The dynamic behavior of trivalent chromium (Cr(lll)) in the activated sludge process .... was daily analyzed for MLSS, mixed liquor volatile suspended solids (ML ...
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Waf. Sci. Tech. Vol. 23. Kyoto, pp. 1047-1056. 1991. Printed in Great Britain. All rights reserved.
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DYNAMIC BEHAVIOR OF Cr(Ill) IN ACTIVATED SLUDGE Akio Imai* and Earnest F. Gloyna** *
National Institute for Environmental Studies,
305 Japan * *
Department of Civil Engineering,
Austin,
TX 78712,
·50
Copyright © 1990 IAWPRC
The
16-2 Onogawa,
University of
Tsukuba,
Texas at Austin,
USA
ABSTRACT
The dynamic behavior of trivalent chromium (Cr(lll)) in the activated sludge process receiving a square-wave input of Cr(Ill) was studied. A laboratory-scale activated sludge reactor was operated under steady-state conditions with respect to the process parameters at pH 7 and a sludge age of 5 days. The CRAS model, an quasi-equilibrium model developed by Imai (1988), was further extended to predict the behavior of Cr(lll) as a function of operational time. A comparison between the experimental results and the model predictions revealed that the CRAS model was capable of predicting quantitatively the total chromium (TCr) and dissolved chromium (DCr) concentrations when the TCr concentration was on the increase (average relative error: 5.46% for DCr and 7.45% for TCr), but underestimated the TCr and overestimated the DCr as the TCr was on the decrease. Intracellularly transported Cr(lll) seemed to be trapped inside the cell and not released back into solution. Iron(III) coprecipitation treatment indicated that about 35.6% of dissolved Cr(lll) was organically complexed, and the CRAS model predicted 35.3%. This agreement supported the validity of the CRAS model for Cr(IlI) speciation in the solution phase. KEYWORDS
Activated sludge; chromium, Cr(lll), adsorption, complexation, intracellular uptake; equilibrium model. INTRODUCTION
Early research involving heavy metals in wastewaters has emphasized the toxic and inhibitory effects of heavy metals on biological oxidation of organics in activated sludge treatment plants. Historically, there has been little concern about the presence of heavy metals as long as their concentrations remained at sub-toxic levels. Today, with increasingly stringent effluent regulations, it becomes necessary to evaluate (1) potential adverse effects of heavy metals discharged into aquatic ecosystems and (2) limitations on the disposal of metal-laden wasted sludge. These two concerns eventually demand that activated sludge treatment plants should be regarded as a logical focal point for controlling the transport of heavy metals to the
1047
1048
A. IMAI and E. F. GLOYNA
environment (Patterson and Kodukula,1984; Nelson et al.,1981). The fate of heavy metals within the treatment plants should be quantitatively evaluated to determine what mass of heavy metals is discharged into receiving waters or disposed on land. Imai (1988) developed a mathematical model (CRAS) to evaluate the behavior of trivalent chromium (Cr(IIl» in the activated sludge process. The model was based on three concepts: chemical equilibria; steady state; and mass balance. Conditional stability constants were introduced to describe the chemical reactions associated with Cr(lll): adsorption, soluble organic complexation, and intracellular uptake. The objective of the model was to predict the variables such as total Cr(IlI) concentration in the mixed liquor (TCr) and the percentage removal of Cr(Ill) under steady-state conditions with respect to Cr(III), mixed liquor suspended solids (MLSS), and filtered organic carbon (FfOC). The model