Environ. Sci. Technol. 1987, 27, 1102-1 107
fluid-phase solute concentration for component i, mg/L initial fluid-phase solute concentration, mg/L Freundlich isotherm capacity coefficient external mass transfer coefficient for a particle of radius r, cm/s bed depth, cm Freundlich isotherm energy coefficient solid-phase concentration of component i at concentration Ci in the presence of component j at concentration Cj,mg/g of carbon particle radius, cm surface-meanradius, cm hydraulic surface loading, gpm/ft2 superficial flow velocity, cm/min weight of adsorbent fraction having a particle radius r(i),g total weight of adsorbent of mixed particle size, g bed void fraction, dimensionless Literature Cited (1) Weber, W. J., Jr.; Smith, E. H. Environ. Sci. Technol. 1987, 21 , 1040-1049. (2) Rosene, M. R.; Deithorn, R. T.; Lutchko, J. R.; Wagner,
N. J. In Activated Carbon Adsorption of Organics from the Aqueous Phase; Suffet, I. H., McGuire, M. J., Eds.; Ann Arbor Science: Ann Arbor, MI, 1980; Vol. 1. Weber, W. J., Jr.; Liu, K. T. Chem. Eng. Commun. 1980, 6 , 49.
Liu, K. T.; Weber, W. J., Jr. J.-Water Pollut. Control Fed.
1981,53, 1541. Liang, S.; Weber, W. J., Jr. Chem. Eng. Commun. 1985,35, 49. Smith, E. H.; Tseng, S.; Weber, W. J., Jr. Environ. Prog. 1987, 6, 18.
Roberts, P. V.; Cornell, P.; Summers,R. S. J. Environ. Eng. Diu. (Am. SOC.Ciu. Eng.) 1985, 111(6),891. Crittenden, J. C.; Luft, P.; Hand, D. W. J. Environ. Eng. Diu. (Am. SOC.Civ. Eng.) 1987, 113(3),486. Wang, C. K. Ph.D. Dissertation, The University of Michigan, Ann Arbor, MI, 1986. Perry, J. H.; Chilton, C. H. Chemical Engineer's Handbook, 5th ed.; McGraw-Hill: New York, 1973; pp 5-53. Received for review January 30,1987. Accepted July 28,1987. This research was supported in part by Grant CR-809808 from the Exploratory Research Grants Program, US.Environmental Protection Agency. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.
Use of Pattern Recognition Techniques To Characterize Local Sources of Toxic Organics in the Atmosphere Sylvia A. Edgerton" and Michael W. Holdren Battelle Columbus Division, Columbus, Ohio 4320 1-2693
Pattern recognition techniques are used to characterize local sources of toxic air contaminants. Data collected in the Kanawha Valley, WV, from four sites are used to construct profiles of chemical emissions from nearby chemical industries. A regional profile is also constructed. A chemical mass balance (CMB) model is used to apportion the emission of selected toxic compounds at the sites among the chemical industries and the regional mixed sources. At sites 1 and 2, which are near one chemical industrial complex, the ambient concentrations of toxics are due almost entirely to the local source. At sites 3 and 4,which are near a second chemical industrial complex, more widespread regional sources often contribute to the ambient concentrations of the toxic compounds. The use of pattern recognition to construct source profiles is useful especially for area sources and where source emission data are not available from specific point sources. These techniques allow a distinction to be made between point and area source contributions to toxic air pollutants. Introduction
There has recently been a growing concern over the concentration of toxic organic compounds in the atmosphere of urban areas. Control of toxic air contaminants is among the highest priority activities within the U.S. EPA, and many states have developed Air Toxics Programs to address specific issues relevant to the individual state or locality. The EPA has identified four major themes that should be stressed in state and local programs that address the problem of toxic air contaminants. Two of the four major themes are (1) the identification of high-risk point sources and (2) the identification of highrisk urban problems ( I ) . 1102
Envlron. Sci. Technol., Vol. 21, No. 11, 1987
Many states are developing air toxics inventories to provide preliminary screening estimates for source categories of toxic air pollutants and to help understand general patterns and trends in pollutant emissions. These inventories are often used as input to dispersion models for estimating ambient air concentrations of toxic pollutants. There are many uncertainties in these emission estimates. Besides the questions of interpreting annual vs short-term emissions, process vs fugitive emissions, and accidental vs routine emissions, emission information for specific toxic compounds is generally not available. Responses to air quality agency questionaires are often incomplete but not necessarily due to lack of cooperation on the part of the industries that are asked to participate. The use and storage of complex chemical mixtures marketed under different trade names make it difficult to recognize what emissions might be present. Fugitive emissions are also hard to estimate and may vary widely from plant to plant and from day to day. Area