Environ. Sci. Technol. 1996, 30, 743-750
Studies on Contaminant Biodegradation in Slurry, Wafer, and Compacted Soil Tube Reactors CHUNSHENG FU, STEVEN PFANSTIEL, CHAO GAO, XUESHENG YAN, AND RAKESH GOVIND* Department of Chemical Engineering, University of Cincinnati, Cincinnati, Ohio 45221
HENRY H. TABAK National Risk Management Research Laboratory, U.S. Environmental Protection Agency, Cincinnati, Ohio 45268
A systematic experimental approach is presented to quantitatively evaluate biodegradation rates in intact soil systems. Knowledge of bioremediation rates in intact soil systems is important for evaluating the efficacy of in-situ biodegradation and approaches for enhancing degradation rates. The approach involves three types of soil bioreactors: slurry, wafer, and porous tube. In the soil slurry reactor, biodegradation occurs in the aqueous phase by suspended and soilimmobilized microorganisms. In the soil wafer reactor, diffusivity of contaminant in the soil matrix controls the biodegradation rate. In the porous tube reactor, oxygen limitations occur inside the tube due to diffusional resistances, and oxygen consumption occurs due to biodegradation. Measurement of cumulative oxygen uptake in soil slurry, wafer, and porous tube reactors are used to determine biokinetics and transport parameters. It is shown that biodegradation rates in intact soil systems are slower than in soil slurry reactors. Furthermore, soil tube reactors in conjunction with respirometry can be used to assess bioremediation rates in intact soil systems.
Introduction To successfully apply soil bioremediation technologies, it is necessary to gain a fundamental understanding of the factors that affect the rates of biodegradation in soils. There are many factors affecting the rates of biotransformation of organic compounds, including water concentration, temperature and pH, number and species of microorganisms present, concentration of substrate, presence of microbial toxicant and nutrients, and availability of electron acceptors. The majority of microorganisms are firmly attached to soil particles (1). As a result, nutrients, electron acceptors, such as oxygen, and contaminants must be brought to the microbes by advection or/and dispersion through water or gas in the soil matrix. * Author to whom all correspondence should be addressed.
0013-936X/96/0930-0743$12.00/0
1996 American Chemical Society
Information on the kinetics of biodegradation is extremely important because (1) it characterizes the concentration of the chemical remaining at any time; (2) it helps to predict the levels likely to be present at some future time; and (3) it allows assessment of whether the chemical will be eliminated before it is transported to a site where susceptible humans, animals, or plants may be exposed. Quantifying the kinetics of biodegradation in soils may help to determine if bioremediation strategies are applicable for different situations as well as to provide insights into how biodegradation rates can be enhanced to make these strategies more efficient and timely. Use of respirometric oxygen uptake methods have gained popularity for the determination of biodegradation kinetics in aqueous systems. Briefly, these studies measure the oxygen uptake of aerobic microorganisms during the degradation of chemicals and utilize this information to make conclusions regarding the rate of biodegradation (24). Laboratory studies involving soil slurry reactor systems for determining biodegradation kinetics of pollutants in soils have recently been reported in the literature (5, 6). Some research has been done with reactor systems designed to simulate actual soil sites in the laboratory. Different reactors of this type, often termed microcosms, vary in how closely they resemble actual soil-contaminated sites (1, 7-9). Several models have been proposed to describe the kinetics of biodegradation of various substrates in soil systems (10-14). Also, considerable research has been conducted in the area of modeling contaminant transport in soils and aquifers. These studies use finite element or finite difference models to simulate contaminant movement in porous media as described by the equations of motion and Darcy’s law (13). Other researchers (14) have added various reaction terms, such as the Monod equation, to the transport equations to better model the overall rate of bioremediation. Some attempts have been made to develop an all-encompassing model to describe the rate of biodegradation and movement of chemicals in soil systems, including such effects as convection/advection, dispersion, adsorption, and biodegradation (15-17). The aim of our research is to investigate the bioavailability of organics in compacted porous soil systems. The work presented in this paper is the development and experimental verification of a set of mathematical models depicting characteristics of diffusion, adsorption /desorption, and biodegradation of the selected contaminant in porous soil.
