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Development of a Computer-Generated Equilibrium Model for the Variation of Iron and Manganese in the Hypolimnion of Lake Mendota Michael I?.Hoffmann"?and Steven J. Eisenreich Environmental Engineering Program, Department of Civil and Mineral Engineering, University of Minnesota, Minneapolis, Minnesota 55455
w A computer-generated chemical equilibrium model has been developed to predict the unusual seasonal variation of iron and manganese in the hypolimnetic water above the sediments in Lake Mendota. Models for the detailed chemical speciation of Mn and Fe have been developed as functions of pH, redox level, adsorption, and organic complexation. Results indicate that pH is the primary variable controlling the preferential release of Mn due to simultaneous pH dependent dissolution of solid MnC03 and desorption of Mn(I1) from oxide surfaces. Progressive appearance of Fe is predicted in terms of a pH-dependent dissolution of iron oxides as influenced by organic complexation. Using the maximum observed concentrations of major chemical components as input data, we have reproduced experimentally determined variations of Mn and Fe as a function of pH over the range 8.76.9.
Introduction Seasonal variations of Fe and Mn in hypolimnetic waters just above the sediments in eutrophic lakes have been observed frequently since an initial detailed description by Einsele (1).Mortimer has demonstrated experimentally both in the laboratory (2) and in the field ( 3 ) that Fe, Mn, and carbonate are released progressively from the sediments during periods of thermal stratification in highly productive dimictic lakes. Subsequently, Delfino and Lee ( 4 , 5 ) reported an unusual variation of Fe and Mn in the hypolimnion of Lake Mendota, which is a hard-water, eutrophic lake of glacial origin in south central Wisconsin. During periods of thermal stratification, Mn is preferentially released from the sediments even though the ratio of Fe to Mn in the sediments is 19:1(6, 7). Previous studies (8)have shown that for most lakes Fe is preferentially enriched in the water column immediately above the sediments. In order to gain further understanding of dominant variables, phases and species that affect the variation of Fe and Mn in Lake Mendota, chemical equilibrium models have been developed using a modified form of the computer program REDEQLB (9,10). Given appropriate sets of analytical data and thermodynamic constants, one can use these models to predict relative variations and concentrations of Fe and Mn in the bottom waters throughout the summer months as the pH drops from 8.5 in early May to 6.9 during the anoxic period of late August. Application of computer-generated equilibrium models to natural water systems has been limited in the past by the lack of sufficiently reliable analytical and thert Present address: W. M. Keck Laboratories, California Institute of Technology, Pasadena, CA 91125.
modynamic data to verify model predictions (11). These problems are minimized in the present study of Lake Mendota because of the availability of extensive field data and of an updated compendium of stability constants published by Smith and Martell (12). An example of the seasonal variation of Mn as observed by Delfino and Lee ( 4 , 5 ) 1 m above the sediments is given in Figure 1. The majority of total Mn was determined analytically to be present as Mn(II), although trace levels of Mn oxides were detected in epilimnetic waters. In anoxic waters, operationally defined soluble Mn(I1) was observed exclusively. Increases in Mn(I1) concentrations 1 m above the sediments begins with the onset of thermal stratification. With formation of a distinct hypolimnion, the bottom waters are effectively sealed from the upper epilimnetic waters and the atmosphere. Bacterial respiration depletes the hypolimnetic dissolved oxygen with a concomitant release of C02 and subsequent decrease in pH. Since the thermocline generally lies below the euphotic zone, there is no replacement of oxygen by photosynthesis. Gradually, the hypolimnion is transformed from an oxidizing to a reducing environment with apparent release of soluble Fe, Mn, [c03'-]T and [Po43-]Tfrom the sediments. During the middle of the stratification period, hydrogen sulfide gas is released from the sediments because of the bacterially mediated reduction of sulfate or organic sulfides or the