sources and many smaller sources are often not included in pollutant inventories, and these sources may collectively be more important than some of the larger single source emitters. The effect of all these uncertainties in emission estimates is a low confidence level in the estimated ambient concentrations of toxic air pollutants, which are based on models requiring accurate input emission information. While there are also problems with relying solely on ambient data from a limited number of urban sampling sites to evaluate an urban area for high-risk point sources, ambient and source data together can provide a much more complete picture of the problem than either taken alone. In this paper, we explore the use of pattern recognition techniques on ambient air data to characterize local sources of toxic air contaminants in an urban area with many
0013-936X/87/0921-1102$01.50/0
0 1987 American Chemical Society
Table I. Target Compounds Measured in Field Monitoring Study compd no. 1 2
3 4 5
6 7
compd name
compd no.
compd name
vinyl chloride 1,l-dichloroethene dichloromethane trichloromethane 1,2-dichloroethane l,l,l-trichloroethane benzene
8 9 10 11 12 13 14
tetrachloromethane trichloroethene toluene 1,2-dibromoethane tetrachloroethene chlorobenzene o-xylene
complex sources of toxic pollutants. Pattern recognition is a technique that attempts to extract and utilize information from large and complex data sets. The data set used in this study is one of ambient air concentrations of 14 toxic compounds measured in the ambient air at four sites in an urban area on 15 different sampling days. The data are divided into a training data set and a test data set. A classification model is developed on the training data set that allows samples to be classified as influenced by local sources specific to each site or by regional sources characteristic of the urban area. A characteristic profile of compounds is developed for each specific local “source area”. The test data set is then modeled with a linear regression technique to apportion the sources of the toxic organics among local sources and a regional contribution.
Chemical Complex A
River
Chemical Conplex B
Kanwha State Foreit
Figure 1. Map of the Kanawha Valley, WV, showing the location of chemical complex A, chemical complex 6,and the sampling sites. 27
18 93
64 40
51 7 5 “2
Experimental Section A field monitoring study was carried out by Battelle Columbus Division and the U.S.Environmental Protection Agency to provide information on the concentration range of selected toxic chemicals present in the ambient air in the Kanawha Valley of West Virginia. The Kanawha Valley includes the city of Charleston, WV, and has a high density of chemical and manufacturing industries. The primary sampling and analysis effort focused on the 14 target compounds shown in Table I. A complete description of the monitoring program and the resulting raw data can be found in Holdren et al. (2). Two different sampling techniques were used to determine the 14 target organic species. The first approach made use of stainless steel canister sampling devices to collect whole-air samples. Each canister sampler utilized a mass flow controller, pump, and valve assembly programmed to automatically fill an evalcuated canister with a time-integrated (24-h) sample of air. The second collection technique involved the use of distrjbutive air volume (DAV) sampling with Tenax solid adsorbent tubes. With this technique, four samples were collected simultaneously at different flow rates over the same 24-h period as the canister sampling. Both sampling techniques have been described elsewhere (3-5). Sample analyses were achieved with a cryogenic gas chromatographic system (6) equipped with a flame ionization and a mass selective detector system. Sampling was carried out at four field sites. Sites 1 and 2 were located in residential areas near a chemical complex in Institute, WV, which we will refer to as chemical complex A, and sites 3 and 4 were located in residential areas near a chemical complex in Belle, WV, which we will call chemical complex B. Institute, WV, is approximately 18 km northwest of Charleston, and Belle, WV, is approximately 14 km southeast of Charleston (see Figure 1). Canister samples were collected approximately every 3 days at the four monitoring sites over a 45-day period. Distributive air volume (DAV) sampling was carried out at one site on alternate canister sampling days. Fifteen
57 52 63 7655
80 6 8 w 5
26
90 5 4 3877
74
3H3 m417
50 32218
5913 85-4
1330 28 lM4HM 83 1 5 29
66
43
24
10
1
Figure 2. Principal components score plot for the entire data set, component 1 plotted against component 2. Each number is a sample number, and the M indicates that one ore more sample numbers fall on top of each other. The samples 66 and 74 are Identified as outliers, and the evaluation of the samples showed these to be contaminated.
