Environ. Sci. Technol. 1907, 21, 1096-1 102
MacKenzie,A. B.; Scott,R. D. Analyst (London) 1979,104, 1151-1158. Christensen, E. R.; Bhunia, P. K. J . Geophys. Res. C: Oceans 1986, 91 (C7), 8559-8571. Health Saf. Lab. Environ. Q. (U.S. Energy Res. Dev. Adm.) 1977, HASL-329. Health Saf. Lab. Enuiron. Q. (U.S. Energy Res. Dev. Adm.) 1972, HASL-258. Kreyszig, E. Advanced Engineering Mathematics; Wiley:
New York, 1983. Krezoski, J. R.; Robbins, J. A. J . Geophys. Res. C: Oceans 1985, 90(C6),11999-12006. Edgington, D. N.; Robbins, J. A. Environ. Sei. Technol. 1976, 10(3),266-274.
(22) U.S. Bureau of Mines Minerals Yearbook;U.S. Department of the Interior: Washington, DC, 1984. (23) Eisenreich, S. J. Water, Air, Soil Pollut. 1980,13, 287-301. (24) Christensen, E. R.; Chien, N. K. Enuiron. Sci. Technol. 1981, 15(5),553-558.
Received for review November 14,1986. Accepted July 13, 1987. Although the information described in this paper has been funded by the U.S. Environmental Protection Agency under Assistance Agreement R810419 to E.R.C., it has not been subjected to the Agency’s required peer and administrative review and, therefore, does not necessarily reflect the views of the Agency, and no official endorsement should be inferred.
A Microscale System for Estimation of Model Parameters for Fixed-Bed Adsorbers Walter J. Weber, Jr.,* and Chang Keun Wang Environmental and Water Resources Engineering Program, The University of Michigan, Ann Arbor, Michigan 48 109
W
A technique for prediction of the performance charac-
teristics of fixed-bed adsorbers using parameters estimated from microscale experiments (micro diameter-depth adsorption systems, MIDDAS) is described. This technique employs specially designed “microcolumn” adsorbers containing small adsorbent particles to develop information for scale-up and design from relatively straightforward experimental measurements and associated model simulations.
Introduction The design of fixed-bed adsorbers generally requires information regarding the breakthrough characteristics of specific “target” compounds in systems containing several adsorbable species. Such information can be gained empirically from pilot-plant investigations, but tests of this nature are typically lengthy and expensive. Mathematical models that can utilize more fundamental data to predict adsorber performance for alternative design configurations and different conditions of operation provide useful adjuncts to pilot-scale studies. At a minimum, they can be used to optimize investigations involving pilot-scale fixed-bed reactor (FBR) systems and to facilitate interpretation of resulting data. Predictive FBR models require input of adsorption equilibrium and mass transport parameters characteristic of the particular thermodynamic and kinetic properties of the system(s) of interest (1). A variety of experimental techniques and numerical correlations for evaluation or estimation of such parameters are available, but many of these involve unacceptable levels of difficulty, imprecision, and uncertainty. The micro diameter-depth adsorption system (MIDDAS) technique described here was developed in response to the need for a relatively simple yet accurate means for determination of the equilibrium and mass transport parameters required for FBR adsorber modeling. The technique employs “deep-bed adsorber” microcolumns (MIDDAS-DBA’s)for equilibrium parameter determinations and ”short-bed adsorber” microcolumns (MIDDASSBAs) for mass transport or rate parameter evaluations. Both the MIDDAS-DBA and MIDDAS-SBA microcolumn systems employ adsorbent particles of significantly smaller diameter than typically used in full-scale FBR systems. 1096
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The use of “micro” particle diameter and bed depth dimensions promotes high rates of adsorption and early bed exhaustion in MIDDAS systems, thus facilitating rapid parameter measurements while still affording sufficiently representative system configuration and flow conditions to ensure accurate parameter characterization. A scale-up procedure has been developed to utilize parameters estimated from the MIDDAS technique to predict the breakthrough properties of target compounds in FBR adsorbers comprised of larger diameter columns, greater bed depths, larger adsorbent particles, and adsorbents of polydisperse particle size.