was found capable of satisfactorily predicting the behavior of Cr(III). The objective of this study was to evaluate the applicability of the CRAS model for the dynamic behavior of Cr(IlI) under conditions where the activated sludge reactor received a square-wave input of Cr(lII). The behavior under both increasing and decreasing TCr concentration was simulated as a function of operational time. The experimental phase of this study was limited to the operation of a laboratory-scale, continuous activated sludge reactor at a fixed pH and sludge age (mean cell residence time: 8c.) MATERIALS AND METHODS
A 5-1 capacity, plexiglass, activated sludge reactor was continuously operated under conditions of pH 7, 8c = 5 d, and hydraulic retention time (8h)= 0.333 d. The reactor was maintained at a steady state regarding MLSS and FfOC. Compressed air was supplied through air diffusers maintaining 5 to 7 mgl-l of dissolved oxygen and completely mixed conditions. A synthetic substrate was added continuously to the aeration basin via a peristaltic pump. The substrate consisted of Bacto-peptone 365 mgl-I(150 mgl-l as TOC); NH4CI 27.2 mgl-l as N; K 2HP04 2.7 mgl-l as P; yeast extract 9.4 mg}-l; CaCh 5.0 mg}-l; MgS0407H 2 0 50 mg}-l; FeC13 0.2 mgl-l; and tap water as the rest of 1 liter. Cr(Ill) feed solution, containing Cr(N03)3 and deionized distilled water, was fed into the reactor through a high-precision piston pump. The pH in the reactor was controlled to 7 within ± 0.1 pH units by an automatic pH controller with an addition of 0.5 M NaOH. The temperature in the reactor varied from 19.4 to 22.0 dc. The 8c was achieved by wasting excess activated sludge on a daily basis. Seed activated sludge was obtained from the Walnut Creek Wastewater Treatment Plant in Austin, Tx. The seed sludge was acclimated to the Synthetic substrate and pH 7 for about 40 d.
Reactor system.
A square-wave input of Cr(III) was simulated as follows: (1) adding continuously the Cr(III) stock solution, which provided 0.5 mgl-l of the influent Cr(Ill) concentration until the TCr and DCr concentrations reached a steady state; (2) stopping the Cr(IlI) dosage; and (3) operating the reactor for 9 more d. The mixed liquor was daily analyzed for MLSS, mixed liquor volatile suspended solids (ML VSS), TCr, and total dissolved Cr(III) (DCr). The effluent was analyzed for suspended solids (SS), TCr, DCr, and FfOC.
Experimental procedure.
In order to evaluate Cr(III) speciation in the solution phase, the filtered effluent, sampled at the end of the Cr(III)-fed period (Day 11), was subjected to iron(III) coprecipitation treatment (Nakayama et ai., 1981). 100 ml of the sample was spiked with 10 mg of Fe(III) (Fe(N03)3), shaken at 20°C for 10 h, filtrated through a 0.45-llm membrane, and analyzed
1049
Dynamic behavior of Cr(III)
for Cr(IIl). Blanks, containing 0.2 mg }-l Cr(IlI) and 0.0 13 M NaN03 in distilled deionized water, were tested in the same way. This treatment made FHCr coprecipitate in the pH range from 5.5 to 9.5. The DCr remaining in solution after the treatment was considered to be organically complexed Cr(III). Dissolved constituents such as DCr and FTOC were defined as those that passed a 0.45-�m membrane filter. TCr samples were prepared by an open digestion method with concentrated HN03. The concentrations of TCr and DCr were determined by either flame (Perkin-Elmer Co. Model 303) or flameless atomic absorption spectroscopy (Perkin-Elmer Co. Model 5000) at 357.9-�m wavelength. FTOC measurements were made with a Beckman Model 915-B carbon analyzer. All pH measurements were done using an Orion Research ionanalyzer. MLSS, MLVSS, and SS analyses were made according to Standard Methods (APHA, 1980).
Analytical methods.