sampling runs (one run per day) were completed during the study. A future paper will describe the comparison of the Tenax and canister sampling results.
Data Analysis Principal Components Analysis. A principal components analysis (PCA) of a data set allows one to search for outliers in the data and observe dominant patterns in a data table. It is a method of reducing the dimensions in a data matrix by modeling the data on principal axes, which explain most of the variance in the data. Mathematical details of the computation and examples of the utilization of PCA in interpreting environmental data can be found in the literature (7-13). In this study, we first use principal components analysis for identification of outliers in the data and then as a basis for classification of samples. The principal components (PC) and classification calculations (14, 15) are accomplished with the use of the SIMCA-3B software for the IBM-PC from Principal Data Components (16). By intially fitting the data to two principal components and plotting the individual factor scores of each sample for each component, a two-dimensional “window” of the data is constructed. Gross outliers can be usually identified in Environ. Sci. Technol., Vol. 21, No. 11, 1987
1103
10
“14 MI 8
3
Flgure 3. Principal components score plot for the training data set, component 1 plotted against component 2. The two principal components explain 85% of the variance. The number corresponding to the sample site of each sample is shown. Cluster A contains samples characteristic of sites 1 and 2, cluster B contains samples characteristic of sites 3 and 4, and cluster C Is characteristic of combined regional sources. M occurs where two or more samples fall on top of one another.
these PCA score plots as sample points separated from the primary cluster of points. Figure 2 shows the results of a PCA score plot for all 93 samples of toxic organic data in the Kanawha Valley data set. Samples 66 and 74 appear to be outliers. Sample 66 had been previously judged to be contaminated, and it appears that sample 74 is also possibly contaminated. These samples were collected consecutively by the same sampler and are the only ones out of the 93 samples that have appreciable concentrations of compounds 11and 14 (1,Zdibromoethaneand o-xylene). A Tenax sample collected simultaneously with sample 66 at the same location did not contain these compounds. The sampler appears to have been contaminated;therefore, samples 66 and 74 are removed from the data set in further calculations. In general, one should use caution in deleting data points, assuring that the data are truly in error. Classification of Samples. Data from runs 1-9 are then used as a training set to classify samples. The training set is used to develop mathematical rules for assigning objects to statistically defined classes on the basis of the data. Because some discrepancies were found in the analysis of the Tenax samples for a few compounds (notably benzene and tetrachloroethene), only canister samples were used for further analysis (58 samples). A principal components analysis of the training set is carried out, and the factor score plot for the first two components is shown in Figure 3. The use of such a large portion of the data for training is necessary to obtain the minimum number of samples for constructing both class models. Each class should have at least five objects, although 10-20 are preferable (15). Two significant principal components are found, explaining 85% of the variance. The program contains a cross validation routine to calculate a correction factor for determining the significance of the components by compensating for the fact that the residuals become smaller as the degrees of freedom are used up. Three clusters are apparent in the plot. One includes samples collected at sites 1and 2, which are near chemical complex A, a second includes samples collected at sites 3 and 4, which are near chemical complex B, and the third includes samples collected from all the sites. We now attempt to 1104
Envlron. Sci. Technol., Yol. 21, No. 11, 1987