dissolution of sulfide solids. As the summer stratification progresses, there is a pronounced increase in soluble Mn(I1) 1m above the sediments a t the Deep Hole sampling station. Fe(I1) concentrations increase a t a less dramatic rate. Analysis of Delfino's data ( 1 3 ) showed that the total Mn concentration increased at an approximate rate of 0.015 mg/(L day) as depicted in Figure 1. Significant increases in [Mn] were first noticed when the dissolved-oxygen concentration decreased to 5 mg/L with a corresponding decrease in pH due to COS released during respiration. As a first approximation it appears that, as the pH drops during the period of thermal stratification, the [Mn] shows a corresponding steady increase with a maximum observed [Mn] = 0.64 mg/L. Delfino and Lee ( 5 )found that, although the sedimentary Fe:Mn ratio was 19:1, little iron was released from the sediments compared to Mn. From these results they concluded that reducing conditions in the anoxic hypolimnion were too mild to result in the release of Fe. Mortimer (2,3) showed that a pH 16.5 was required for Fe(I1) to appear in the hypolimnion of Esthwaite Lake in England. In addition, with the generation of HzS, total Fe concentrations may have been limited by FeS solubility. Nriagu (14) has reported substantial evidence for the existence of amorphous FeS in,the Mendota sediments along with magnetic iron oxide spherules (15)which
0013-936X/81/0915-0339$01.25/0 @ 1981 American Chemical Society
Volume 15, Number 3, March 1981 339
may have entered the lake system as a result of coal utilization. Initially, Delfino and Lee ( 4 ) developed a simple equilibrium model which predicted that the appearance of soluble Mn(I1) should be a function of the pH-dependent dissolution of MnC03, although the predominance of MnC03 over MnS was sensitive to the sulfide-to-carbonate ratio. Slightly higher [HS-]T:[ c o 3 2 - ] ratios ~ than those observed in the hypolimnion would have been required to shift stability to MnS. Subsequently, the same authors ( 5 ) suggested that the pHdependent desorption of Mn(I1) adsorbed onto Mn and Fe oxides could be the primary mechanism controlling the appearance of Mn(I1) a t the sediment-water interface. This conclusion was based on laboratory experiments and the lack of X-ray diffraction evidence for the existence of either MnC03 or MnS in the Lake Mendota sediments. The fundamental principles of the aquatic chemistry of iron and manganese have been presented (8,16-24) and discussed in terms of an array of simple inorganic species of Fe and Mn. In addition to elementary inorganic species and solids, numerous complexes of Fe(III), Fe(II), and Mn(I1) with both inorganic and organic ligands may exist. Adsorption of the aquated and hydrolyzed metals on oxide and colloidal organic surfaces may also contribute to the speciation of Fe and Mn in aquatic systems. Simple thermodynamic models may be developed by constructing pc vs. pH and pC vs. pH diagrams (24) as a guide to changes in chemical behavior as a function of pH, pc, and pC. However, the usefulness of these diagrammatic models is limited by the number of chemical reactions considered, the reliability of the thermodynamic data, and the concentration ranges that may be conveniently explored. Possible controlling phases and species may be identified, but detailed information on the interrelationship of complex chemical phenomena remains obscured. Important processes such as adsorption-desorption, acid-base equilibria, and inorganic-organic complexation may be overlooked. The limitations of simple equilibrium models described above can be overcome with the use of a general-purpose computer program, such as REDEQL2 (9-111, which is capable of examining in detail multiple acid-base, anationaquation, dissolution-precipitation, oxidation-reduction, and sorption-desorption processes in aqueous systems. In general, for a well-defined natural water system or isothermal, isobaric equilibrium model can be constructed with the aid of REDEQL2 provided that the important chemical constituents and phases can be adequately identified and quantified. To compute the equilibrium composition of a natural water system, it is necessary, first, to have analytical information that defines the chemical and physical parameters of that system and, second, to have adequate thermodynamic and stoichiometric data to define the equilibrium state of the system.
