD.4 There are * To whom correspondence should be addressed. Phone: 518-473-7299. Fax: 518-473-2895. E-mail:
[email protected]. † School of Public Health. ‡ Wadsworth Center. (1) Bandiera, S.; Safe, S.; Okey, A. B. Chem. Biol. Interact. 1982, 39, 259-277. (2) Clarke, J. U. Chemosphere 1986, 15, 275-287. (3) Safe, S. Chemosphere 1987, 16, 791-802. (4) Poland, A.; Glover, E. Mol. Pharmacol. 1973, 9, 736-747.
5412 Analytical Chemistry, Vol. 78, No. 15, August 1, 2006
increasing numbers of reports indicating the widespread presence of non-ortho and mono-ortho PCB congeners. Therefore, evaluation of the environmental behaviors of these toxic PCBs in terms of their physicochemical characteristics is very important. PCB concentrations in water, sediment, and biota can be significantly influenced by local emissions or diffuse urban sources; however, atmospheric inputs can also be significant. Atmospheric transport has been considered a major global redistribution process for PCBs.5 The Henry’s law constant (HLC) represents the air-water equilibrium partition coefficient for a particular chemical compound present in a dilute aqueous solution; therefore, it expresses a key physical property of a compound with respect to predicting that compound’s behavior, transport, and fate in the environment. In addition, HLCs are requisite in the applicability of potential treatment methods such as airstripping for treatment of contaminated groundwater. Accurate HLCs and other physical and chemical parameter data must be obtained before fate-prediction models can be used with confidence. Unfortunately, accurate HLCs for most non-ortho and mono-ortho PCBs are still lacking. In general, there are two distinct approaches for the determination of HLCs. One is a static method, including thermodynamic,6,7 headspace,8,9 and phase-ratio variation techniques.10,11 It involves determination of the air and water concentrations of a substance in a closed system under equilibrium conditions. Therefore, the static equilibration technique is suitable for solutes with relatively high aqueous solubilities and vapor pressures. The other approach is a kinetic method, including gas-stripping,12,13 wetted wall column,14,15 and fog chamber14 techniques. It involves (5) Atlas, E.; Foster, R.; Glam, C. S. Environ. Sci. Technol. 1982, 16, 283-286. (6) Murphy, T. J.; Mullin, M. D.; Meyer, J. A. Environ. Sci. Technol. 1987, 21, 155-162. (7) Fischer, A.; Muller, M.; Klasmeier, J. Chemosphere 2004, 54, 689-694. (8) Ettre, L. S.; Welter, C.; Kolb, B. Chromatographia 1993, 35, 73-84. (9) Hadjoudj, R.; Monnier, H.; Roizard, C.; Lapicque, F. Ind. Eng. Chem. Res. 2004, 43, 2238-2246. (10) Gossett, J. M. Environ. Sci. Technol. 1987, 21, 202-208. (11) Chiang, P. C.; Hung, C. H.; Mar, J. C.; Chang, E. E. Water Sci. Technol. 1998, 38, 287-294. (12) Hassett, J. P.; Milicic, E. Environ. Sci. Technol. 1985, 19, 638-643. (13) Sahsuvar, L.; Helm, P. A.; Jantunen, L. M.; Bidleman, T. F. Atmos. Environ. 2003, 37, 983-992. (14) Fendinger, N. J.; Glotfelty, D. E.; Freeman, H. P. Environ. Sci. Technol. 1989, 23, 1528-1531. (15) Altschuh, J.; Bruggemann, R.; Santl, H.; Eichinger, G.; Piringer, O. G. Chemosphere 1999, 39, 1871-1887. 10.1021/ac0604742 CCC: $33.50
© 2006 American Chemical Society Published on Web 07/07/2006
determination of the rate of loss of a substance from water while that substance is being stripped with a continuous flow of inert gas from an aqueous solution. Because the kinetic method allows a significantly greater volume of gas to be placed in contact with the water than does the static method, and because it is based on a relative concentration change in one phase, it can be readily applied to compounds with HLCs as low as ∼1 × 10-4 (dimensionless).16 Thus, it is particularly suitable for PCBs, which have very low aqueous solubilities and low vapor pressures. There are three approaches to prediction of HLCs of PCBs. Most models in the literature actually belong to the category of quantitative property-property relationship (QPPR) models, even though they are often referred to as quantitative structureproperty