Model Development In order to obtain a complete set of models and model parameters, we divided our modeling work and experimental work into three parts (i.e., slurry, wafer, and porous tube as shown in Figure 1), respectively. For the modeling work, the first step was to establish a set of models for depicting the slurry reactor case (containing 20 g of soil, 250 mL of water, and an amount of the selected chemical). The second step was to establish a set of models for depicting the wafer case (containing 20 g of soil, 10 mL of water, and the same amount of chemical as the slurry case).
VOL. 30, NO. 3, 1996 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
743
FIGURE 1. Schematic of the soil slurry, wafer, and porous tube reactors.
The third step was to develop a set of models for the porous tube case (containing the same amount of soil, water, and chemical as the wafer case). Models for the slurry, wafer, and porous tube reactors are summarized in Table 1. Soil is assumed to consist of spherical porous particles with average porosity of soil particle, , as measured by BET, equal to 0.03. Due to radial symmetry, concentration of contaminant varies radially inside the soil particle, and equilibrium is assumed between the soil and aqueous phases inside the soil particle and at the outside surface. The experimental adsorption and desorption data were fitted to the Freundlich isotherm equation. In the soil slurry reactor, the liquid-phase mass transfer coefficient, kf, is used to model mass transfer from the soil particle surface to the bulk aqueous phase. Further, it is assumed that contaminant biodegradation occurs by the microorganisms suspended in the aqueous phase (Monod model parameters µwmax and Kw) and immobilized on the outside surface of the soil particles (Monod model parameters µsmax and Ks). Since the average pore diameter in the soil particle, as measured by nitrogen adsorption and desorption porosimetry (Micrometrics, ASAP 200), was 58 Å, it was assumed that there are no active microorganisms inside the pores of the soil particle. The soil wafer model equations are the same as for the soil slurry reactor, except the amount of water is significantly lower. Due to low water content in the soil wafer, the contaminant concentration in the aqueous phase between the soil particles is assumed to be uniform. In the porous tube reactor model, the soil in the tube is subdivided into 10 radially concentric layers. Due to contaminant biodegradation and consequent oxygen consumption, eq 8 in Table 1 models the radial oxygen profile inside the tube. The rate of contaminant degradation in both the aqueous and soil phases are described by a double Monod model. The slurry reactor data was analyzed using a mathematical model to derive the biokinetic parameters for the aqueous and soil phases. These parameters were then used to model the soil wafer and porous tube reactors. The porous tube reactor using compacted soil represents insitu bioremediation more closely than the soil slurry reactor.
Experimental Studies Electrolytic Respirometer Studies. A respirometer apparatus consists of a reaction flask securely connected to an electrolytic cell, which supplies oxygen to the flask. Each reaction and oxygen-generating flask is immersed in a constant temperature bath, which is covered with a lid to prevent exposure of the reaction flask contents to light. In our case, the reaction flask contained soil, an aqueous solution containing essential nutrients, and a compound (phenol) whose degradation characteristics were studied. As aerobic degradation occurs in the reaction flask, oxygen is consumed and carbon dioxide is produced. The elec-
744
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 30, NO. 3, 1996
trolytic respirometer automatically detects the depletion of oxygen in the flask and supplies oxygen accordingly. The amount of oxygen that is supplied is then automatically and accurately read and recorded. A computer reads this oxygen uptake data from the recorder unit every 15 min and prepares plots that display oxygen uptake as a function of time for the particular system being studied. The following summarizes the experimental procedures used for the respirometer experiments. Materials and Methods. A main phenol stock solution was made with a concentration of 2.5 g/L phenol in distilled deionized (DD) water. Using the main stock solution, 50200 mL of experimental stock solution was made with a phenol concentration of 500 mg/L or 1.0 g/L as needed and with nutrient concentrations as recommended by OECD (18). Also stock solution containing no phenol but with the same concentration of nutrients as in step 2 was made to be used for control