MIDDAS Development The MIDDAS technique employs a modified version of the “high-pressure minicolumn” (HPMC) method described by Rosene et al. (2) in combination with the microcolumn or short-bed adsorber (SBA) technique of Weber and Liu (3, 4 ) to determine capacity and mass transport parameters, respectively. The SBA is defined as an adsorber of sufficiently shallow depth to ensure “immediate” breakthrough of the compound(s) of interest. Mass transfer thus occurs over the entire bed depth after one theoretical “bed volume” or unit detention time. Weber and Liu ( 3 , 4 )employed sensitivity analyses of SBA breakthrough curves with respect to the extraparticle “film”-transfer coefficient (123 and intraparticle “surface diffusion” coefficient (D,) associated with a widely used two-resistance FBR adsorber model (1) to demonstrate that (1)the initial stage of an incipient breakthrough event in an SBA is dominated by film transfer, regardless of the eventually predominant mass transport control, and (2) the response of an SBA to changes in the parameters kf and D,is more sensitive than is that of a deep FBR adsorber. The authors verified the SBA approach for single-solute systems by successfully predicting FBR adsorber breakthrough curves using models calibrated with parameters obtained from SBA measurements and corresponding model simulations. Liang and Weber (5) and Smith et al. (6)subsequently extended the SBA procedure to estimation of mass-transfer coefficients for mixed-solute systems. The general validity of the SBA approach to rate parameter estimation has since been confirmed by other investigators (7, 8).
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The current work expands upon the simple SBA approach by incorporating a means for dynamic measurement of equilibrium isotherm or capacity parameters and by developing a procedure for adjustment of mass transport parameters to accomodate adsorbent particle size gradations, that is, polydisperse particle sizes. Adsorbent particles having mean diameters ranging from 0.006 through 0.102 cm (U.S. Standard Sieve mesh sizes 2001325 through 16/20) were tested to identify the most efficient and feasible particle size for determining both capacity and mass transport parameters. While a highly powdered form of carbon (e.g., less than 2001325 mesh size, the size range commonly used in HPMC tests and for isotherm capacity measurements) can provide rapid equilibrium, estimation of mass transport parameters from experiments conducted with powdered carbon is difficult and potentially inaccurate because of the combined effects of pressure drop, flow distribution, wall effects, and short circuiting. A particle size small enough to be comparable with a powdered adsorbent in terms of equilibration time yet sufficiently ”granular”to ensure reliable mass transport parameter estimation was chosen for the MIDDAS technique, specifically a particle size of 0.0164-cm mean diameter (80/100 mesh size). Experimental Section Calgon Filtrasorb 400 activated carbon (Calgon Corp., Pittsburgh, PA) was selected as the adsorbent. Four solutes having substantially different adsorption characteristics were chosen as adsorbates: p-chlorophenol (PCP), p-nitrophenol (PNP),p-toluenesulfonate (PTS), and dodecylbenzenesulfonate (DBS). Three solute pairs, PCP/PNP, PCP/PTS, and PCP/DBS, were selected for bisolute experiments on the basis of their isotherm and mass transport parameters and on the ease with which they could be differentiated by ultraviolet spectroscopy, the analytical technique employed. Capacity parameters were measured by both the traditional completely mixed batch reactor (CMBR) “bottle-point” technique and the MIDDAS-DBA technique. For MIDDAS-DBA isotherm determinations, 0.386-cm (i.d.) stainless steel columns were packed with known