relationship (QSPR) models.17-20 Very few models truly fall into the QSPR category.21,22 The third approach essentially entails a method of compiling the available data: a recommended value is produced after the original input data have been adjusted according to certain arbitrary criteria.23 Unlike QPPR models, QSPR models do not require any information other than the molecular structure of the chemicals of interest; they provide more intuitive means of understanding the impact of molecular structure on physical-chemical properties. Nevertheless, most QSPR models established thus far have used very complicated methods to describe the structure of PCBs, and these models lack sensitivity to the chlorine substitution pattern. For the third method, the major weakness is the possibility that an error or a systematic bias is introduced during adjustment. Nevertheless, this method’s strength lies in the fact that data obtained from multiple sources are used. The objectives of this investigation are (a) to devise a simple method to measure HLCs for four highly toxic non-ortho- and eight moderately toxic mono-ortho-substituted PCBs, (b) to compare our results with data determined in other studies, and (c) to develop a simple QSPR model to predict HLCs of PCBs. The experimental apparatus consists of a known volume of water through which a fixed volume of air is sparged, under conditions such that the solute concentration in the exit gas is essentially in equilibrium with the aqueous concentration. From a plot of ln(Cn) versus (∑1/V)n, HLC can be deduced. Subsequently, data from this study and from the literature were compiled to investigate the relationship between the chlorine substitution patterns of PCB congeners and these congeners’ HLCs. This relationship could be used to predict HLCs for the remainder of the 209 PCB congeners. EXPERIMENTAL SECTION Reagents and Materials. All PCB congeners used in this study were obtained from AccuStandard Inc. (New Haven, CT) (16) Hovorka, S.; Dohnal, V. J. Chem. Eng. Data 1997, 42, 924-933. (17) Burkhard, l. P.; Armstrong, D. E.; Andren, A. W. Environ. Sci. Technol. 1985, 19, 590-596. (18) Abraham, M. H.; Al-Hussaini, A. J. M. J. Environ. Monit. 2005, 7, 295301. (19) Dunnivant, F. M.; Elzerman, A. W.; Jurs, P. C.; Hasan, M. N. Environ. Sci. Technol. 1992, 26, 1567-1573. (20) Sabljic, A.; Gusten, H. Chemosphere 1989, 19, 1503-1511. (21) Brunner, S.; Hornung, E.; Santl, H.; Wolff, E.; Piringer, O. G.; Altschuh, J.; Bruggemann, R. Environ. Sci. Technol. 1990, 24, 1751-1754. (22) Wang, X. D.; Tang, S. L.; Liu, S. S.; Cui, S. H.; Wang, L. S. Chemosphere 2003, 51, 617-632. (23) Li, N. Q.; Wania, F.; Lei, Y. D.; Daly, G. L. J. Phys. Chem. Ref. Data 2003, 32, 1545-1590.
as 99+% pure. The PCBs were used without further purification. Hexane and acetone were nanograde from Mallinckrodt (St. Louis, MO). Sodium sulfate was purchased from Fisher Scientific (Pittsburgh, PA). It was washed with hexane in a Soxhlet system for 6 h and was then dried at 80 °C under vacuum. The water was prepared with a Milli-Q Plus water-purification system (Millipore Corporation, Milford, MA). Glass beads were washed in double-distilled water, acetone, and hexane, followed by heating for 4 h in a 120 °C oven. Safety. The dioxin-like PCBs should be treated as a potential health hazard. Exposure to these compounds should be reduced to the lowest possible level. These compounds were handled using essentially the same techniques employed in handling PCDD/Fs recommended by EPA Method 1613.24 Preparation of PCB Aqueous Solution. Aqueous solutions of PCBs were prepared by a generator-column technique25 so as to avoid the presence of microcrystals. Ten grams of glass beads (0.2-mm diameter) were added to 200 mL of an acetone solution containing 10 mg of the PCB congeners of interest. The solvent was slowly evaporated in a rotary evaporator so that the congener remained on the support particles in the form of a thin layer. The generator column was prepared by packing an 8 cm × 8 mm i.d. chromatographic column (Michel-Miller low-pressure liquid chromatographic column; ACE