Procedure Hoffmann (25) has shown that when the majority of chemical reactions, which are given consideration in an equilibrium model, can be shown to be kinetically rapid in comparison to the chemical residence time, an equilibrium model is a valid approximation for determination of the distribution of chemical species in a dynamic system. For the particular case of the hypolimnion of a thermally stratified lake, the steady-state condition appears to be a reasonable approximation of equilibrium, even though concentration gradients have been shown to exist vertically in the lake system ( 4 , 5 ,13). Because of the formation of the thermocline, it can be assumed that the hypolimnion is sealed essentially from interaction with the atmosphere and, as a first approximation, from interaction with the epilimnetic waters. Two additional assumptions in development of an equilibrium model for the sedimht-water interface are, first, that biological processes 340
Environmental Science & Technology
May I
I
SI
.
May 31
30
June
I
July
,
I
31
l u g 31 I
-
79-
DH 77
-
75
-
73
-
71
-
4b
d0
60
TIME
-
7b
,b
i0
lb0
Ilb
122
DAYS
Figure 1. pH and [Mn] in mg/L plotted vs. time for the Deep Hole sampling station 1 m above the sediment-water interface during 1967. Data taken from Delfino ( 73).
Table 1. Imposed Concentrations for Primary Components metal/ llgand
Ca2+ Mgz+ ~ e ~ + Fe2+ Mn2+
cO3*-
sod2CI-
PCI (imposed)
(Imposed), mg/L
4.00 4.00 20.00
3.14 3.78 5.60 8.00
29.04 4.03 0.14 0.00
4.93 2.45
0.65 212.88
3.78 3.45 5.18
15.93 12.58 0.1 1
3.96 5.31 4.63
3.51 0.47 2.16
4.70
3.1 1
14.00 6.00 4.00 3.50 3.40
9-
5.60 16.00
P043Si044DlPY
10.50 10.00 8.00
"3
tC1
PCeq (guess)
are not critical either chemically or physically and, second, that the hydromechanics of the bottom waters have a negligible effect on the chemical reactions that take place. Experimental data collected by Delfino and Lee ( 4 , 5 )have been combined with conventional chemical and limnological parameters obtained from internal publications of the Water Chemistry Laboratory, the Limnology Laboratory, and the Water Resources Research Center of the University of Wisconsin to provide input data to the computer program. Data accumulated during 1966 and 1967 have been critically selected for model development. To model the time-dependent changes in the iron and manganese concentrations, we determined the chemical speciation of Fe and Mn as a function of pC, pH, pc, and pS (negative logarithms of concentration, hydrogen ion activity, electron activity, and surface area (ha/L or 104 m2/L), respectively). Initially, a simple inorganic model was developed in which the primary variables were pH and pc. pH and pc were varied incrementally over the ranges 6.7 < pH < 9.0 and -6.0 < PE < 12.0. For these pc values, pc = -6.0 represents a strongly reducing environment and p~ = 12.0 represents a strongly oxidizing environment. To this simple model a set of adsorbing oxide surfaces and a set of representative organic ligands were superimposed. The metals and ligands selected for consideration in model development and their imposed concentrations are listed in Table I. a-SiO2, Fes04, Fe(OH)3, and 6-MnOz were considered as solid surfaces. pC values listed in Table I represent the maximum observed concentrations of the major cations and anions during the
Summer of 1967. Organic ligands, which are commonly thought to mimic the complexation characteristics of naturally occurring organic matter, were selected, and individual concentrations were based initially on a distribution of one-tenth of the observed DOC according to the number of equivalents of carbon for each ligand. Additional solid phases and cornponents were considered in selected redox equilibria. The solids in this group were MnOz, Mn304, MnOOH, Fez% Fe304,FeOOH, and FeSz. Assuming that the hypolimnion was a closed system with a fixed ionic strength of 0.006, a fixed pH, and a Pco2of 0.0, the solution to the equilibrium problem involving 5 metals, 13 ligands, 100 complexes, 18possible solids, redox equilibria, and 3 adsorbing surfaces was determined. Successive solutions to the same basic problem were