flasks. All experiments were run at 25 °C, and the temperature was maintained by the constant temperature bath. The soil selected for this study was uncontaminated topsoil (Faywood silty clay loam, 12-20% slopes; Family: fine, mixed, mesic; Subgroup: Typic Hapludalfs; Order: Alfisols) (19) with 17.5-20% by weight soil moisture, 1060 (g/L) density, 0.415% total carbon content, 6.65 pH in DD water and 5.79 pH in 0.01 M CaCl2. The soil was air-dried and passed through a 2-mm sieve. The measured particle size distribution was as follows (µm, wt %): 0.98) was obtained between the experimental data points and the Freundlich isotherm equation. A linear isotherm was found to fit the bacterial adsorption/desorption data, and the isotherm parameter Kb was determined using linear regression. Estimation Procedure. Initial estimates for the model parameters were determined by approximate analysis of the model equations. The value of cumulative oxygen consumption at the start of plateau was used to estimate an initial estimate of the yield coefficient, Y. For example, using a value of 45 mg/L from Figure 3 (slurry case) and considering the initial phenol concentration to correspond to 70 mg/L COD, an initial estimate of Y was obtained as 0.35. Similar results can be obtained for the slurry case shown in Figure 4. The oxygen yield coefficient, Yo, was determined from the biomass yield parameter, Y, using the stoichiometric COD balance, i.e., Yo ) Y/2.383. The mass transfer coefficient, kf, was obtained from the following equation derived from analysis presented by Parvatiyar (24):
kf ) 0.32
[
t ) 0; for TM:
t)0 t ) 0, r ∈ R t ) 0, r ∈ Rt
|
∂St(r,t) ∂r
r)0
)0
t)0
t ) 0, r ∈ Rt
m
∑ [O(t ) - O
O(r,t) ) O0
)0
aqueous and soil phases, diffusivity of phenol in soil, etc. were determined by fitting the model equations to cumulative oxygen uptake data. The Crank-Nicholson scheme was used to approximate derivatives (in partial and ordinary differential equations). The method of backward differences (22) was used for numerical integration. Nonlinear equations were solved by a combination of bisection and Newton-Raphson methods (23). The extent of convergence between the model equations and experimental data was defined by the relative residue error (RRE), which is defined as follows:
RRE )
Cs(t) ) 0
]
Dp02/3 1/3 Ds1/2dp1/4 3/4 ν Re dp DT Do1/2HL1/4
(2)
r)0
)0
t)0
The slurry model equation (section (5) in Table 1) can be approximately written as follows:
[
]
xw VµwmaxCi WKbµsmaxSi ∆Ci(V + Wk) + )∆t Y Kw + Ci Ks + S i i ) 1, 2, 3, 4 (3) For purposes of obtaining initial estimates, equilibrium adsorption and desorption data were approximated by a linear isotherm, S ) kC, where C (mg/L) and S (mg/g) are solute concentrations in the aqueous and soil phases, respectively. The best-value of k was found to be 0.0366 (L/g). Using a yield coefficient value of Y ) 0.35 and xw ) 2 mg/L, as obtained from the control cumulative oxygen uptake curve, the initial estimates of biokinetic parameters were obtained using the approximate slurry model (eq 3). Results obtained were as follows: µwmax) 0.23 (1/h); µsmax) 0.32 (1/h); Kw) 1.99 (mg/L); and Ks ) 2.12 (mg/L). Similarly, using wafer rather than slurry experimental data, approximate values of Kw and Ks for the wafer and tube models (Table 1) were obtained as 30.9 (mg/L) and 44.8 (mg/L), respectively. Using the above initial estimates, experimental oxygen uptake data for the slurry reactor was used to converge on final estimates for the slurry reactor parameters. The relative residue error, defined in eq 1, was used to obtain the best-fit values for slurry reactor parameters: SS1 ) {µwmax, µsmax, Y, b}. These parameter values were then used in the wafer model equation to obtain the best-fit wafer model parameters: SS2 ) {Kw, Ks, Dp, 1/n ′}. The slurry and wafer model parameters, SS1 and SS2, were then used in the tube model equation to obtain the best-fit tube model parameters: SS3 ) {βw, βs, Kwo, Kso, Do, f}. The procedure of using one type of reactor model parameters to obtain other parameters is shown in Figure 2. Slurry, wafer, and tube reactors were operated with the following initial amounts of phenol: 2.5, 3.75, 7.5, and 10.0 mg. Cumulative oxygen uptake data obtained for initial phenol amounts of 2.5 and 10.0 mg were used to obtain the model parameters. Oxygen uptake data obtained for initial phenol amounts of 3.75 and 7.5 mg were used to test the model predictions, as shown in Figures 3 and 4. Initial parameter estimates, as discussed before, were used to converge on the best-fit values and to obtain results with physical significance. The mean and standard deviation values estimated for all parameters are shown in Table 2.