Flgure 4. Variable loadings for the first two princlpal components plotted in Figure 3. The numbers correspond to the compound numbers listed in Table 11. M occurs where two or more variables fall on top of each other. The zero point is located in the tight cluster where numbers 1 and 14 and the M’s occur. Variables 3, 4, 8, and 10 contain the most informatlon for source dlscrimintion,
classify the samples as being influenced locally, such as those in clusters A and B, or influenced regionally, such as those in cluster C. A complementary plot of the variables projected into the two-dimensional window can show clusters of variables that may help discriminate between the classes. Figure 4 shows a variable plot for the training set, including the zero point. All of the compounds except four cluster around zero. Compounds 3 , 4 , 8 , and 10 provide the most information for class differentiation. It will be shown that compounds 4,10, and 14 are useful variables for characterizing the sites near chemical complex A, while compounds 3 and 8 are characteristic of the sites near chemical complex B. The SIMCA (soft independent modeling of class analogy) program models each class separately by a principal components (PC)model. In the Kanawha Valley data, class A includes samples that are representative of the local sources near chemical complex A, and Class B includes samples that are representative of local sources near chemical complex B. These two classes are then PCmodeled with the samples identified in Figure 2 as belonging to class A or class B. Two principal components are found for class A and class B explaining 89% and 95% of the variance, respectively. A tolerance interval is constructed around each class, and test objects can then be tested for membership in class A, in class B, or in neither. The tolerance interval is calculated from the class standard residual deviation (RSD) and is a modified-F test based on ( M - A ) and ( N - A - 1)(M- A) degrees of freedom, where M is the number of variables, N is the number of objects in the class, and A is the number of components used to model the class (14). The results from the classification of the test objects in runs 10-15 (18 canister samples) are shown in Figure 5. Samples located near the Y axis belong to class A, samples located near the X axis belong to class B, and samples at a distance from both axes belong to neither class and therefore represent a combined regional and local influence on toxic pollutant concentrations. Source Area Characterization and Apportionment of Toxic Compounds. Source profiles representative of each source area are developed from the samples in the training data set that have been identified as belonging
a
~
Table 111. Comparison of Model-Derived Emission Profile Ratios with Those Obtained from Emission Inventory
1 RSD
emission inventory Chemical Complex A dichloromethane/ benzene 2 trichloromethane/ benzene 3
93
Chemical Complex B dichloromethane/ benzene 6 trichloromethane/ benzene 5 tetrachloromethane/ benzene 2
64
ctm
D
Figure 5. Coomans' or residual plot of class dlstahces. The classification of test objects as belonging to class A, close to the vertical axis, or class B, close to the horizontal axis. The A s and B's represent the training samples used to define the classes.
Table 11. Source Profiles Developed from the Classification of Samplesn
compd no. 3
4 6 7
8 10 12 13 14
f 41 chemical chemical complex A complex B (n = 7) (n = 5)
compd name dichloromethane trichloromethane l,l,l-trichloroethane benzene tetrachloromethane toluene tetrachloroethene chlorobenzene o-xylene
regional source (n = 9)
0.08 f 0.01 0.48 f 0.04 0.17 f 0.04 0.19 0.05 0.20 0.03 0.08 0.03 0.06 f 0.02 0.04 f 0.01 0.14 f 0.02
*
*
*
0.10 f 0.03 0.05 f 0.01 0.18 f 0.02 0.03 0.01 0.05 0.01 0.05 0.01
*
*
0.44 f 0.04 0.03 f 0.01 0,02 0.01 0.05 f 0.02
0.14 f 0.01 0.00 f 0.01 0.00 0.01 0.03 f 0.01
*
*
0.29 f 0.02 0.02 f 0.01 0.00 0.01 0.07 f 0.01
*
a The f i j is the mass fraction of compound i in source j out of all nine compounds. The values are the standard errors of the mean.