Glass, Inc., Vineland, NJ) with the PCBcoated glass beads. The packing was held in place by 7.5-mmdiameter Teflon filters at both ends. The saturated PCB solution was generated by pumping water through the generator column at a flow rate of 0.5 mL/min, using a Waters HPLC pump. The first 500 mL of the eluate was discarded because stable equilibrium was not yet established.25 The later eluate was collected in an amber glass bottle and used within a week for HLC determination. Purge Experiments. The gas-purging system used for the experiments was a modification of the one used by Mackay et al.26 and Brunner et al.21 A water-jacketed column, 30 cm long with a 1.1-cm i.d. (ACE Glass Inc.) was used as the purging vessel. High-purity nitrogen was used to strip the PCB congener from its aqueous solution. Gas flow was controlled by an electrical flow meter (Cole-Parmer Instrument, Vernon Hills, IL) and was regularly checked. The gas was introduced into the bottom of the column through a fritted Teflon filter, which produced many small bubbles. The gas exited the upper end of the column through a three-way stopcock and was discharged into cooled hexane (beneath the surface) through Teflon tubing (0.125-in. o.d., 0.063-in. i.d.). A reflux condenser was attached to the top of the hexane trap to minimize the loss of hexane and the eluate. The aqueous samples (∼1 mL each) were taken by a 2-mL syringe through Teflon tubing (0.0625-in. o.d., 0.031-in. i.d.) connected to a three-way stopcock at the upper end of the column. Precautions were taken to minimize the quantity of gas bubbles present in the Teflon tubing and, thus, to avoid sampling a mixture of solution and gas. An aqueous PCB solution (28 mL) was transferred into the purge column and was thermostated at 25 ( 0.1 °C for 12 h to (24) USEPA, Method 1613. “Tetra- through Octa-Chlorinated Dioxins and Furans by Isotope Dilution HRGC/HRMS”, EPA 821B94005a, Office of Water Engineering and Analysis Division; U.S. Government Printing Office: Washington, D. C., October 1994. (25) Hong, C. S.; Qiao, H. C. Chemosphere 1995, 31, 4549-4557. (26) Mackay, D.; Shiu, W. Y.; Sutherland, R. P. Environ. Sci. Technol. 1979, 13, 333-337.
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accomplish the adsorption equilibrium between the solution and the glass wall. After a water sample was taken for determination of the initial concentration of the particular PCB congener, gas purge started immediately. Under the optimal conditions (a flow rate of 50 mL/min and a sampling interval of 15 min), five water samples were taken sequentially, and the precise volume of each sample was determined gravimetrically. Data collected from one purge column constituted an individual HLC measurement, and an average of three to six such HLC measurements was calculated for each of the 12 PCBs. Recovery of a PCB congener in the gaspurge experiment was calculated from the mass balance between the initial aqueous concentration and the total amount of congener collected in the cooled hexane plus the remainder in the water phase after the experiment. This recovery value was used to estimate the uncontrolled losses of PCB (i.e., by adsorption) and to validate the measured HLC, as well. In this study, recoveries of gas purge were generally >90%. HLC determinations with gaspurge recoveries of 125% were discarded. Analytical Technique. Surrogate standard (1 mL of 1 ng/ mL in isooctane) containing PCB 61 or PCB 204 was added (PCB 61 was used for PCB 77 and PCB 81, and PCB 204 for the remaining PCBs) to each aqueous sample prior to hexane extraction so as to monitor the analytical recovery. The sample was stirred by vortex mixer and was then centrifuged. The organic phase was separated from the aqueous phase, and the aqueous phase was extracted twice more using 1 mL of hexane each time. The combined hexane/isooctane extract, after drying over the sodium-sulfate column, was concentrated to 50 µL under a gentle stream of nitrogen. For analysis of gaseous PCBs in the reservoir of cooled hexane, surrogate standard was added as described above, then evaporated to 2 