Samples for the measurements of TCr and DCr were transferred to polyethylene bottles, acidified to pH lower than 2 with HN03, and stored at 4°C. FTOC samples were transferred to borosilicate glass test tubes, acidified to pH lower than 2 with HCl, and stored at 4°C. High-purity deionized water from a Barnstead Water Purification System was used throughout. Certified atomic absorption standard solution (K 2CQ07, Fisher Scientific Co.) was used to prepare standard solution for chromium analyses. The Cr(lll) stock solution was prepared with Cr(N03b·9H20 (reagent grade, Fisher Scientific Co.). Laboratory glass and plasticware were cleaned by soaking in 6M HN03 for 24 to 48 h. eRAS MODEL
The CRAS model combined an equilibrium distribution model with mass balances on both Cr(lll) and activated sludge to predict the time-invariant (steady-state) concentrations of Cr(lll) species. The equilibrium model is, strictly speaking, a quasi-equilibrium model since the activated sludge process is clearly an open system (Stumm and Morgan, 1981). Equilibrium concepts were applied as a tool for simplification. Detailed description and associated assumptions of the model can be found elsewhere (Imai,1988). In this paper, the CRAS model is used without a condition of steady state with respect to Cr(lll). Thus, it is assumed that all the chemical reactions involving Cr(lll) proceed so fast that Cr(lll) can achieve a state of quasi-equilibrium instantaneously (Morgan and Stone, 1985). The distribution of Cr(Ill) in the mixed liquor is considered to be controlled by competitive equilibria among four chemical reactions: hydrolysis; soluble organic complexation; adsorption; intracellular uptake. The oxidation of Cr(III) into hexavalent chromium, Cr(VI), is neglected. This reaction was found not to proceed in the activated sludge process (Imai, 1988). Precipitation as Cr(OH)3(s) is not included. The batch adsorption experiments (Imai 1988) suggested that Cr(Ill) adsorption predominates over the precipitation under the conditions of this study. Cr(Ill) was found to form a 1:2 organic complex with soluble organics (represented by FTOC) in the activated sludge reactor regardless of pH and e c (Imai, 1988).
Equilibrium model.
TCr
=
=
DCr
[FRCr] + [CrL2] + [CrSad] + [CrSin] [FRCr] + Ke [FHCr][Lt - CrL2]2Fe 2 + Kad[FHCr] [FRCr] + [CrL2] 2[FHCr]KeLt Fe 2+ 1-(4[FHCr]KeLtFe 2 + 1) 1/2 2[FRCr]KeFe2
JWST 23-4/6-GG
X X + Kin [FHCr].r;- (l) ]; (2) (3)
1050
A. lMAl and E. F. GLOYNA
in which: FHCr = free-and-hydrolyzed Cr(lll) (=[Cr3+]+[CrOH2+]+[Cr(OH)1 ]+[Cr(OH)4]) [mgl-l]; CrL2 = organically complexed Cr(III) (1:2 complex) [mgl-l]; CrSad = Cr(lll) adsorbed on activated sludge [mgl-l]; CrSin = Cr(lll) transported intracellularly [mgl-l]; Ke, Kad, Kin = conditional stability constants for complexation, adsorption, and intracellular uptake, respectively;Lt= complexation capacity [mgl-l as Cr]; Fe = mass of flOC complexed with Cr(lll) per unit mass of Cr(III) complexed (Lt Fe = flOC) [-];X = MLVSS [mgl-lUx VSS/SS ratio [-]. =
Mass balance.
A mass balance on Cr(lll) around the reactor can be performed as follows:
d (TCr) dt d (TCr) Oh dt V
= Q TCro - (Q-Qw )TCre - Qw TCr
°hX = TCro - A- DCr- (Kad+Kin) [FRCr] Oe!x X d (1 +Ke [Lt-CrL 2]2 F e2 +(Kad+Kin).r;-)[FRCr] Oh dt = TCro - A- (1 + Ke [ Lt-CrL2]2 Fe2 + (Kad+Kin) d [FHCr] dt
A B C
=
(1-
=
1 8h (TCro-A-B)/C
(Jh X )[FHCr] (Je!x (4)
7; )( C;� e_C;S )Xe
2[FHCr]KeLtFe2+1-(4[FHCr]KeLtFe2+1)1/2 Oh X )[FHCr]-+, 2[FRCr]KeFe2 (Je!x 2KeLtFe2[FHCr]+1 X 1 = 1+(Kad+Kin + ; 2 2 2 2KeFe [FHCr] 2KeFe [FHCr]2(4KeLtFe2[FHCr]+1)1I2 = (1+(Kad+Kin)
t
