Environ. Sci. Technol. 2004, 38, 6571-6581
Stream-Subsurface Exchange of Zinc in the Presence of Silica and Kaolinite Colloids JIANHONG REN* AND AARON I. PACKMAN Department of Civil and Environmental Engineering, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208-3109
The mobility of sorbing contaminants in surface waters often depends strongly on associations with sediments, including both fine suspended particles and stationary bed sediments. Hydrodynamic flow coupling causes an exchange of dissolved and suspended substances between streams and underlying pore waters (hyporheic exchange). As a result, the fate of many pollutants is expected to be greatly influenced by the flux of colloids and contaminants across the stream-subsurface interface and the interactions of both types of substances with the bed sediments. Herein, we present experimental results on the streamsubsurface exchange of zinc in the presence of colloidal silica and kaolinite in a laboratory flume. We also apply a process-based theoretical model to predict the coupled transport of colloids and reactive solutes. Model input parameters were obtained using independent batch and column experiments. Zinc immobilization in the bed was found to be significantly greater in the presence of kaolinite than in the presence of colloidal silica. Model predictions indicated that there were two distinct reasons for the greater zinc immobilization in the presence of kaolinite: zinc sorbed more strongly to kaolinite than to silica, and kaolinite particles also deposited more readily in the streambed than did silica colloids. Model simulations were found to be highly dependent on the colloid size distribution. When the colloids had a bimodal distribution, colloidal-phase contaminant transport occurred primarily on the finer fraction, but bulk colloid deposition was dominated by removal of the coarser fraction.
1. Introduction The interface between streams and groundwater has received increasing attention because of its important role in regulating the transport of contaminants and ecologically relevant substances such as carbon and nutrients (1-6). Local-scale biogeochemical interactions often control the transport of reactive substances in surficial sediments (e.g., ref 7). Analysis of contaminant transport in such systems is greatly complicated by the fact that the flow system at the streamgroundwater interface is itself very complex. Hyporheic exchange can be induced by the interaction of the streamflow with a variety of physical features such as riffles, pools, obstacles, and bedforms (8-12). Improved understanding * Corresponding author phone: (361)593-2798; fax: (361)593-2069; e-mail:
[email protected]. Present address: Department of Environmental and Civil Engineering, Texas A&M University Kingsville, Kingsville, Texas 78363-8202. 10.1021/es035090x CCC: $27.50 Published on Web 11/02/2004
2004 American Chemical Society
of contaminant transport and nutrient dynamics in streams will require closer examination of hyporheic flow paths and associated reactions. Currently, our ability to represent the coupling of physical transport and biogeochemical processes in the hyporheic zone is very limited, and transport models with better integration of component physical, chemical, and biological processes are needed (5, 6). The most commonly used model for hyporheic exchange, the Transient Storage Model and its implementation in the OTIS software package, idealizes hyporheic exchange as a bulk mass-transfer process with a well-mixed hyporheic zone of defined area (13, 14). This model has been linked with reaction subcomponents to represent the effects of processes such as sorption to streambed sediments, and this approach has proven to be very valuable for the evaluation of remediation options in metal-impacted streams (15-17). While this exchange model provides great flexibility in representing observed in-stream transport (i.e., breakthrough curves), it does not provide either predictive capability or generally applicable insight into local-scale exchange processes. The hydrodynamic component of the model averages exchange over stream reaches of considerable length and relies on empirical determination of exchange parameters based on curve-fitting the results of solute injection experiments. This type of approach is inherently limited by the inability to resolve pore water flow paths, which prevents explicit analysis of the physical-chemical process couplings that control reactive transport in the environment. Recent advances in understanding the mechanics of stream-subsurface flow interactions have led to the development of predictive, process-based models for hyporheic exchange. A wide range of periodic topographical features is found in streams, including pool-riffle sequences in highgradient streams and dunes in sand-bed rivers (18, 19). All of these topographical features can induce advective streamsubsurface exchange flows, which are generally termed pumping exchange (8, 10, 11). Bedform-induced pumping exchange processes have been examined in detail in laboratory experiments and described via theoretical modeling (8, 10, 11, 20-22). These studies have shown that advective pumping is often the dominant mechanism of hyporheic exchange and that net exchange can be modeled successfully by considering the basic properties of the stream-subsurface system, such as the stream velocity, bed topography, and bed sediment permeability. Recently, a new class of models has been developed to allow application of this type of process information to analyze downstream solute transport in natural streams (23, 24). These recent theoretical advances offer the potential for analysis of reactive hyporheic exchange and net downstream transport of contaminants based on explicit representation of local-scale biogeochemical processes in a pore water transport framework. A second important effect that cannot be represented in lumped empirical exchange models is the transport of contaminants in a colloidal phase. Colloidal particles, such as silica and clays, are ubiquitous in streams and can significantly mediate the transport of surface-active contaminants. Colloids are considered here as fine particles with a diameter less than 10 µm (following ref 25). Due to their small size, colloids often have very large specific surface areas and are very important absorbents in natural waters. Many field studies have demonstrated that metals are often associated with colloidal particles through either coprecipitation or adsorption mechanisms (7, 26-28). VOL. 38, NO. 24, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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Previous studies have shown that the downstream transport of colloidal particles is mediated by the combination of advective hyporheic exchange and subsurface filtration (22, 29). These studies modeled colloid deposition by explicitly considering advective pumping exchange, pore water transport, and colloid immobilization due to filtration and settling. Advective pumping can also transport pollutants, such as metal ions, from streamwater deep into the sediment bed. The transfer of metal ions between the streamwater and sediment bed can be predicted well by a model that combines hydrodynamic transport and sorption of metals to the bed sediment (30). In the work presented here, we conducted experiments to assess the effects of colloidal silica and kaolinite clay on the exchange of zinc between the stream and streambed in a laboratory flume. The hydrodynamic and chemical conditions were well controlled so that we could relate the observed net mass transfer to the underlying controlling processes. A multiphase contaminant transport model was applied to interpret the observed stream-subsurface exchange of both colloids and zinc in terms of the controlling physical and chemical processes. The results of this study clearly illustrate the role of colloidal particles in mediating the transport of sorbing contaminants across the stream-subsurface interface and suggest that this process can substantially influence contaminant mobility in surface waters.