VOL. 30, NO. 3, 1996 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
747
TABLE 2
Parameters for Slurry, Wafer, and Porous Tube Models µwmax (1/h) µsmax (1/h) Y (mg/mg) b (1/h) Kw (mg/L) Ks (mg/L) kf (cm/h) Dp (cm2/h)
FIGURE 2. Parameter sets derived from the slurry, wafer, and tube oxygen uptake data using nonlinear regression techniques.
FIGURE 3. Cumulative oxygen uptake data and model prediction for slurry, wafer, and tube reactors at 7.5 mg of phenol added in each reactor.
FIGURE 4. Cumulative oxygen uptake data and model prediction for slurry, wafer, and tube reactors at 3.75 mg of phenol added in each reactor.
Results and Discussion Figures 3 and 4 show the oxygen uptake data for each of the experimental schemes for different initial phenol concentrations. The similarities and differences in the data for each of the reactor schemes are clearly shown by this figure. Clearly, the oxygen uptake curve for the slurry case reaches a higher plateau than the curves for the soil wafer or tube reactor, indicating that more of the phenol is actually being degraded in the slurry reactor. Although the wafer and tube curve are very similar, especially if the variation of the data for each scheme is taken into account, it is clear that the curve for the wafer case does reach a higher plateau than the curve for the tube case. The slurry reactor exhibits a high initial rate of degradation as compared to the wafer and tube reactors. Cumula-
748
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 30, NO. 3, 1996
Ka Kd Kb 1/n , t 1/n′ f Y0 βw βs Kw0 (mg/L) Kso (mg/L) D0 (cm2/h)
slurry
wafer
tube
0.228 ( 0.012 0.294 ( 0.024 0.342 ( 0.007 0.00077 ( 0.00002 1.99 ( 0.63 2.12 ( 1.04 10.9 ( 0.25 4.5 × 10-5 ( 6 × 10-6 0.0105 0.01259 0.167 0.84 0.03 0.77 1.0
same as slurry same as slurry same as slurry same as slurry 30.9 ( 0.98 44.8 ( 3.45
same as slurry same as slurry same as slurry same as slurry same as wafer same as wafer
3.3 × 10-5 ( 4 × 10-6 same as slurry same as slurry same as slurry same as slurry same as slurry 0.71 0.81
same as wafer same as slurry same as slurry same as slurry same as slurry 0.2 same as wafer 0.64 0.143 0.477 ( 0.031 0.404 ( 0.044 0.473 ( 0.049 0.749 ( 0.034 0.059
tive oxygen uptake in the slurry reactor attains the plateau value much sooner than the wafer and tube reactors. This is mainly attributed to mass transfer limitations in the wafer and tube reactors. The effect of pore size in the microporous tube reactor was studied by conducting separate experiments using vycor glass tubes with average pore size varying from 40 to 3200 nm. Results showed that the oxygen uptake was unaffected by pore size, which demonstrated that oxygen transfer was not limited by the microporous glass tube. Hence, 40 nm average pore size vycor glass tubes were used in all our experiments. Based on all the measured and estimated parameters listed in Table 2, model predictions were accomplished. The prediction results were compared with our experimental data, which was not used for the parameter estimation. As shown in Figures 3 and 4, all the slurry, the wafer, and the tube model prediction results for different initial phenol concentrations (Figure 3 for 7.5 mg of phenol and