mL using a Kuderna-Danish evaporator, and further concentrated to 50 µL with nitrogen evaporation. The concentrated PCB solution was then analyzed on a 30-m × 0.25-mm-i.d., 0.25-µm film thickness of DB-XLB fused-silica column (J & W Scientific Inc., Folsom, CA) by an HP 6890 gas chromatograph (GC) equipped with a split/splitless injector system and a 63Ni electron capture detector (ECD). The carrier gas was helium with constant pressure control, and the makeup gas was nitrogen. The injector and detector temperatures were 280 and 310 °C, respectively. The GC oven temperature was programmed as follows: initial temperature of 100 °C for 2 min, then increasing to 300 °C at 10 °C/min, and held for 8 min. An HP 7673 autosampler was used for splitless injection (1 µL injected), with the split valve closed for 1 min after injection. The data were managed with HP ChemStation software. Calculation of HLCs. In a dilute aqueous system, the HLC (H) is given as H ) P/C, where P is the solute partial pressure (Pa) and C is the aqueous-phase solute concentration (mol m-3). It is essential that both P and C refer to the same state (liquid or solid). In the stripping process, when the solute in the exit vapor is in equilibrium with the liquid, a mass balance for the solute gives the transfer rate as
-V dC/dt ) PG/RT ) HGC/RT
(eq 1)
where G is the gas flow rate (m3 min-1), V is the volume of the liquid (m3), R is the gas constant (8.31 m3 Pa mol-1 K-1), T is the system temperature (K), and t is the gas stripping time (min). 5414
Analytical Chemistry, Vol. 78, No. 15, August 1, 2006
This equation can be integrated from initial conditions (t ) 0 and C ) C0) to give
ln(C) ) ln(C0) - (HG/VRT)t
(eq 2)
A plot of ln(C) against t should be linear, with a slope of -HG/ VRT if the water volume is constant. In this study, the initial volume of aqueous solution (V0) for gas purge was 27 mL. Withdrawing a sample of 1 mL produces a reduction of 3.7% in volume, and a total of 5 mL withdrawn represents an 18.5% change of volume. Obviously, it is inappropriate to ignore this factor and assume volume to be constant, when integrating eq 1 from t0 to tn. Instead, a modified approach is used.13 Because the volume of aqueous solution is constant between two samplings, that is, during the time of t0-t1, t1-t2, ..., tn-1-tn, we have, according to eq 1,
ln(C1) ) ln(C0) - [HG(t1 - t0)]/(V0RT)
(eq 3)
ln(C2) ) ln(C1) - [HG(t2 - t1)]/(V1RT)
(eq 4)
...
ln(Cn) ) ln(Cn-1) - [HG(tn - tn-1)]/(Vn-1RT)
(eq 5)
Combination of eqs 3-5 gives
ln(Cn) )ln(C0) - [HG∆t/(RT)](Σ1/V)n
(eq 6)
where Cn is the concentration at the end of the nth time interval, ∆t ) (t1 - t0) ) (t2 - t1) ) ... ) (tn - tn-1) [) 15 min in this experiment], and (∑1/V)n) 1/V0 + 1/V1 + ... +1/Vn-2 + 1/Vn-1. A plot of ln(Cn) versus (∑1/V)n gives a straight line with a slope of -HG∆t/(RT), which can be used to determine H. Modeling of the Structure-Property Relationship. Metaanalysis is a technique employed to compile results of comparable independent studies in clinical trials. We adopted it here due to the limited amount of experimental HLC data available for PCBs. Experimental data are generally assumed to have Gaussian distributions. Thus, in each study, the true value (µ) of a parameter can be best estimated as the average of repeated measurements. When results from several independent studies are available, µ can be estimated as
µ ) (Σωixi)/(Σωi)
(eq 7)
where xi is the average and ωi is the precision in the ith study. Principal component regression (PCR) is a hybrid of principal component analysis (PCA) and multivariate linear regression (MLR). In general, the first step is to center the independent variable X0 and the dependent variable Y0, giving X and Y. Then, the score matrix (S) from PCA of X is used to establish a MLR model on Y. Finally, Y0 can be predicted by a simple linear expression of X, where the linear regression coefficients (Β) are
Figure 1. Measured HLCs (H) of PCB 77 at various purging flow rates. Figure 2. Typical plots of ln(Cn) versus (Σ 1/V)n. (O) PCB 77 (r 2 ) 0.9687); (3) PCB 114 (r2 ) 0.9774); (0) PCB 169 (r2 ) 0.9792).