*
to class A, class B, or neither. Each profile is a "fingerprint" of local source emissions and consists of the mass fraction of each toxic compound present in the emissions with respect to the total emissions of all compounds included in the profile. The compounds included in the profile for both classes are dichloromethane, trichloromethane, l,l,l-trichloroethane, benzene, tetrachloromethane, toluene, tetrachloroethene, chlorobenzene, and o-xylene (compounds 3,4,6-8, 10, and 12-14). These are the compounds that contain useful information for source discrimination purposes. The samples of the training set that do not belong to class A or class B ( p < 0.05) are considered to be representative of a regional profile of mixed sources, and these are used to develop the regional profile. The resulting profiles are shown in Table 11. The average mass fraction of toxic compound i in source class j , fL,, is calculated as
where Q(s,)~is the mass concentration (ng/L) of compound i in sample s, and n is the number of samples used to define class j .
model 1 2
10 4 1
The f values are the standard error of the mean. Chemical complex A is characterized by a high-mass fraction of trichloromethane and toluene (compounds 4 and 10). Chemical complex B is characterized by a highmass fraction of dichloromethane and trichloromethane (compounds 3,4). The regional source is characterized by dichloromethane (methylene chloride), benzene, and toluene, which are all known to have a wide range of area sources. Emission inventory information, from point and area sources associated with chemical complexes A and B, was obtained for several of the compounds modeled (17). The ratio of these compounds to benzene in the modeled emission profile is compared with the ratio in the emission inventory in Table 111. A chemical mass balance (CMB) model is used on the test data set to apportion the sources of the measured toxic compounds between chemical complex A (class A), chemical complex B (class B), and a regional source. The CMB model is based on a linear least-squares fit to measured ambient concentrations of chemicals and the source profiles of those chemicals to predict the contribution of each source to the total ambient chemical concentrations. The mathematical details and applications of the CMB model in apportioning sources of atmospheric chemicals have been described in the literature (11, 18-20). Previously, CMB models have been used to evaluate sources of volatile hydrocarbons where the source profiles have been estimated from the literature (21-23). In many cases, such as in the Kanawha Valley, source emission estimates for toxic organics are not available for major sources. Pattern recognition techniques may be the only method of creating source profiles in these cases. The CMB model was applied to all test samples that could not be classified as belonging to class A or class B and, therefore, were influenced by a combination of sources. The CMB software used was the QSAS-I11source apportionment model (24) with effective variance fitting, which weights the model parameters by uncertainties in both the source profiles and the ambient concentrations. The results of the model application are shown in Table IV. The measured/ (model calculated) ratio of chemical compounds was always within the range of 0.5-2.0, implying a good model fit. With the exception of run 14, which could only be explained as regional influence, the other samples were all dominated by the local source. The sites surrounding chemical complex A (sites 1 and 2) were almost always dominated by the local sources. The sites surrounding chemical complex B (sites 3 and 4) however were often influenced by a combination of sources. Table V shows the average contributions at the sites for the modeled samples. The chemical concentrations at sites 3 and 4 are frequently due to a combination of both local and regional sources. The highest mean concentrations of trichloromethane and toluene (compounds 4 and 10) are observed a t sites 1 and 2, while the highest mean concentrations of dichloromethane and tetrachloromethane (compounds 3 and 8) are observed at sites 3 and 4. This Environ. Sci. Technol., Vol. 21, No. 11, 1987
1105
1
Table IV. Apportionment of Selected Chemicals (Compounds 3,4,6-8, 10, and 12-14) among Regional and Local Source Categories in the Kanawha Valley modeled contributions, ng/L total chemical chemical measured complex complex regional mass, A B source ng/L
run no. (run date) 10 (3/30/86)
75 0 6 28 5 12 3
11 (4/02/86)
12 (4/04/86)
11
13 (4/07/86)
14 (4/09/86) 15 (4/11/86)
31 18 0 0 0 0 26 0
0 78 16 0 35 27 32 19 0 0 27 41 0 0 2 61
7 0 7 6 0
70 82 40 30 46 58 46 37 34 19 37 47 31 7 26
0
85
0
8 22 0
8 13 14 9 4 2
Table V. Average Contribution a t the Sample Sites to Selected Toxic Chemical Concentrations Determined by the CMB Model” % contributionb
sites 1 and 2 (n = 5)
sites 3 and 4
source chemical complex A chemical complex B regional source
94 f 2 If1 412
9 f 4 60 f 10 30 13
(n = 9)
*
=The f values are the standard errors of the mean. Samples 83 and 93 are excluded because a significant fraction of the mass was not accounted for in the model. bReferenced to the total modeled mass.