Figure 4 for 3.75 mg of phenol) fitted the related experimental data very well (RRE < 0.18). Experiments were also conducted with 14C-labeled phenol to verify that the net oxygen uptake was solely due to phenol degradation. Measurements of carbon dioxide evolved were quantified by adsorbing the carbon dioxide in each reactor flask head space in KOH solution, periodically withdrawing the KOH solution, and titrating it with standard HCl solution. For the radiolabeled phenol studies, 14C-labeled carbon dioxide was measured by scintillation counting of the KOH solution. The carbon dioxide solution in the radiolabeled phenol experiments closely agreed with the CO2 evolution in the unlabeled experiments, demonstrating that the carbon dioxide evolved and hence the net oxygen consumed were solely due to phenol degradation. Oxygen profiles in the packed tube were simulated, and Figure 5 shows the calculated dynamic profile. At the outside surface of the porous tube (r ) R), the oxygen concentration is at equilibrium with air. However, the oxygen concentration decreased rapidly inside the porous tube, and at 0.25 tube radius from the center (r ) 0.25R), the oxygen concentration is zero after 35 h. The decrease
parameters determined for soil slurry systems allows the estimation of reactor size and treatment cost for an ex-situ soil slurry bioreactor treatment scheme. Biokinetic parameters obtained for the soil wafer reactor represent soil treatment using land farming or bioventing approaches, wherein oxygen limitations are minimized. Finally, biokinetic parameters obtained for porous tube reactors allow biodegradation rates to be obtained for in-situ bioremediation. This allows estimation of treatment time and hence treatment cost.
Nomenclature FIGURE 5. Calculated oxygen profile in the porous tube as a function of time.
of oxygen concentration inside the tube was mainly attributed to oxygen consumption inside the soil due to the biodegradation of phenol. It should be noted that incomplete degradation of phenol was achieved in the wafer and porous tube reactors. The extent of degradation is defined by the parameter f, which is given by f ) 1 - (St)plateau/Sto, where (St)plateau is the total concentration of phenol left in the reactor when oxygen uptake has reached a plateau value, and Sto is the initial total phenol concentration in the reactor. For the slurry reactor, there is no phenol left in the reactor after oxygen uptake has reached a plateau value, which gives a value of 1.0 for the parameter f. In the wafer reactor, the value of parameter f is 0.81, which means that 81% of the initial phenol added is biodegraded in the reactor. Only 64% of phenol is biodegraded in the tube reactor. Incomplete degradation in the wafer and tube reactor is attributed to lack of bioavailability of phenol to the microorganisms present in the soil and aqueous phase. Phenol adsorbed in the soil micropores remains inaccessible to the microbiota. In the slurry reactor, phenol is able to desorb from the soil surface and become bioavailable to either the soilimmobilized or suspended microorganisms. In the tube reactor, phenol present in the inner core is unable to biodegrade due to lack of oxygen.