in which: V = reactor volume [1];Q = Influent flowrate [ld-l];Qw = wastage flowrate [ld-l]; TCro = Influent Cr(lll) concentration [mgl-l]; TCre = effluent TCr concentration [mgI-l]; CrS = Cr(III) associated with solids in mixed liquor [mgl-l]; CrSe = Cr(lll) associated with solids in effluent [mgl-I];Xe = suspended solids in effluent [mgl-I]. The solid-bound Cr(III) in the effluent was found to be considerably greater than the solid bound Cr(lll) in the mixed liquor (Patterson and Kodukula, 1984; Imai, 1988). The difference in solid-bound Cr(Ill) between the mixed liquor and the effluent (equivalent with the term nAn) needed to be incorporated into the model (Imai, 1988). The CrSe/Xe was found difficult to express as a function of DCr. The term nAn for a specific day was obtained from experimental results and used in the model calculation. Equation 4 is a first-order ordinary differential equation. The FHCr concentration can be numerically calculated as a function of the operational time given the initial values of FRer and time. The 4th-order Runge-Kutta method was used with a 0.25 d of step-size. The initial values of FHCr were calculated through Eqs. I to 3 by the Newton-Raphson method using the measured TCr concentration. Two initial values of FHCr were employed: (1) the value after one day of operation with Cr(III) addition; and (2) the value for the final day (Day 11) of operation with Cr(I1I) addition (Table 1). The former was used to predict the behavior of Cr(III) during the Cr(III)-fed period, and the latter was used during the no Cr(III)-fed period.
1051
Dynamic behavior of Cr(Ill)
Inputs for CRAS model. Input values for the CRAS model are presented in Table 2. The conditional stability constants, Kad, Kin, Kc, and the conversion factor, F c, were experimentally determined (Imai, 1988). The average values of MLSS and FrOC observed during the Cr(II1)-fed and no Cr(II1)-fed periods, respectively,were employed as inputs. Table 1.
Input data for CRAS model
Ke xl02
Fe
(l2mg-2 )
( -) Day
0
to Day 11
1.502 1.44
11.40
140
9.02
Day
II
14.2 to Day 20
1.714
Table 2. Day of Operation Day
1
Day 11 *
Initial values for simulation
MLSS
TCr
(mgl-l)
(gl-l)
1.555 1.605
10.6
FHCr* (mgl-1)
10.2
1.056
0.047
13.0
2.984
0.134
The FHCr values were obtained through numerical calculation.
RESULTS AND DISCUSSION
The average MLSS and FTOC were: 1502 mgl-I (standard Process Performance. deviation, sd=86 mg]-l) and 14.2 mgl-1 (sd=2.0 mg]-l) during the Cr(III)-fed period; and 1714 mgl-I (sd=86 mg]-l) and 10.6 mgl-I (sd=1.5 mgl-I) during the no Cr(III)-fed period. The activated sludge reactor worked well as far as its main objective, the removal of organics was concerned. The performance data before the initiation of Cr(III) addition resulted in 1523 mgl-I (sd=110 mgl-I) and 9.1 mgl-I (sd=1.4 mgl-I) as the average MLSS and FrOC, respectively. With these average values, the average MLSS and FTOC after the initiation of Cr(IIl) addition were tested statistically for a null hypothesis HO: there is no difference before and after the initiation of Cr(lll) addition. The null Hypothesis HO was rejected for the FrOC during the Cr(III)-fed period and the MLSS during the no Cr(III)-fed period. There might have been a slight change in biological activity of the activated sludge process after Cr(IlI) entered the process. Comparison between experimental data and model predictions. The CRAS model was tested for the distribution of Cr(lll) between the solution and solid phases, that is, the TCr and DCr concentrations which were only directly measurable variables. The predicted Cr(lll) speciation in solution was also evaluated in comparison with the results obtained through the iron(III) coprecipitation treatment.
CraIl) distribution between the solution and solid phases. The experimental results and model predictions on TCr and DCr are plotted along with the operation time in Figs. 1 and 2, respectively. The CRAS model satisfactorily predicted the TCr and DCr concentrations during the Cr(III)-fed period. The average relative error was 5.46% (sd=3.28%) for DCr
A. IMAI and E. F. GLOYNA
1052
and 7.45% (sd=2.76%) for TCr. The distribution of Cr(ll) between the solution and solid phases was well approximated as the equilibrium distribution .. As assumed in the CRAS model, the characteristic times for the chemical reactions involving Cr(lll) may be much shorter as compared with the characteristic time of mixing in the process, that is, (Jh =8 h.