derived from the MLR coefficients (Γ) of Y versus S and the loadings (L) from PCA; that is, B ) Γ × L. RESULTS AND DISCUSSION Evaluation of Experimental Method. Desorption is one potential limitation of any gas-purging device.12 If adsorption of PCBs from the amount initially added to the glass column has stabilized after 12 h of equilibrium, subsequent desorption of PCBs back into the solution may occur after depletion of PCBs in the water due to purging. This process will lead to a decrease in the values of measured HLCs. We used the depletion curve of the cumulative amount of PCBs purged against purge time to evaluate the presence or absence of glass desorption. A depletion curve obtained from the sampling period of 0-75 min did not reach plateau (in the “rise” portion of the plot, as opposed to the complete curve, including both rise and plateau); this indicates that within a purge interval of 15 min, adsorption/desorption from the glass purge column can be considered negligible and does not affect the HLC values measured. Obtaining the vapor/solution equilibrium is essential in successfully measuring HLCs; however, in a gas-purge system, this equilibrium may not be achieved under certain circumstances.27 In general, equilibrium is dependent on the contact time between the gas and liquid phases. In a gas-stripping design, residence time of the gas phase in the purge column is proportional to the column length and inversely proportional to the gas flow rate. Therefore, equilibrium can be achieved by controlling the gas flow rates when the column length is fixed. We employed PCB 77 as the test compound, since its relatively high aqueous solubility makes it less likely to reach equilibrium, as compared to other PCB congeners in this study. Purge runs were performed in triplicate, with five flow rates ranging from 50 to 110 mL/min. A Student’s t test showed that the HLC values of PCB 77 measured at the differing flow rates are constant (Figure 1), thus demonstrating that equilibrium was achieved in the flow-rate range that we tested. Thus, all further experiments for HLC determination were performed at a fixed flow rate of 50 mL/min. Comparison of Experimental HLCs to Literature Values. HLCs were calculated from the slope of a plot of ln(Cn) versus (27) Kutsuna, S.; Chen, L.; Ohno, K.; Tokuhashi, K.; Sekiya, A. Atmos. Environ. 2004, 38, 725-732.
(∑1/V)n (Figure 2 displaying a few example plots) according to eq 6, and the values are shown in Table 1. Each reported HLC value is the average of three to six measurements. PCB 105 has the smallest HLC (5.6 Pa m3/mol), and PCB 123 has the largest HLC (21.8 Pa m3/mol) among the 12 PCB congeners studied. Percent variation on the mean value of measured HLC ranges from 4.1 to 31.5%, with an average precision of 13%. PCBs 105, 157, and 189 have much larger RSDs than the nine other congeners, and RSDs seem to be uncorrelated with HLC values (R2 ) 0.11). The HLC for PCB 77 (11.0 Pa m3/mol) reported in this study agrees well with two values experimentally measured using the kinetic approach (9.5 Pa m3/mol from Dunnivant et al.19,28,29 and 16.2 Pa m3/mol from Bamford et al.30). Our HLCs for PCB 105, 118, and 126 are lower than those of Bamford et al.30 by at least a factor of 2. Nevertheless, for all 12 PCBs, results from our study agree with predicted values from various existing models.17,19,20,22,31 Modeling of the Structure-Property Relationship. HLCs of PCBs have been measured using both the static and the kinetic approaches.5,6,12,19,21,28-30,32-34 As mentioned before, PCBs have very low HLCs, and thus, kinetic methods are acknowledged to be much more reliable than the static methods. Among all of the data measured by the kinetic approach, four previous studies19,21,28-30,33 have reported HLCs of PCBs at 25 °C (Figure 3). The reported experimental values of HLCs are so scattered that it is quite difficult to select a “best” value for a specific congener. The data of Brunner et al.21 seem to be the only set to show a strong trend; that is, the HLC decreases as the total number of chlorines increases. Generally, HLCs of Brunner et al.21 seem to be lower than values reported by other studies on PCB congeners, especially for the highly chlorinated congeners. Goss et al.35 commented that the HLCs reported for the highly (28) Dunnivant, F. M.; Coates, J. T.; Elzerman, A. W. Environ. Sci. Technol. 1988, 22, 448-485. (29) Dunnivant, F. M.; Elzerman, A. W. Chemosphere 1988, 17, 525-541. (30) Bamford, H. A.; Poster, D. L.; Baker, J. E. J. Chem. Eng. Data 2000, 45, 1069-1074. (31) O ¨ berg, T. Internet J. Chem. 2001, 4, art. no. 11. (32) Coates, J. T.; Elzerman, A. W. J. Contam. Hydrol. 1986, 1, 191-210. (33) Fendinger, N. J.; Glotfelty, D. E. Environ. Toxicol. Chem. 1990, 9, 731735. (34) Oliver, B. G. Chemosphere 1985, 14, 1087-1106.