Conclusions A three-step experimental protocol for determining the important kinetics parameters for the in-situ biodegradation of toxic chemicals in soil systems was developed using phenol as the test compound. The protocol was developed so that the experimental schemes that were used grew in complexity toward the actual in-situ case, but remained simple enough to allow them to be adequately modeled. The data gained for each of the schemes agreed with expectations. Both the rate and the extent of biodegradation of phenol were found to decrease with the increase in the complexity of the soil systems in the experimental schemes. Modeling procedures applied to the three experimental schemes proved useful for determining the biokinetic parameters for the degradation of phenol. Model predictions were found to agree very well with experimental data. The application of this protocol to other chemicals should be feasible with only minor alterations in the methodology presented in this paper. The protocol presented in this paper can be applied to contaminated soils from actual field sites. The biokinetic
Ap b C Cexp Cb Cb0 Cs Do dp Dp Dp0 Ds DT f HL Ka Kb Kd kf Ks Kso Kw Kwo L m N 1/n 1/n ′ O O0 Oexp(ti) Ou q qt r Redp
external surface area of the soil particle (cm2) decay coefficient of the cells (1/h) liquid concentration of solute in the pore soil particle (mg/L) experiment data at time ti (mg/L) bulk liquid concentration of solute (mg/L) initial bulk liquid concentration of solute (mg/ L) liquid concentration of solute at the soil particle surface (mg/L) diffusivity of the oxygen (cm2/h) soil particle diameter (cm) pore diffusion coefficient (cm2/h) diffusivity of the solute in water (cm2/s) impeller diameter (cm) reactor diameter (cm) fraction of total phenol biodegraded in the reactor height of the liquid in the reactor (cm) coefficient of adsorption isotherm partition coefficient for the cells (L/g) coefficient of desorption isotherm mass transfer coefficient for external (film) diffusion (cm/h) saturation constant for solid phase (mg/g) oxygen saturation constant for solid phase (mg/ L) saturation constant for liquid phase (mg/L) oxygen saturation constant for liquid phase (mg/ L) length of porous tube reactor (cm) number of a batch of experimental data speed of the stirring bar (1/s) exponent of adsorption isotherm exponent of desorption isotherm oxygen uptake concentration (mg/L) oxygen concentration in the air (mg/L) experimental cumulative oxygen uptake at time ti (mg/L) calculated oxygen uptake (mg/L) average solute concentration of the soil particle (mg/g) average solute concentration of the compacted soil in the tube (mg/g) radial distance (cm) Reynolds number [)(DsdpN)/ν]
VOL. 30, NO. 3, 1996 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
749
ri R Rt S St Sti Sto (St)plateau t V Vp Vi Vt VW W Wi xs xs0 xt xt0 xti
xw xw0 Y Yo
radial distance for the ith layer of soil particle or compacted soil in the tube (cm) radial size of the particle (cm) radial size of the porous tube (cm) solid concentration of solute in the pore soil particle (mg/g) total concentration of solute (mg/L) total concentration of solute for the ith layer of the compacted soil in the tube (mg/L) initial total concentration of solute (mg/L) total concentration of phenol left in reactor when oxygen uptake has reached a plateau (mg/L) time (h) volume of the solution in reactor (L) volume of the particle (cm3) ith layer’s volume of the soil particle or compacted soil in the tube (cm3) total volume of the content of the tube (L) total volume of soil wafer in wafer reactor (L) weight of the soil in the slurry or wafer or tube reactor (g) ith layer’s weight of the compacted soil in the tube (g) concentration of the cells in the solid phase (mg/ g) initial concentration of the cells in the solid phase (mg/g) total concentration of the cells (mg/L) initial total concentration of the cells (mg/L) total concentration of the cells for the ith layer of the compacted soil in the solid phase in the tube (mg/L) concentration of the cells in the liquid phase (mg/L) initial concentration of the cells in the liquid phase (mg/L) yield coefficient for the cells vs substrate (mg/ mg) yield coefficient for the cells vs oxygen (mg/mg)