is consistent with the local source profiles that have been developed with the pattern recognition technique. The mean concentrations of all measured compounds at all sites are shown in Figure 6. Results and Discussion
The application of pattern recognition techniques to ambient air data is useful for identifying and evaluating sources of toxic pollutants in urban areas. In areas with a complex combination of point and area sources of toxic chemicals, it is useful to be able to estimate the relative contribution of each. In the Kanawha Valley example, one sample area was shown to be dominated primarily by local sources, while the other often experienced a significant regional impact. A useful extension of this study would be the collection and evaluation of ambient air data in Central Charleston and the estimation of the relative influence of the two chemical source complexes. The principal component plots allow one to evaluate an ambient air data base for (1)the presence of outliers or contaminated samples in the data, (2) clusters of samples that vary in a similar way and might represent specific sources or source areas, and (3) clusters of variables that are useful for characterizing the variance in the data and the contributing sources. Classifying the samples into those dominated by specific sources or source areas then allows one to construct chemical profiles to characterize these sources. This is useful especially for area sources and where source emission data are not available from a specific point source. Once the profiles have been constructed, a 1106
Environ. Sci. Technol., Vol. 21, No. 11, 1987
16 15
14 13
P
3 4
rza
sm1
124
SITE 2
COMPOUND NUMBER SliE 3
Figure 6. Bar graph of mean concentrations of target compounds at each monitoring site. Sites 1 and 2 are near chemical complex A, and sites 3 and 4 are near chemical complex B.
chemical mass balance (CMB) model may be applied to apportion the ambient chemical concentrations among the sources. To fully address the problem of toxic pollutants in the urban atmosphere, it is necessary to identify what sources contribute most to those pollutant concentrations in the ambient air. By making point/area source distinctions in contributions to toxic air contaminants, control strategies may be developed and evaluated more effectively. The pattern recognition and CMB modeling techniques described in this paper represent a complimentary method of evaluating sources of toxic air pollutants to the more traditional emission inventory method. Acknowledgments
We thank Tom Hartlege, Tom Lumpkin, and Bill McClenny of the US.EPA (Research Triangle Park, NC) for their efforts during the Kanawha Valley air monitoring study (EPA Contract 68-02-3745). Registry No. 1, 75-01-4; 2, 75-35-4; 3, 75-09-2; 4, 67-66-3; 6 , 107-06-2; 6,7165-6; 7,71-43-2; 8,56-23-5; 9,79-01-6; 10,108-88-3; 11, 106-93-4; 12, 127-18-4; 13, 108-90-7; 14, 95-47-6.