amounts of SO/lOO mesh size activated carbon and operated at ambient pressure and a superficial influent face velocity of 10.7 cm/min. Effluent samples were collected with a liquid fraction collector. The columns were run until the effluent concentrations were the same as their respective influent concentrations, at which point ”practical equilibration” was assumed. Effective adsorption capacities were then calculated by application of appropriate mass balance relationships. Parallel effective capacity measurements were made in MIDDAS-SBA systems; these were, as expected, lower than those determined from MIDDAS-DBA systems. Once a MIDDAS bed was made sufficiently deep that no incipient breakthrough occurred, unit adsorption capacity became essentially independent of bed depth. The same column and operating procedures used for the MIDDAS-DBA isotherm determinations were used for the rate parameter evaluations, except that a shorter bed was employed for the latter (MIDDAS-SBA). Four different particle size classes were chosen (16/20,30/40, 50/60, and SO/lOO mesh size) for evaluation of the effects of particle size on the external mass-transfer coefficient and for FBR adsorber verification. Three columns having different inside diameters (0.386, 1.021, and 2.54 cm) were used, the particular choice of diameter selected for a given study being made on the basis of the particle size involved in that study. Influent flow was maintained at a superficial face
velocity of 10.7 cmlmin for each column, regardless of size. Constraints on manuscript length prohibit presentation of results for all systems investigated. The data reported here are representative of the overall findings. Further experimental detail, additional data, and associated modeling codes are available elsewhere (9). Adsorption Equilibria Attainment of Equilibrium. The feasibility of using the MIDDAS-DBA technique to estimate adsorption capacity depends on whether a practically complete level of equilibrium has obtained by the time a given column run is terminated. Under the dynamic conditions typical of adsorption columns, there is the possibility of continued uptake and slow diffusion of solute into the solid phase, even beyond the point at which the effluent appears to have reached essentially the same concentration level as the influent (i,e,,“complete breakthrough). To investigate this possibility, a MIDDAS-DBA run was carried out until complete breakthrough (C, cz Gout) was observed. The feed pump was then turned off for a period approximately equal to that used initially to obtain this level of concentration breakthrough. Pumping was then reinitiated for 1-2 h to allow sample collection and then terminated for another period approximately equal to the initial breakthrough time. This procedure was repeated 5-7 times. The results indicated that diffusive uptake did indeed continue during the quiescent periods, the breakthrough concentration exhibiting a slight drop-off immediately after reinitation of flow. The contribution of this continued diffusion to total adsorption capacity was small ( 1.0 in eq 1 and 2.
Table 11. Experimental Conditions Used To Estimate kfand D , for Single-Solute Systems co,
run PC1 PN1 PT1 DB1
solute PCP PNP PTS DBS
mgIL 100.0 100.0 150.0 200.0
particle size class”
r, cm x
80/100 80/100
80/100 80/100
E
8, gpmIft2
cmlmin
L , cm
0.371 0.371 0.371 0.371
2.62 2.62 2.62 2.62
10.7 10.7 10.7 10.7
0.3 0.3 1.2 1.2
io3
8.1 8.1 8.1 8.1
v,,
“US.Standard Sieve mesh. Table 111. Single-Solute Values for kfand D , Determined by the MIDDAS-SBA Technique” run
solute
PC1 PN1 PT1 DB1
PCP PNP PTS DBS
kf? cm/s x io3 100.0 100.0 150.0 200.0
5.0 5.2 2.7 1.5
15.0 6.0 3.0 0.65
“For a 80/100 U.S. Standard Sieve mesh F-400 carbon.
Figure 4. MIDDAS-DBA breakthrough data (symbols) and MADAM simulation (solid line).