Greek Letters βs oxygen limitation coefficient for the solid phase oxygen limitation coefficient for the liquid phase βw substrate biodegradation rate (mg L-1 h-1) γs oxygen consumption rate for the ith layer of the γoi compacted soil in the tube (mg L-1 h-1) substrate biodegradation rate for the ith layer of γsi the compacted soil in the tube (mg L-1 h-1) cell growth rate (mg L-1 h-1) γx cell growth rate for the ith layer of the compacted γxi soil in the tube (mg L-1 h-1) porosity of adsorbent porosity for the compacted soil in the tube t maximum specific growth rate of the cells in µwmax liquid phase (1/h)
750
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 30, NO. 3, 1996
µsmax ν F
maximum specific growth rate of the cells in solid phase (1/h) kinematic viscosity (cm2/s) density of adsorbent (g/L)
Literature Cited (1) Wilson, B. H.; Smith, G. B.; Rees, J. F. Environ. Sci. Toxicol. 1986, 20, 997-1002. (2) Tabak, H. H.; Govind, R. Determining of biodegradation kinetics with use of respirometry for development of predictive structurebiodegradation relationship models. Presented at the 4th International IGT Symposium, Colorado Springs, CO, December 1991. (3) Tabak, H. H.; Gao, C.; Desai, S.; Govind, R. Water Sci. Technol. 1992, 26, 763-772. (4) Graves, D. A.; Lang, C. A.; Leavitt, M. E. Appl. Biochem. Biotechnol. 1991, 28/29, 813-826. (5) Bachmann, A.; Walet, P.; Wunen, J. L.; Huntiens, M. R.; Zehnder, A. J. B. Appl. Environ. Microbiol. 1988, 54, 143-149. (6) Mihelcic, J. R.; Luthy, R. G. Appl. Environ. Microbiol. 1988, 54, 1188-1198. (7) Barrio-Lange, G. A.; Parsons, F. Z.; Nasar, R. S.; Lorenzo, P. A. Environ. Toxicol. Chem. 1987, 6, 571-578. (8) Chang, F. ASM Abstracts; ASM 85th Annual Meeting, Las Vegas, NV, March 3-7, 1985; ASM: Materials Park, OH, 1985; p 266, Q-50. (9) Michaels, G. B.; Laplante, J. P.; Schneck, D. J. ASM Abstracts; ASM 88th Annual Meeting, Miami Beach, FL, May 8-13, 1988; ASM: Materials Park, OH, 1988; p 305. (10) Alexander, M.; Scow, K. M. Reactions and Movement of Organic Chemicals in Soils. SSSA Spec. Publ. 1989, 22, 243-269. (11) Scow, K. M.; Schmidt, S. K.; Alexander, M. Soil Biol. Biochem. 1989, 22, 703-708. (12) Bailey, T. J.; Ollis, D. Biochemical Engineering, 2nd ed.; McGrawHill, New York, 1987. (13) Kueper, B. H.; Frind, E. O. Water Resour. Res. 1991, 27, 10491070. (14) Kosson, D. S.; Agnihotri, G. C.; Ahlert, R. C. J. Hazardous Mater. 1987, 14, 191-211. (15) Yates, M. V.; Yates, S. R. ASM News 1990, 56, 324-327. (16) Corapcloglu, M. Y.; Hossan, M. A. J. Theor. Biol. 1990, 142, 503516. (17) Yong, R. N.; Mohamed A. M. O.; Warkentin, B. P. Principles of contaminant transport in soils; Elsevier Science Publications: Amsterdam, 1992. (18) OECD. OECD guidelines for testing of chemicals section 3, degradation and accumulation, method 301C, ready biodegradability: modified MITI test (I) adopted May 12, 1981 and method 302C inhernt biodegradability: modified MITI test (II) adopted May 12, 1981; Director of Information, Organization for Economic Cooperation and Development: Paris, France, 1981. (19) Weisenberger, B. C.; Dowell, C. W.; Leathers, T. R.; Odor, H. B.; Richardson, A. J. Soil Survey of Boone, Campbell, and Kenton Counties, Kentucky; U.S. Government Printing Office: Washington, DC, 1989. (20) Day, P. R. Particle fractionation and particle-size analysis. In Methods of Soil Analysis; Black, C. A., Ed.; American Society of Agronomy Inc.: Madison, WI, 1965; Chapter 43. (21) Govind, R.; Gao, C.; Lai, L.; Yan, X.; Pfanstiel, S.; Tabak, H. H. Paper presented at the In-Situ and On-Site Bioreclamation, 2nd International Symposium, San Diego, CA, April 5-8, 1993. (22) Henrici,P. Elements of Numerical Analysis; John Wiley & Sons: New York, 1964. (23) William, H. P.; Brain, P. F.; Saul, A. T.; William, T. V. Numerical Recipes; Cambridge University Press: Cambridge, 1986. (24) Parvatiyar, M. G. Interfacial Heat and Mass Transfer in Newtonian and Non-Newtonian Fluids. Ph.D. Thesis, University of Cincinnati, 1992.
Received for review September 19, 1994. Revised manuscript received October 17, 1995. Accepted October 18, 1995.X ES940581Q X
Abstract published in Advance ACS Abstracts, January 1, 1996.