4
NO Cr(lll) FEED
Cr(lII) FEED
o
3
Predicted
::r
�
Observed
2
�
o
2
4
6
8
10
12
14
16
18
20
Operation time [d] Fig. 1.
Comparison between experimental data and model predictions: TCr.
. 0.3 ....----------,---------,
NO Cr(lII) FEED
Cr(lII) FEED
o
Observed OCr Predicted
o
2
4
6
8
10
12
14
16
18
20
Operation time [d] Fig. 2.
Comparison between experimental data and model predictions: DCr.
However, during the no Cr(III)-fed period where the TCr was decreased, the model predictions deviated significantly from the experimental results. The model underestimated the TCr and overestimated the DCr. These deviations indicate that much less Cr(lll) associated with solids came out into the solution phase than predicted. An isotherm developed from the experimental data clearly indicates the existence of hysteresis (data not shown).
1053
Dynamic behavior of Cr(lll)
Chemical reactions involving the release of solid-bound Cr(III) may be much slower as compared with the adsorption and intracellular uptake of Cr(IIl). Solid-bound Cr(Ill) is considered to consist of adsorbed Cr(lll) outside the cell and intracellular Cr(Ill) inside the cell. Based on this concept, to further examine the observed deviation during the no Cr(III)-fed period, two limiting cases were tested: Case l ---intracellular Cr(lll) is not released into the solution phase; and Case 2---both intracellular and adsorbed Cr(III) (that is, all Cr(Ill) associated with solids) are not released. Case 1 involved the replacement of the term Kad +Kin with Kad for Eqs. 1 and 4; and the calculation for intracellular Cr(Ill), CrSin, by the following equation: CrSin
=
CrSinll exp
11.0-t
�)
(5)
in which: CrSin11 intracellular Cr(Ill) calculated at Day 11 [mgl-1 J. Case 2 was carried out by the following three simple equations: =
TCr CrS DCr
= CrS + DCr 11.0-t CrSll exp �)
(6)
11.0-t ---e; );-
(7)
=
=
DCn 1 exp(
in which: CrSll Cr(Ill) associated with solids observed at Day 11 lmgl-l]; DCrll observed at Day 11 [mgI-ll. =
=
DCr
Calculated results for these two cases along with the corresponding experimental results are shown in Figs. 3 and 4. Clearly, both Case 1 and 2 predictions better described the experimental results for both the TCr and DCr concentrations as compared to the previous predictions (Figs. 1 and 2). The Case 1 prediction with respect to TCr provided a better fit with the experimental results rather than Case 2. For the DCr concentration, Case 1, qualitatively, adequately predicted the experimental observation but, quantitatively, slightly overestimated it. Calculations according to Case 2 indicated that no DCr existed in the reactor after Day 13 (detection limit'" 1 f.1g 1.1 at 99% confidence level). Since a DCr concentration greater than the detection limit was experimentally observed after Day 13, Case 2 was rejected. Therefore, it can be assumed that a portion of Cr(Ill) associated with solids, either intracellular or adsorbed or both, did dissolve into solution. The Case 1 predictions for DCr were greater than the observed. Thus, solid-bound Cr(Ill), quantities greater than intracellular Cr(III), was assumed to remain in the solid phase. Which form of solid-bound Cr(Ill) was released into solution, either adsorbed Cr(lII) or intracellular Cr(lll) or both, was indeterminable. However, it was most likely that adsorbed Cr(lll) was desorbed into solution and intracellular Cr(IlI) remained inside the cell without being released. Williams (1981) indicated that the biochemistry of metal ions inside the cell is made extremely complicated by the existence of different compartments. The compartments are not at equilibrium. Sigg (1987) suggested that metals may not be bound in a reversible way inside the cell. Metal ions may become trapped in inactive forms as a detoxication mechanism. Paton and Budd (1972), in a study on zinc uptake by mycelium of Neocosmospora vasinfecta, defined the intracellular uptake as non-desorbable uptake. Since the observed DCr concentration was lower than the Case 1 prediction, it can be said that even adsorbed Cr(III) may not be desorbed in a completely reversible way as predicted. The
1054
A. IMAI and E. F. GLOYNA
o Observed TCr
3.0
2. 0
1.0
11
12
13
14
15
16
17
18
19
20
Operation Time [d] Fig. 3. Comparison between experimental data and model predictions with simplified assumptions: TCr.