=I 0
EJ
80/lOO
Mass Transport Parameters The homogeneous surface diffusion (HSD) version of MADAM, the Michigan Adsorption Design and Applications Model (I), was used to fit the MIDDAS-SBA data for mass transport parameter evaluation and for DBA predictions. This model is predicated on the concept that the adsorption mass transport process for microporous adsorbents is comprised by the following sequence of steps: diffusion from the bulk fluid phase to the outer surface of the adsorbent particle (film transfer); immediate sorption at the surface of the adsorbent; diffusion in the adsorbed state along the particle pore surfaces (intraparticle surface diffusion). The mass transport parameters associated with this “two-resistance”model include the external (film) transfer coefficient (kf)and the surface diffusion coefficient (D,). The MIDDAS-SBA procedure is employed for determining these mass transport parameters. The value of kfis obtained from the incipient SBA breakthrough curve by initially “guessing” its value and then changing it on a “trial and error” basis until a good fit to the initial plateau of the breakthrough curve obtains for a given initially guessed value of D,. Initial guesses for kfcan be based either upon experience with systems similar to those under consideration or upon estimates made with any one of a number of readily available correlation procedures (1). The parameter D, is then estimated by incrementally changing its initially guessed value until a satisfactory fit to the entire breakthrough curve is obtained. It should be noted that determination of kf by the procedure described is essentially independent of the initially guessed value for D, because the initial portion of the SBA breakthrough is insensitive to D,. Figure 4 presents a typical incipient breakthrough curve for PCP obtained with the MIDDAS-SBA technique. Physical conditions concerning the experimental rate parameter determinations for all four solutes tested are summarized in Table 11. Values of lzf and D,estimated by the MIDDAS-SBA technique are presented in Table 111. As indicated by the PCP simulation example given in Figure 4,the MADAM-HSD model calibrated well to the MIDDAS-SBA data.
16/2 0
8
t
%f,
-4
0.10
0.20
0.30
0.40
0.50
PARTICLE RADIUS ICMl IXlO’I
0.M
0.70
Figure 5. Correlatlon of the external mass-transfer coefficient and particle size (PCP; v , = 10.7 cm/s).
Particle Size and Polydispersity. Most commercial granular activated carbons are characterized by a distribution of particle sizes, generally all of which are larger than the sizes used in the MIDDAS technique (SO/lOO mesh size). Successful application of the technique for full-scale system simulation and prediction depends on the accurate characterization of the significant physical features of the full-scale system, particularly particle size and bed depth. Development of the MIDDAS procedure thus involved side by side comparisons of parameter values obtained with different ranges of particle sizes and degrees of polydispersity. Tables IV and V summarize the experimental conditions of a series of SBA measurements made with all four solutes and several classes of carbon particle size, along with the associated kf and D,values calculated from these measurements. An initial guess for a parameter was determined from the value of that parameter for a similar system if such was available. The D, value given in Table V for each solute has the same value for different carbon particle sizes because the initial guess for each (obtained from the D,value for 50/60 size GAC) was held constant on the presumption that intraparticle surface diffusivity is independent of particle size. This was confirmed by the fact that different values of D,(approximately &lo% from the initial guess) yielded no improvements in model predictions. The dependence of the external mass-transfer coefficient on particle size is illustrated graphically for PCP in Figure 5. When plotted against particle radius, the estimated Environ. Scl. Technol., Vol. 21, No. 11, 1987
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Table IV. Experimental Conditions Used To Estimate kr and D,for Different Particle Sizes in Single-Solute Systems"
"0,
run
solute
PC3 PC4 PC5 PC8 PN3 PN4 PN5 PN8 PT3 PT4 PT5
PCP PCP PCP PCP PNP PNP PNP PNP
PTS PTS
PT8
PTS PTS
DB3 DB4 DB5 DB8
DBS DBS DBS DBS
C,, mgIL
particle size classb
cm x io3
€
L, cm
100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 150.0 150.0 150.0 150.0 200.0 200.0 200.0 200.0
50160 30140 16/20 mixedc 50160 30140 16/20 mixed 50160 30140 16/20 mixed 50160 30140 16/20 mixed
13.75 25.63 51.00 27.70d 13.75 25.63 51.00 27.70 13.75 25.63 51.00 27.70 13.75 25.63 51.00 27.70
0.438 0.479 0.513 0.479 0.438 0.479 0.513 0.479 0.438 0.479 0.513 0.479 0.438 0.479 0.513 0.479
1.1 2.8 3.0 2.8 0.7 2.8 3.0 2.8 2.6 5.6 12.0 5.6 2.6 2.8 6.0 2.8
r1
= 10.7 cm/s. bU.S. Standard Sieve mesh. c16/20, 50% by wt; 30140, 30% by wt; and 50160, 20% by wt. dSurface-mean radius.