2. Theory To model contaminant transport in the presence of colloids, advective exchange of both colloids and contaminants between the streamwater and stream bed must be considered along with the following nonconservative processes: (1) contaminant sorption to/desorption from colloids; (2) contaminant sorption to/desorption from bed sediments; and (3) colloid deposition by means of filtration and settling. We developed a predictive model to analyze the effects of these processes in Ren and Packman (31). We apply this model heuristically here in conjunction with experimental results obtained in a controlled laboratory setting to advance basic understanding of multiphase contaminant transport processes. Modeling of field applications, i.e., use of the model to assess the boundary exchange term in the advectiondispersion equation for downstream contaminant transport, is addressed in ref 31. Key elements of the model are presented below. Pumping Exchange Flow between Stream and Streambed. Bedform-induced advective pumping produces the following two-dimensional pore water velocity distribution in the streambed (21)
u ) -kKhmcos(kx)[tanh (kdb)sinh (ky) + cosh(ky)] + uu (1a) v ) -kKhmsin (kx)[tanh (kdb)cosh(ky) + sinh (ky)] (1b) where u is the longitudinal Darcy pore water velocity, v is the vertical Darcy pore water velocity, K is the hydraulic conductivity of the bed sediment, hm is the half amplitude of the head variation over the dune-shaped bedform, k is the bed form wavenumber (k ) 2π/λ, λ is the dune wavelength), x is the longitudinal coordinate along the streambed, y is the vertical coordinate (perpendicular to x), db is the bed depth, and uu ) KS is the pore water flow due to the stream slope, S. Pore water velocity profiles and resulting solute transport 6572
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can be normalized by applying characteristic length, time, and velocity scales related to the bedform geometry
x* ) kx ) 2πx/λ
(2a)
y* ) ky ) 2πy/λ
(2b)
db* ) kdb ) 2πdb/λ
(2c)
u* ) u/kKhm ) u/um
(2d)
v* ) v/kKhm ) v/um
(2e)
t* ) k2Khmt/θ ) kumt/θ
(2f)
uu* ) uu/um
(2g)
where θ is the porosity of the bed sediment, um ) kKhm is the maximum induced pore water velocity, and x*, y*, db*, u*, v*, and t* are dimensionless x coordinate, y coordinate, bed depth, longitudinal Darcy velocity, vertical Darcy velocity, and time, respectively. Colloid Transport along Exchange Flow Paths. Both settling and filtration influence colloid deposition in the streambed. Since the travel paths of colloids will be modified by their settling velocity, the two-dimensional particle velocity distribution in the bed is
uparticle ) u ) -kKhmcos(kx)[tanh (kdb)sinh (ky) + cosh(ky)] + uu (3a) vparticle ) -kKhmsin (kx)[tanh (kdb)cosh(ky) + sinh (ky)] - vsθ (3b) where vs is the colloid settling velocity calculated by Stokes law. This solution applies for all bed depths, i.e., ranging from shallow streambeds to unconstrained systems. Colloid filtration along particle flow paths is calculated using the classical colloid filtration equation
Cc/Cc0 ) exp[-λf L]
(4)
where L is the distance traveled by colloids along paths through the bed, Cc is the mobile suspended colloid concentration in the pore water, Cc0 is the initial colloid concentration at inflow locations along the bed surface, and λf is the colloid filtration coefficient. The difference between Cc and Cc0 is the concentration of colloids that have become attached to bed sediments. Reactive Solute Transport along Exchange Flow Paths. Interactions between reactive solutes and both colloids and bed sediments are modeled using equilibrium adsorption isotherms. While more sophisticated sorption models are available, e.g., the surface complexation approach (32), computational constraints prevent direct implementation of this type of model in the multiphase, reactive pore water transport framework presented here. To overcome this limitation, batch sorption experiments were conducted to assess contaminant interactions with each of the sediments used here, surface complexation modeling was used to analyze the batch experiment results, and these results were then parametrized in the form of either Langmuir or Freundlich isotherms for implementation in the multiphase contaminant transport model. It will be shown that this simplified parametrization of the sorption process was sufficient for analysis of multiphase contaminant transport
at the scale of the laboratory flume experiments. The Langmuir sorption model is given by
KadsC 1 + KadsC
Γ ) Γmax
(5)