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Table 1. Mean HLC Values (Pa m3/mol) Measured by the Gas Purging Method: Comparison with Literature Values
d
PCB
IUPAC no.
n
HLC
RSD (%)
literature valuesa
3,3′,4,4′-tetra 3,4,4′,5-tetra 2,3,3′,4,4′-penta 2,3,4,4′,5-penta 2,3′,4,4′,5-penta 2,3′,4,4′,5′-penta 3,3′,4,4′,5-penta 2,3,3′,4,4′,5-hexa 2,3,3′,4,4′,5′-hexa 2,3′,4,4′,5,5′-hexa 3,3′,4,4′,5,5′-hexa 2,3,3′,4,4′,5,5′-hepta
77 81 105 114 118 123 126 156 157 167 169 189
6 3 4 4 5 4 3 3 6 5 6 4
11.0 11.3 5.6 18.8 17.4 21.8 10.0 14.8 16.6 12.8 12.3 11.9
6.1 9.0 31.5 10.2 12.8 6.3 4.1 6.8 24.9 10.4 8.6 25.1
9.5,b 16.2,c 4.6,d 10.3,e 4.4,f 10.4g 50.1,d 10.1,e 5.2,r 14.5,g 25.8,h 15.0i 33.6,c 3.2,d 5.9,e 5.7,f 10.0,g 6.1i 5.5,d 6.5,e 35.6,f 14.5,g 36.7,h 11.5i 36.2,c 12.6,d 6.1,e 9.3,f 12.8,g 11.8i 3.2,d 5.7,e 8.8,f 17.6,g 36.7,h 26.5i 21.1,c 10.7,d 5.3,e 2.7,f 8.3,g 5.5i 1.2,d 3.0,e 17.5,f 9.0,g 37.0,h 2.2i 7.4,d 2.9,e 3.3,f 8.6,g 31.6,h 6.7i 29.5,d 2.9,e 5.4,f 11.1,g 39.2,h 12.5i 25.1,d 2.5,e 1.6,f 6.6,g 23.4,h 6.0i 2.7,d 1.5,e 9.0,f 6.8,g 28.8h
a Literature values in boldface are measured values; the remaining ones are estimated values. b Dunnivant et al., ref 28. c Bamdford et al., ref 30. Wang et al., ref 22. e O ¨ berg, ref 31. f Burkhand et al., ref 17. g Dunnivant et al., ref 19. h Bamford et al., ref 38. i Sabljic and Gusten, ref 20.
substituted PCBs by Bamford et al.30 were too high, on the basis of the fact that they are inconsistent with the other physicalchemical properties (vapor pressure, etc.) for these congeners. Another argument by Goss et al.35 for doubting the validity of the HLC values by Bamford et al.30 was the unreasonable variability of the enthalpy of air-water exchange between congeners. Baker et al.36 have vigorously defended the HLCs of Bamford et al.30 on both experimental and theoretical grounds; clearly, the position is far from being resolved. Abraham and Al-Hussaini18 compared their predictions of HLCs with the values of Bamford et al.,30 and they found that the Bamford et al.30 values for the highly chlorinated PCBs were relatively high. However, they also claimed that the differences between their values and the values of Bamford et al.30 should be considered in the context of experimental errors. Therefore, we do not have strong evidence to label any of these data as “biased”. It is not an easy task to identify the outliers for PCBs’ HLC values from independent studies. In many cases, there is only one experimental datum available. Logically, it is inappropriate to assume an HLC value to be an outlier, merely because it is smaller or larger than the corresponding HLC values from different studies or because it does not follow a trend that we may perceive through direct observation. Even when two or more reported HLC values are available for a given congener, extreme caution should be exercised when an HLC value is being discredited. Issues such as whether a particular experimental device has a systematic bias and, if so, how this bias affects the measurement of each individual PCB congener further confound our judgments. Therefore, the Bayesian view on data (that is, that all data are good unless evidence tells us otherwise) is adopted in the task of compiling HLCs of PCBs from independent studies. In addition, identification of outliers by some sort of modeling on all of the valid data is a better approach, although different models may detect slightly different data points as possible outliers. Overall, when we include the data from this study (12 experimentally determined HLC values for 12 PCB congeners) along with 120 experimentally determined HLC values from the (35) Goss, K. U.; Wania, F.; McLachlan, M. S.; Mackay, D.; Schwarzenbach, R. P. Environ. Sci. Technol. 2004, 38, 1626-1628. (36) Baker, J. E.; Totten, L. A.; Gigliotti, C. L.; Offenberg, J. H.; Eisenreich, S. J.; Bamford, H. A.; Huie, R. E.; Poster, D. L. Environ. Sci. Technol. 2004, 38, 1629-1632.