obtained for incremental changes in pH. Sensitivity of a particular set of results to variations in pC, PE, and pS was determined. Stability constants in the thermodynamic data bank were updated by using the critically evaluated constants reported by Smith and Martell ( 1 2 ) for p = 0.0 and T = 25 "C along with selected values from the current literature and some selected values from Sillen and Martell (26). Ionic strength corrections for constants of zero ionic strength are made within a subroutine of REDEQL2 which utilizes the Davies equation ( 2 4 ) for corrections of individual ion activity coefficients. In this case, activity coefficients are corrected to p = 0.006. Adsorption of metal ions on oxide surfaces is modeled within the framework of the electrostatic model of James and Healy (27) using an appropriate subroutine in REDEQL2. In the James and Healy (28) model adsorption on oxide surfaces is considered to be an equilibrium process represented by an overall equilibrium constant. The choice of the James and IIealy model was pragmatic in terms of being available as a proven subroutine of REDEQL2. Moreover, Westall and Hohl (29) have shown that there are few differences in the variegated models developed in recent years to quantitatively account for experimentally observed adsorption of metals on oxide surfaces. Their conclusion is that the choice of a particular model is not critical for simple oxide systems. Physical parameters for each oxide surface and appropriate AG ",-hem values to adsorbing metals were obtained from Stumm and Morgan (21,24), Leckie and James (28), and Vuceta (30). Because of limited data on AHo and AS" values for each equilibrium constant, temperature corrections were not made. In its present form, REDEQL2 does not have a subroutine to make temperature corrections on individual equilibrium constants. To make temperature corrections, it is necessary to utilize a separate thermodynamic data base for temperatures other than 25.0 "C. This deficiency presents a small problem for the Lake Mendota model, since the temperature at the sediment-water interface is near 12.0 OC throughout the summer. Aqueous equilibria may be shifted in either direction by a decrease in temperature. For example, gas solubility and some solid dissolutions (e.g., CaC03) increase with decreasing temperature, whereas many chemical reactions show a significant lowering in AGO with decreasing temperature although some reactions such as ionization of weak acids show little temperature dependence. From the integrated form of the van't Hoff equation (24), it can be shown that the X AH at 25.0 approximate change in log Kldegree is 2.5 X " C . For FeS and MnS dissolution, pK, (26, 27) would be shifted ca. 0.33 and 0.16 log units, respectively, to more negative values. These changes would have a minimal impact on maximum solubilities if either FeS or MnS were controlling phases. In addition, for most species that were considered in model development, AH values are comparatively small, and on this basis it can be assumed that the error involved in solving the equilibrium problem at 25 "C and not at 12 "C will be minor.
901 80
$
40
8
30
P
x
Figure 2. Inorganic model for the speciation of Mn under reducing conditions including the impact of an adsorbing oxide surface at the sediment-water interface, where PE = -4.0,pS = 3.0,pMn = 4.93, pC03*- = 2.45,and Pco2= 0.0, which denotes a system closed to the atmosphere.
Results and Discussion Models generated by REDEQL2 indicate that pH is the primary variable controlling the apparent release of Mn due to the pH-dependent dissolution of MnC03 and the pH dependent desorption of Mn(I1) from oxide surfaces. The primary distribution of Mn as a function of pH for a simple inorganic model with adsorption is given in Figure 2, where the observed percentage of a particular species is plotted vs. pH. The relative concentrations at a specific pH would be given by Ci,j = % X 10-pCT4 where C T , ~is the imposed analytical concentration of component j and C i j is the concentration of the species i of component j. The predominant species shown in Figure 2 represents at