Table V. Single-Solute kr and D,Values Determined by the MIDDAS-SBA Technique for Different Particle Sizes run PC3 PC4 PC5 PC8 PN3 PN4 PN5 PN8 PT3 PT4 PT5 PT8 DB3 DB4 DB5 DB8
C,, solute mg/L PCP PCP PCP PCP PNP PNP PNP PNP PTS
PTS PTS PTS DBS DBS DBS DBS
particle kf? size class" cm/s x
100.0 100.0
100.0 100.0 100.0 100.0 100.0 100.0 150.0 160.0 150.0 150.0 200.0 200.0 200.0 200.0
50160 30140 16/20 mixedb 50160 30140 16/20 mixed 50160 30140 16/20 mixed 50160 30140 16/20 mixed
io3
4.5 4.0 2.6 3.9 4.8 4.1 2.6 3.9 2.5 2.1 1.3 2.0 1.41 1.21 0.80 1.14
D,,
cm2/s x
io9
15.0 15.0 15.0 15.0 6.0 6.0 6.0 6.0 3.0 3.0 3.0 3.0 0.65 0.65 0.65 0.65
Figure 6. MIDDAS-DBA breakthrough data (symbols) and MADAM simulation (solid lines) for the PCPIPNP system.
gl
"U.S. Standard Sieve mesh. b16/20, 50% by wt; 30/40,30% by wt; and 50160, 20% by wt.
kf values lie on a relatively straight line, suggesting a linear correlation. On the basis of this and similar findings for the other solutes studied, the following relationship between the film-transfer coefficients kf(r) and kf(r? for particles of radius r and r', respectively, was developed:
Until tested more broadly, use of the correlation given in eq 3 should be restricted to the particular carbon and experimental conditions associated with the measurements described. Figure 6 illustrates the MIDDAS-SBA breakthrough simulations used to estimate mass-transfer parameters for the solute pair PCP/PNP. While the estimated values of kf and D,for the PCP/PNP and PCP/PTS solute pairs are the same as those for the corresponding single solutes, D,for PCP in the PCP/DBS system (3.0 X 10-9cm2/s) was dramatically different from its single-solute value (15.0 X cmz/s). This suggests that slowly diffusing DBS molecules (D,= 0.65 X lo+ cm2/s) interfere with the diffusion of PCP molecules. A breakthrough curve for PCP in a MIDDAS-SBA system containing mixed particle sizes is given in Figure 7. The average particle radius used for model calibration 1100
Environ. Sci. Technol., Vol. 21, No. 11, 1987
Figure 7. MIDDAS-DBA breakthrough data (symbols) and MADAM simulation (solid line) for a mixed particle size system.
(solid line) was the "surface-mean" radius, estimated such that the total outer-particle surface area for the "averaged" particles is the same as that of the polydispersed particles. This seems a reasonable basis for averaging in that kf is determined from that stage of the breakthrough curve influenced predominantly by the total outer-particle surface area. The equation for estimation of surface-mean radius is (IO) F W(i) -W(t) - - NE(4) F r(z) where W(t) = total weight of the mixed particle size adsorbent, F = surface-mean radius, W(i)= weight of that fraction of adsorbent having a particle radius r(i),and NF = number of particle size fractions.
isotherm
hf/
l m
%al
Flgure 8. FBR breakthrough data (symbols) and MADAM predictions (solid lines) for PCP using isotherm parameters determined by the MIDDAS-DBA and CMBR methods (C, = 100 mg/L; 16/20 particle size: L = 27.0 cm).