5416 Analytical Chemistry, Vol. 78, No. 15, August 1, 2006
literature,19,21,28-30,33 132 HLC values are available for 97 PCB congeners. If different HLC values (from different sources) were reported for a particular PCB congener, we calculated an average, weighted by experimental precision (eq 7), for that PCB congener. The result is 97 HLC values in the pooled data. PCR analysis was performed on this dataset, with the chlorine substitution pattern used as the independent variable (X0, a 97 × 10 indicator matrix), and log transformation of HLCs (log(H)) as the dependent variable (Y0). Six HLCs measured by Bamford et al.,30 one by Dunnivant et al.,29 and one by Brunner et al.21 fell outside of the 95% confidence intervals of predicted values. They were, thus, classified as possible outliers (Figure 3). The major technical difference among the gas purge studies lies in the method used for the preparation of the aqueous PCB solutions. We posit that the higher HLC values from Bamford et al.30 can be accounted for, at least in part, by the effect of the small amount of isooctane in the aqueous solution. Although Bamford et al. claim in their paper that the change in aqueous solubility caused by the presence of isooctane was negligible,30 a significant change in PCB solubility was observed by others6 when an aqueous solution was prepared in a manner similar to that used by Bamford et al. Further research is needed to explore this issue.
Figure 3. Measured HLCs (log H) from the literature and from this study. Possible outliers are indicated by arrows labeled with IUPAC number. ([) Bamford;30 (9) Brunner;21 (4) Dunnivant;19,28,29 (1) Fendinger;33 and (O) this study.
Figure 4. Cross-validation plots. Training (4, 2) and validation (3, 1) data are generated from the pooled data of 94 HLC values. Table 2. Parameter Estimates of Optimal PCR Model for Predicting HLCs on the Basis of Chlorine Substitution Pattern loading (L)
X xi
name
l1
l2
x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 R2 (%)
2 (ortho) 3 (meta) 4 (para) 5 (meta) 6 (ortho) 2′ (ortho) 3′ (meta) 4′ (para) 5′ (meta) 6′ (ortho)
1.15 1.69 -1.74 0.37 2.79 3.28 0.68 -2.96 1.65 1.44 16.38
-0.49 2.86 2.19 2.56 -1.09 0.34 2.80 1.57 2.30 -0.37 47.30
After we remove the eight outliers (Figure 3) from the original dataset (132 HLC values for 97 PCB congeners), 124 HLC values are available for 94 PCB congeners. We then recalculated an average, as described above, for each PCB with multiple HLC values. PCR analysis was again performed on this new pooled dataset of 94 HLC values for the purpose of establishing a predictive model for HLCs. Cross-validation yields an optimal number of two principal components (PCs); this choice is associated with a higher R value and lower root-mean-square error (RMSE) value (Figure 4). Parsimony (that is, the simplicity of the model) is also taken into account. The two PCs explain similar amounts of the total variance of the indicator matrix (X), 20.2 and 18.8%, respectively. However, the second PC explains 47.3% of the variance of log(H), significantly more than does the first PC (16.4%) (Table 2). In addition, the second PC resolves more than 70% of the total variance accounted for by the full PCR model with all 10 PCs. Therefore, it is the dominant variable in the predicting power of this model. The optimal PCR equation can be expressed as
log H ) 0.99 - 0.01x1 - 0.36x2 - 0.12x3 - 0.27x4 0.04x5 - 0.20x6 - 0.30x7 + 0.003x8 - 0.31x9 - 0.04x10 (eq 8) where xi represents the centered value of the chlorine substi-
Figure 5. Predicted versus measured HLCs (log H) of 94 congeners (124 observations, excluding the 8 possible outliers labeled in Figure 3). ([) Bamford;30 (9) Brunner;21 (4) Dunnivant;19,28,29 (1) Fendinger;33 and (O) this study.