Literature Cited (1) “Guidance for Preparing State and Local (S/L) Multiyear
(2)
(3)
(4) (5)
(6) (7) (8)
Development Programs for Air Toxics”; U.S. EPA memorandum to Air Division Directors, Regions I through X, Aug 5, 1986. Holdren, M. W.; Smith, D. L.; Smith, R. N.; Ward, G. F. ‘‘Monitoring for Toxic Organic Compounds in the Kanawha Valley, West Virginia”; Final Report to the U.S. Environmental Protection Agency under Contract 68-02-3745; Battelle Columbus Division: Columbus, OH, May 1986. Westberg, H. H.; Lonneman, W.; Holdren, M. W. Analysis of Individual Hydrocarbon Species in Ambient Atmospheres: Techniques and Data Validity; Symposium on the Identification of Analysis of Organic Pollutant in Air; Keith, L. H., Ed.; Ann Arbor Science: Ann Arbor, MI, 1983. Walling, J. F. Atmos. Enuiron. 1984, 18, 855-859. Spicer, C. W.; Koldren, M. W.; Slivon, L. E.; Coutant, R. W.; Shadwick, D. S. “Intercomparsion of Sampling Techniques for Toxic Organic Compounds in Indoor Air”; Final Report on EPA Contract 68-02-3745; Battelle Columbus Division: Columbus, OH, Sept 1986. McClenny, W. A.; Pleil, J. D.; Holdren, M. W.; Smith, R. N. Anal. Chem. 1984,56, 2947-2951. Harman, H. H. Modern Factor Analysis, 3rd ed.; University of Chicago Press: Chicago, IL, 1976. Henry, R. C.; Hidy, G. M. Atmos. Enuiron. 1979, 13, 1581-1586.
Environ. Sci. Technol. 1987, 21, 1107-1 111
(9) Henry, R. C.; Hidy, G. M. Atmos. Environ. 1981, 16, 929-943.
(10) Hopke, P. K. In Atmospheric Aerosol: SourcelAir Quality Relationships;Macias, E. S., Hopke, P. K., Eds.; American Chemical Society: Washington, DC, 1981; pp 21-49. (11) DeCesar, R. T.; Copper, J. A. In Receptor Models Applied to Contemporary Pollution Problems; Dattner, S. L., Hopke, P. K., Eds.; Air Pollution Control Association:
Pittsburgh, PA, 1983;pp 127-140. (12) Hopke, P. K. Receptor Modeling in Environmental Chemistry; Wiley: New York, 1985. (13) Shard, M. A,; Illman, D. L.; Kowalski, B. R. Chemometrics; Wiley: New York, 1986; Chapter 6, pp 179-296. (14) Wold, S.; Sjostrom, M. In Chemometrics: Theory and Applications; Kowalski, B. R., Ed.; American Chemical Society: Washington, D.C, 1977; pp 243-281. (15) Wold, S.; Albano, C.; Dunn, W. J.; Edlund, U.; Esbensen, K; Geladi, P.; Hellberg, S.; Johansson, E.; Lindberg, W.; Sjostrom, M. In Proceedings of a NATO Advanced Study Institute on Chemometrics;Nowalshi, B. R., Ed.; Reidel: Dordrecht, Holland, Sept 1983; pp 17-25. (16) SIMCA-3B Pattern Recognition Programs;Principal Data Components: Columbia, MO.
(17) Lumpkin, T., U. W. Environmental Protection Agency, Research Triangle Park, NC, personal communication, 1987. (18) Friedlander, S. K. Environ. Sci. Technol. 1973,7,235-240. (19) Watson, J. G. PbD. Dissertation,Oregon Graduate Center, Beaverton, OR, 1979. (20) Henry, R. C.; Lewis, C. W.; Hopke, P. K.; Willimson, H. J. Atom. Environ. 1982,18,1507-1515. (21) Mayrsohn, H.; Crabtree, J. H., Atmos. Environ. 1976,10,
137-143. (22) Wadden, R. A,; Uno, I.; Wakamatsu, S. Environ. Sci. Technol. 1986,20, 473-483. (23) Klevs, M.; Scheff, P. A. Presented at the 1986 EPA/APCA Symposium on Measurement of Toxic Air Pollutants, Ra-
leigh, NC, April 1986;U.S. EPA Research Triangle Park, NC, 1986. (24) Quantitative Source Apportionment System Z Z t NEA: Beaverton, OR, 1984. Received for review February 20, 1987. Accepted July 7, 1987. This paper has not been subjected to Agency review and therefore does not necessarily reflect the views of the Agency, and no official endorsement should be inferred.