o
0 . 20
Observed OCr
:i C,
oS U
0 . 10
o
1 1
12
13
14
15
16
17
18
19
20
Operation time [d] Fig. 4. Comparison between experimental data and model predictions with simplified assumptions: DCr.
desorption of Cr(lll) may not be fast enough to achieve an instantaneous equilibrium under the conditions of this study. Hysteretic (irreversible) isotherms were frequently observed in adsorption-desorption studies on organic compounds. For the sorption of a hexachlorophenyl to various sorbents (sediment, clay, and silica), Di Toro and Horzempa (1982) found that the adsorption process was rapid, the desorption process was slow because a significant portion of the sorbed chemical was more strongly adsorbed. They proposed a two-component model for the phenomenon that the adsorbed fractions may be comprised of both reversibly (labile) and strongly bound or resistant (non-labile) components. Karickhoff and Morris (1985) applied a kinetic expression to describe the nonlabile sorption-desorption process. In an analogous way, the adsorption-desorption of Cr(lll) on activated sludge (either cell surface or extracellular polymers or both) may be interpreted. A transformation of reversible binding sites (labile) to strong binding sites (nonlabi1e) might occur in the surface components of activated sludge. The comparison discussed above points out that Cr(lll) intracellular uptake may be an irreversible reaction. The CRAS model should be modified to incorporate a kinetic
1 055
Dynamic behavior of Cr(Ill)
expression for the release reaction of the intracellular uptake or simply ignore the release reaction, similar to Case 1, because the reaction may be extremely slow. Cr(Ill) adsorption may also require consideration in a two-component system: labile and non-labile. Consequently, in cases where more complicated dynamic Cr(IlI) behavior requires quantification, a kinetic approach may be more meaningful as compared to the CRAS model, that is, an equilibrium approach in predicting the behavior of Cr(Ill). Cr(Ill) speciation in solution. The predicted FHCr and CrL2 concentrations by the CRAS model are plotted along with the operation time during the Cr(III)-fed period in Fig. 2. When the TCr concentration was low, the CrL2 predominated over the FHCr in the solution phase. Then with increasing TCr, the FHCr became greater than the CrL2 which remained relatively constant. This reverse in predominant Cr(IlI) species was caused by difference in capacity between Cr(Ill) adsorption and intracellular uptake and Cr(Ill) organic complexation. The capacities of Cr(IlI) adsorption and intracellular uptake were so great that linear models could be applied while the complexation reaction was found to be clearly limited under the conditions of this study (0.007 mgCr/mgFTOC) (Imai, 1988). This also indicates that the percentage removal of Cr(IlI) by the activated sludge process would decrease if the TCro were less than 0.5 mgl-1 such that the OCr was lower than the complexation capacity. The percentage removal of Cr(III) would be a as function of TCrO. The predicted Cr(Ill) speciation in the solution phase was evaluated by subjecting the filtered effluent, sampled at Day 11, to the iron(III) coprecipitation treatment (Nakayama et aI., 1981). As shown in Fig. 5, 35.6% of Cr(IlI) remained in solution after the treatment, indicating that 35.6% of OCr was organically complexed Cr(Ill). The percentage fraction of CrL2 in OCr, as predicted by the CRAS model, was 35.3%. This excellent correlation seems to support the validity of the CRAS model in terms of Cr(Ill) speciation in the solution phase. It should be noted that the iron(IlI) coprecipitation treatment may cause the settlement of weakly complexed Cr(Ill), and thus the concentration of organically complexed Cr(Ill) by the coprecipitation treatment may be underestimated. CALCULATED
�======�--l
BLANK 2 BLANK 1 RUN 2 RUN1
������ o
10
20
30
40
Percent Cr(lII) remaining i n solution [%J
Fig. S. Percentage fraction of OCr remaining in solution after the iron(III) coprecipitation treatment: samples at Day 11 was used. SUMMARY AND CONCLUSIONS
A dynamic behavior of Cr(Ill) in the activated sludge process was investigated when the process received a square-wave input of Cr(III). The CRAS model, an equilibrium model under steady-state conditions, was extended to predict the behavior of Cr(Ill) as a function of the operational time. The main assumption in the CRAS model was that all chemical reactions involving Cr(III) proceeded fast enough that the equilibrium distribution of Cr(III) was instantaneously established in the process.