tution index on position i (for i ) 1-10, referring to 2 (ortho-), 3 (meta-), 4 (para-), 5 (meta-), 6 (ortho-), 2′ (ortho-), 3′ (meta-), 4′ (para-), 5′ (meta-), and 6′ (ortho-), respectively, in the IUPAC No. nomenclature as shown in Table 2). The linear coefficients are the product of the MLR coefficients (log(H) versus the score matrix with the first two PCs) and loadings for the first two PCs. Because all PCs are orthogonal to one another, linear regression coefficients in eq 8 are stable and, thus, quantitatively represent the effect of each substitution position on HLCs. The scatter plot of predicted versus measured HLCs is presented in Figure 5. The predicted values agree well with the experimental data. A more complicated indicator matrix, which additionally takes into account the neighboring effects of substituted chlorines and the multiple interactions among ortho-chlorines, gave no significant improvement in the prediction (R2 ) 0.68). Therefore, eq 8 was used to predict the HLCs for the entire set of 209 PCB congeners. The predicted values of HLCs have relatively wide confidence intervals, so more experimental data will be necessary if we are to improve the quality of the prediction. The effect of the chlorine substitution pattern of PCB congeners on HLCs has been discussed.17,19,21,22,28-31 One limitation for all of these studies is “selective bias”. This is well-demonstrated by the fact that most studies have failed to establish a relationship between HLC values and molecular weight, except for studies using the data of Brunner et al.21 Another limitation lies with the simple linear regression: if independent variables are somewhat correlated, the linear coefficients can be unstable. That is, an independent variable with high predictive power may be regarded as insignificant. Our analysis does not suffer from either of these two limitations. Thus, our QSPR model is more reliable than other models using conventional linear regression. In addition, compared to such constructs as molecular holograms22 and weighted holistic invariant molecular (WHIM) descriptors,37 the substitution index matrix that we use is easy to derive and to understand, and the model is sensitive to the chlorination pattern. Values of the loadings can be used to understand the meanings of the two PCs in our analysis. For the second PC, the most (37) Gramatica, P.; Navas, N.; Todeschini, R. 1998, 40, 53-63. (38) Bamford, H. A.; Ko, F. C.; Baker, J. E. Environ. Sci. Technol. 2002, 36, 4245-4252.
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important latent variable in predicting HLCs, all meta substitutions are associated with higher absolute numbers, as compared to those with ortho or para substitutions (2.86 for x2, 2.56 for x4, 2.80 for x7, and 2.30 for x9, as shown in Table 2). This indicates that meta-chlorine is actually the most important factor in predicting HLCs, yet this factor has never been reported in the literature. It is quite possible that the effect of meta-chlorine is masked in conventional linear regression, when the total number of chlorines (Ncl) is used as an independent variable. Equation 8 suggests that the HLC value decreases directly with increasing number of the meta substitution. Chlorine substitutions on other positions (orthochlorines and para-chlorines) have much weaker effects on the values of the HLC, as shown by the value of loadings in Table 2 as well as linear coefficients in eq 8. In general, chlorine substitutions (ortho-, meta-, or para-chlorines) lead to a smaller HLC, which is not a contradiction to the inverse relationship between HLCs and the Ncl reported in the literature. However, although each substitution position has a different effect on HLCs, the effect of different chlorine substitutions is not observed in a two-dimensional plot in most cases when Ncl, a variable expressing the combined effect of all substituted chlorines with equal weights, is used as a descriptor. The observed relationship between orthochlorines and HLCs in conventional linear regression is likely to be an artifact arising from overestimation of the effect of orthochlorines when Ncl is used as a descriptor. The QSPR model developed in this study can also be used to model vapor pressures and octanol-air partition coefficients, important parameters that influence the estimation of mass transport in the atmosphere. It may be necessary to reassess the relationship between the structure of PCBs and other physical
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properties, such as water solubility and octanol-water partition coefficients, by means of advanced statistical techniques, such as PCR analysis. CONCLUSIONS HLC values for 12 non- and mono-ortho PCBs were determined using a modified gas purge technique. Gas/water partitioning reached equilibrium, as we verified by varying the gas-flow rate. Measured HLCs of 12 PCB congeners range from 5.6 to 21.8 Pa m3/mol, and these values are comparable to values reported in the literature. The experimental precision (relative percent standard deviation or RSD) is 13%. Our device was easily set up and dismantled, and the technique proved to be sensitive in that it measured the aqueous concentration only. It is particularly suitable for low-solubility solutes, and no large liquid phase is necessary. Meta-analysis and PCR techniques were used to construct a QSPR between the chlorine substitution patterns and HLCs of PCBs. Our results provide a QSPR model that differs from what other studies have provided. The PCR analysis isolated the effect of each chlorine substitution position on the HLC, and it identified meta-chlorine as the factor most important for predicting HLCs. ACKNOWLEDGMENT This research was supported in part by the NIEHS Superfund Basic Program under Grant No. ESO4913-10. Received for review March 15, 2006. Accepted June 1, 2006. AC0604742