least 99% of the total imposed manganese concentration. For this model the apparent increase in soluble Mn is due primarily to the dissolution of MnC03 as the pH drops from 8.7 to 7.5. A pc of -4.0 was selected as a reasonable fixed value for the entire 3-month period considered in the model. At first glance, it seems that the combination of sulfate reduction with a concomitant pH decrease should result in a significant variation in the effective redox potential; however, when the pc of the system was estimated from the sulfide/sulfate data of Delfino (13) using the HS-/S042- redox couple (31) as shown in Table 11,little variation in pc was observed. Over a 2-month period of anoxic conditions, the average PEwas estimated from experimental data to be -3.80 f 0.2. Therefore, a pc of -4.0 for all computer calculations was considered to be a reasonable approximation. These calculated trends are consistent with the laboratory observations of Mortimer (3) and the field observations by Delfino and Lee ( 5 ) .Comparison of the Calculated Mn concentrations in mg/L to the observed values is given in Figure 3. The trend in calculated values agrees well with the trend in observed values for Mn. These results indicate that the primary mechanism for the release of Mn is the pH-dependent dissolution of the solid MnC03 and that the secondary mechanism is the pH-dependent desorption of Mn(I1) from oxide surfaces. The impact of incremental changes in PEwithin certain ranges appears to be negligible. Although there are limitations to the application of current adsorption models Volume 15, Number 3,March 1981 341
Table II. Estimation of a Reasonable pc for the Lake Mendota Sediment-Water Interface Based on the HS-/ SO4*- Redox Equilibrium date
pn
ST, mg/L
a1 a
pWS-1
P[so42-l
8-2-67 8-9-67 8-16-67 8-24-67 9-6-67 9-19-67 9-30-67
7.42 7.35 7.16 7.38 7.30 7.15 6.98
0.29 0.96 1.90 2.88 2.28 2.46 3.50
0.72 0.69 0.59 0.71 0.67 0.53 0.49
5.20 4.70 4.48 4.22 4.33 4.41 4.26
3.78 3.78 3.78 3.78 3.78 3.78 3.78
a a1 = [HS-]/ST = ([H+]/Kl = 34.0 (see ref 24, p 310).
+ 1 t K2/[H+])-’
where pK1(H2S)= 7.0 and pK2(H2S)= 14.0.
PE =
’islog K + ’/a
log
Pfb
-3.91 -3.90 -3.71 -4.00 -3.89 -3.60 -3.53
[H+]’/[HS-])
where log K
100
90
80
30
20 IO ~
68
70
72
74
76
78
80
82
84
86
88
pH in Hypdimnion
oS=28Si02
PEE-4.0
pDlP=47
pMn.50
( 3 1 ) ,the present results indicate that the surface area of the oxides must be increased to high values before pH-dependent desorption becomes the predominant factor in Mn release. In addition, at higher adsorbing surface areas, the observed trend in Mn(I1) release was not matched as closely because a significant fraction of Mn(I1) remains adsorbed on both a-MnO2 and a-Si02 a t pH 7. This is understandable since the pH of the zero point of charge for both a-MnO2 and a-Si02 is close to 2. Neither Fe304nor Fe(OH)3shows significant adsorption capacities in the pH and pS ranges of interest. This is primarily due to the higher pH,,, value for these solids. An optimized surface area for a-MnO2 and a-Si02 appears to be pS = 2.8. At this surface area both a-MnO2 and a-SiO2 desorption combined with MnC03(s) dissolution reproduces the general trend in observed concentrations although the fit observed with a-Si02 alone is more satisfactory than either a-MnO2 alone or a combination of a-MnO2 and a-SiOz. In order to reproduce the dynamic Fe trend, it was necessary to superimpose on the initial inorganic adsorption model for Mn a set of organic ligands with a significant affinity for chelation with Fe but with minimal affinity for Mn, and it was necessary also to suppress the formation of FeS2 over the pc range 0 to -6.0 by ignoring the FeSz redox couple. This is not inappropriate since Nriagu (14) has confirmed the absence of discrete crystals of FeS2 in the blue-black sludge that dominates the top 150 cm of sediment. The principal forms
Environmental Science & Technology
I
I
a
a3
\
ai
1
1
I
I
I
7.9
z7
75
7.3
7.1
69
PH
Figure 3. Comparison of observed Mn concentrations with calculated values at the pH for the final inorganic-organic adsorption model.
342
87
pFe= 5.60 p i -4.0
pDlPY = 4 70
pcop = 0.0
p = 0 006
pS102.