1056
A. lMAl and E. F. GLOYNA
A comparison between the CRAS model predictions and the experimental results revealed the following: (1) The CRAS model quantitatively predicted the dynamic behavior of Cr(Ill) as a function of the operational time when the TCr concentration increased. The average relative error was 5.46% for DCr and 7.45% for TCr. (2) When the TCr concentration decreased, the model predictions significantly underestimated the TCr concentration and overestimated the OCr concentration. Assuming that intracellular Cr(III) is trapped inside the cell in inactive forms, the model adequately predicted the behavior of Cr(III). A kinetic approach to describing intracellular Cr(IlI) uptake may be needed. (3) The iron(III) coprecipitation treatment left 35.6% of OCr, sampled at Day 11, in solution. In comparison, the CRAS model predicted that 35.3% of DCr was organically complexed Cr(lll), CrL2. This agreement seemed to support the validity of the CRAS model in terms of Cr(III) speciation in solution. ACKNOWLEDGEMENTS
The financial support provided by the Bettie Margaret Smith endowment fund is gratefully appreciated. Appreciation is also extended to Mr. Frank Hulsey for helping to set up the experimental apparatus. REFERENCES APHA (1980). Standard Methods for the Examination of Water and Wastewater. 15th ed., American Public Health Association,Washington D. C. Di Taro, D. M. and Horzempa, L. M. (1982). Reversible and resistant components of PCB adsorption-desorption: isotherms. Environ. Sci. Technol., lQ, 594-602. Imai,A. (1988). The behavior of chromium in the activated sludge process. Ph. D. thesis,University of Texas at Austin. Karickhoff, S. W. and Morris, K. R. (1985). Sorption dynamics of hydrophobic pollutants in sediment suspensions. Environ. Toxicol. Chem.,A, 469-479. Morgan, J. J. and Stone,A. T. (1985). Kinetics of chemical processes of importance in lacustrine environments. In: Chemical Processes in Lakes, W. Stumm (Ed)., John Wiley & Sons, New York,389-424. Nakayama, E., Kuwamoto, T., Tsurubo, S., Tokoro, H., and Fujinaga, T. (1981). Chemical speciation in sea water. Part 1. Effect of naturally occurring organic materials on the complex formation of chromium(III). Analytica Chimica Acta, 130,289-294. Nelson, P.O., Chung, A. K., and Hudson, M. C. (1981). Factors affecting the fate of heavy metals in the activated sludge process. 1. Water Pollution Control Fed. , 53,1323-1333.
Patton, W. H. and Budd, K. (1972). Zinc uptake in Neocosmospora vasinfecta. 1. Gen. Microbiol., 72, 173184. Patterson, J. W. and Kodukula, P. S. (1984). Metal distributions in activated sludge systems. 1. Water Pollution Control Fed. ,�,432-441. Sigg,L. (1987). Surface chemical aspects of the distribution and fate of metal ions in lakes. In: Aquatic Surface Chemistry, W. Stumm (Ed)., John Wiley & Sons, New York, 319-346. Stumm,W. and Morgan,J. J. (1981). Aquatic Chemistry. 2nd Ed. John Wiley & Sons, New York. Williams, R. 1. P. (1983). The symbiosis of metal ions and protein chemistry. Pure & Appl. Chem.,,&, 35-46.