T I t E IHR)
Figure Q. FBR breakthrough data (symbols) and MIDDAS-calibrated MADAM predictions (solid lines) for the PCP/PNP system (16120 particle size; L = 27.0 cm).
a dl
External mass-transfer coefficients for mixed particles were evaluated by the MIDDAS-SBA technique employing eq 4 for the average particle radius. As illustrated in the example given in Figure 5 for PCP, estimated kfvalues for mixed particles were found to agree well with the correlation developed from experiments with narrow ranges of particle size (eq 3). It is therefore concluded that use of the surface-mean radius in that correlation provides for reasonable estimates of kffor randomly distributed particles.
Sensitivity of Predictions to Isotherm Differences It was noted earlier that differences between CMBRand MIDDAS-DBA-evaluated isotherms were observed, the CMBR test generally giving somewhat higher apparent capacities (see Figure 1). As an example of how different capacity values can impact modeling predictions, Figure 8 compares experimental breakthrough data and MADAM-HSD-generated breakthrough curves based upon isotherm parameters determined separately by the CMBR and MIDDAS-DBA methods. Both model projections given in Figure 8 were made with the same set of mass transport parameters; i.e., kf = 2.6 X cm/s and D, = 15 x cm2/s. It is apparent that an overprediction of adsorber performance occurred when the CMBR capacity data were used, while the model calibrated with MIDDAS equilibrium parameter estimates provided good prediction of breakthrough behavior. The percentage of overprediction is approximately the same as the percentage difference between isotherms determined by the MIDDASDBA and CMBR methods. A difference in model projections for different equilibrium parameters and a fixed set of mass transport parameters is to be expected, given the sensitivity of models like MADAM-HSD to the former. The model can be recalibrated with a D, value more compatible with the CMBR equilibrium parameters (kfshould not be affected) to shift the CMBR-related prediction to the left in Figure 8. In other words, a capacity discrepancy can be “absorbed’ or compensated for by a different estimate for the intraparticle diffusion coefficient. While this would result in shifting the general location of the model prediction closer to the data set, it would also result in a different shape for the predicted curve; that is, the pattern of “dispersion’ of the predicted curve about the experimental data would be changed. Indeed, such adjustments were made in several modeling runs. The results invariably yielded poorer projections of actual behavior when CMBR equilibrium parameters and recalibrated D,values were employed.
TIDE IW) Flgure 10. FBR breakthrough data (symbols) and MIDDAS-calibrated MADAM predictions (solid lines) for the PCP/PNP system (mixed particle size; L = 25.2 cm).
Verification of MIDDAS for System Scale-up Figures 9 and 10 present FBR system experimental data and MADAM predictions for the PCP/PNP solute pair. The isotherms used for model calibration were determined by the MIDDAS-DBA technique and described by multicomponent isotherms employing the displacement-enhancement concept (eq 1and 2). The agreement between FBR operating data and model predictions shown by way of example for the PCP/PNP solute pair in Figures 9 and 10 is typical of that obtained for the other systems studied. Conclusions and Significance The MIDDAS technique appears to provide a relatively simple and accurate means for estimation of the isotherm and mass transport parameters required for predictive modeling of fixed-bed adsorber behavior. The validity of the method has been demonstrated by utilizing MIDDAS-estimated parameters in an appropriate scale-up procedure to generate predictions of FBR adsorber breakthrough curves for single-solute and bisolute systems for different conditions of flow, particle size, particle polydispersity, and bed depth. Some differences in the effective or operational adsorption capacities obtained by the MIDDAS technique and by more traditional completely mixed batch reactor (CMBR) methods were observed. Projections of the expected behavior of FBR systems were made with mass transport parameters from the MIDDAS-SBA technique and equilibrium capacity parameters from both the MIDDAS-DBA and CMBR methods. Better agreement between experimental breakthrough data and predicted curves was obtained with parameters from the MIDDASDBA technique, indicating that this procedure provides more representative estimates of fixed-bed or column operating capacities than do CMBR methods. Environ. Sci. Technol., Vol. 21, No. 11, 1987
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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-mean radius, 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
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