Influence of Organic Cosolvents on Leaching of Hydrophobic Organic Chemicals through Soils Peter Nkedl-Klzza," P. Suresh C. Rao, and Arthur G. Hornsby Soil Science Department, University of Florida, Gainesvllle, Florida 326 11
The sorption and leaching of two herbicides (diuron and atrazine) were measured in soil columns eluted with aqueous solutions and binary solvent mixtures of methanol and water. These data vere used to evaluate the solvophobic theory recently outlined for describing sorption and transport of hydrophobic organic chemicals (HOC) from mixed solvents. The retardation factor (R") for both herbicides decreased drastically as the volumetric fraction of organic cosolvent (f") was increased in the binary solvent mixture. The log-linear decrease in (R" - 1)observed with increasing f " was well predicted by the solvophobic theory. All breakthrough curves (BTCs) were asymmetrical in shape, but the extent of asymmetry decreased with increasing f" for 0 5 f" I 0.5. At f " = 0.5, the BTCs for both diuron and atrazine were similar in shape (symmetrical and sigmoidal) and location (R" = 1)to that of tritiated water, a nonadsorbed tracer.
chemicals in soils in the presence of aqueous and aqueous-organic solvent mixtures. This theory was used (3) to explain the trends in the soil TLC leaching data reported by Hassett et al. (I). The same theory was also successfully used by Nkedi-Kizza et al. (4) to predict the sorption of two herbicides (diuron and atrazine) and anthracene by several soils from binary mixtures of methanol-water and acetone-water and by Woodburn et al. (5) for diuron sorption from ternary solvent systems. The objective of this study was to test the validity of the solvophobic theory (3) by using data for leaching of diuron and atrazine herbicides in soil columns eluted with water and binary mixtures of methanol and water. Theory
The retardation factor ( R )is a measure of the mobility of solute being eluted through a soil column. For displacement with water, R is given by (6)
Introduction Most of the research on leaching of organic chemicals through soils has been done with aqueous systems. It is, however, likely that a mixture of solvents (water plus organic cosolvents) may be present at land disposal sites where these chemicals are disposed. Only a few studies to date have addressed the leaching of organic chemicals through soils by mixed solvents. Hassett et al. (1) used a soil thin-layer chromatography (TLC) technique to measure Rf value's for the elution of the herbicide dicamba, 2-naphthol, and 3-methylcholanthrene by various watermethanol mixtures. Griffin and Chiou (2) also used a soil TLC method to examine the influence of organic solvents on the mobility of polybrominated biphenyls (PBBs) and hexachlorobenzene (HCB). In neither study were attempts made to quantitatively describe the relationship between leaching and the amount and the nature of the cosolvent. Rao et al. (3) presented a solvophobic approach to predict sorption and transport of hydrophobic organic 0013-936X/87/0921-1107$01.50/0
where the superscript w designates water and all the terms used in eq 1and elsewhere in this paper are defined under Glossary. For a nonadsorbed solute, R" = 1.0 since P" = 0.0. Thus, increase in sorption (P' >> 0) leads to an enhanced retardation of solute leaching (R" >> 1). The retardation factor (R") for leaching with binary solvent mixtures is given by (3)
where
Pm = P'exp(-cracfc)
0Sf"ll
(3)
AyC(HSA)/hT (4) The superscripts m and c denote respectively mixed sol-
0 1987 American Chemlcai Socie'tY
a'
Environ. Sci. Technol., Vol. 21, No. 11, 1987
1107