28 Figure 4. An inorganic-organic adsorption model for the speciation of Fe as function of pH at the sediment-water interface, where pFe = 5.6, pDlPY = 4.70,pC03*- = 2.45,fco2 = 0.0(a closed system), y = 0.006,pS = 2.8,and T = 25.0 O C . of iron in the sediments appear to be X-ray amorphous FeS (hydrotroilite or mackinawite), which is thought to contribute to the black hue, and Fe304 (magnetite) which has been identified as the principal component of magnetic spherules found in the sediments (15). The speciation of Fe over the pH range 8.7-6.9 is presented in Figure 4, and the concentrations of significant species are listed in Table 111. This final model was selected after a trial-and-error fitting of calculated values for the total soluble iron concentration to observed values. Using the principle of “Ockham’s Razor” (32),the model presented in Figure 4 is the simplest model capable of reproducing the observed trend reasonably well. The apparent release of Fe(I1) can be explained in terms of a pH-dependent dissolution of Fea04influenced directly by complexation of Fe(I1) by 2,2’-dipyridyl (DIPY) (log 0 = 17.2 for 1:3 complex). DIPY was selected as the primary complexing ligand because its stability constant for the 1:1complex (log = 4.9) was in the range of values reported for complexation of metals by dissolved organic carbon in natural waters (33)and because Tuschall, Jr., and Brezonik (34) have shown that nitrogen containing dissolved organic carbon exhibits an affinity for Fe in Florida lake water. In a narrow range of pH between 7.4 and 6.9, FeS appears as
a predominant phase although it begins to redissolve below pH 7.2. For the reasons discussed above, PE = -4.0 was selected for the final model, which gives the trends for both Mn (Figure 3) and Fe (Figure 5 ) as a function of pH alone. The trend in Fe(I1) can be closely approximated by other models involving dissolution and reduction of Fe(OH)3 and FeOOH, but these models required suppression of both FeSz and Fe304 as possible phases in addition to a stepwise lowering of p~ with pH. Other models invoking a high level of adsorption on FesO4, Fe(OH)3, and M n 0 2 (i.e., high pS) were promising but were not as satisfactory or as simple as the model presented in Figure 4. The overall model indicates that desorption of Fe(I1) and Mn(I1) plays an important role but not necessarily the dominant role in metal control in aquatic systems as suggested by Jenne (22) and Delfino and Lee ( 5 ) .Dissolution of Fe304 may be an important release mechanism for Fe in light of the high concentration of magnetic spherules in the sediments. The source of magnetic spherules has been traced to airborne fly ash of coal-fired power plants situated near Lake Mendota ( 1 5 ) .Dissolution of Fe304 is commonly thought to be an extremely slow process in the absence of catalytic influences; however, Jenne ( 2 2 )has shown that extraction of iron oxides from the Chinle Formation of the Colorado Plateau proceeded rapidly (i.e., dFe/dt N 1 mg min-l g-l) in the presence of a dithionite-citrate solution. Other synthetic complexing agents have been shown by Hoffmann et al. (35, 36) to have a significant accelerating effect on the rate of dissolution of chalcopyrite, CuFeSz, pentlandite, (Ni,Fe)&s, plagioclase, NaA1Si308-CaAlzSiO8, and olivine, (Fe,Mg)zSi04. The effect of synthetic and naturally occurring organic ligands on the weathering of silicate, sulfide, and oxide minerals is well documented (37-40). In general, the rate and the extent of solid dissolution are enhanced in the presence of moderately strong chelating ligands. These experimental results together provide support for the hypothesis that Fe complexation is an important mechanism for the appearance of soluble iron in the hypolimnetic waters as predicted by the thermodynamic model. Formation of FeS is consistent with the high levels of sulfide found near the sediments toward the end of summer. Of interest is the narrow stability field of FeS under these conditions. As the pH drops below 7.2, the activity of S2- drops significantly so that the system is no longer saturated with respect to FeS in the overlying waters and subsequently it dissolves and Fe(I1) is found predominantly in a complexed form. Mortimer ( 3 ) and others (22)have shown that pH must be shifted below 6.7 for a significant release of Fe to occur from lake-bed sediments. Mandel (41) observed little Fe release when organic matter was low but a high release of Fe upon addition of organic matter to water overlying paddy sediments. Mn release was found to be high either in the presence or in the absence of dissolved organic matter ( 3 7 ) .Schindler and Alberts (42) found that Fe(I1) in anoxic bottom waters of four Georgia reservoirs was associated with dissolved organic carbon in soluble size fractions. In oxic surface waters there was no detectable iron in filtered fractions (