where C is the equilibrium concentration of the contaminants in the pore water, Γ is the sorbed concentration of reactive solute per unit mass of colloids or bed sediments, Γmax is the maximum sorbed concentration of reactive solute per unit mass of colloids or bed sediments, and Kads is the equilibrium constant (25). The Freundlich isotherm equation is given by
Cads-sediment ) ms‚C ns ) (As‚Ss‚Ks)‚C ns Cads-colloid ) mc‚C
nc
) (Ac‚Sc‚Kc)‚C
nc
(6a) (6b)
where C is the dissolved-phase contaminant concentration in the pore water (mg/L), Cads-sediment is the adsorbed mass of pollutant per unit mass of bed sediments (mg/g), Cads-colloid is the adsorbed mass of pollutant per unit mass of colloid (mg/g), m is the Freundlich constant, related to sorption capacity, Ks and Kc are the partition coefficients for bed sediments and colloids (L/mole sites), Ss and Sc are surface site densities for sand and colloids (mole sites/m2), As and Ac are specific surface areas for sand and colloids (m2/g), and n is the nonlinearity of the sorption process (25). Calculation of Net Stream-Subsurface Exchange. Net contaminant mass transfer from the streamwater to streambed is calculated using a residence time function approach. The residence time function for contaminants, R(t), is the fraction of the contaminant mass that entered the bed at time 0 and remains in the bed after an elapsed time, t. The flux-weighted average residence time function, R h (t), accounts for the nonuniform contaminant flux through the bed surface and can be obtained by analysis of transport and reaction along pore water flow paths using eqs 4-6. This residence time function approach thus allows net mass transfer from the stream to streambed to be related to both physical transport through the bed and nonconservative processes such as local-scale colloid deposition or contaminant sorption. For the case when the in-stream concentration suddenly increases to an initial concentration C0 (i.e. a dissolved or suspended substance is initially well-mixed in the stream, but not present in the bed), the initial mass in the stream per unit bed surface area is given by C0d′, where d′ is the total volume of water in overlying water column per unit bed surface area. At a later time t, the mass per unit bed area remaining in the stream is given by C(t)d′. The accumulated mass in the bed per unit area is given by M(t)θC(t), where M(t) is an equivalent depth of penetration into the subsurface. M(t) can be considered to be a normalized measure of accumulated mass transfer with units of length. Mass conservation between streamwater and streambed then yields
C*(t) )
C(t) d′ ) C0 d′ + M(t)θ
(7)
A second equation for M(t) can be obtained by performing a convolution integral of the interfacial flux, the flux-weighted average residence time function, and the in-stream concentration:
M(t)C(t) )
q j θ
∫ Rh (τ)C*(t - τ)dτ t
0
(8)
FIGURE 1. Photograph of the recirculating flume. The coupling of eqs 7 and 8 can be used to predict net exchange with a sediment bed in a closed system such as the recirculating laboratory flume used here for studies of multiphase contaminant transport. This analysis assumes the tracer is well-mixed in the stream (no significant concentration gradients in the downstream or transverse directions), which is a good assumption for the experiments analyzed here. Additional theory is required to represent surface water mixing processes, which are important in field applications such as the analysis of contaminant breakthrough curves in streams and rivers. For that type of application, the mean interfacial flux, q j , and residence time function, R h (t), used in eq 8 can be readily applied to assess the effects of hyporheic exchange on downstream contaminant transport (23, 24).
3. Experimental Methodology 3.1. Apparatus and Materials. Recirculating Flume. Experiments were conducted in a recirculating flume to investigate the stream-subsurface exchange of zinc in the presence of colloidal particles. As shown in Figure 1, the tilting flume used in this study has a test section of 2.5 m length, 20 cm width, and 50 cm depth. Solutes and colloids injected in the stream are easily recirculated in the flume by means of a return pipe and a pump. A parabolic ramp was placed in the inlet to the test section so that a steady uniform flow is obtained soon after the water enters the test section. A nylon mesh screen was used at the downstream end of the test section to retain the bed sediment at the desired depth. On the top of the channel, a point gauge was installed on a movable carriage. In addition, a calibrated vortex-shedding flow meter (Rosemont #8800) was installed in the return pipe to measure the recirculating flow rate. This type of the recirculating flume has been extensively used in laboratory studies of stream-subsurface exchange due to the easy control of physical and chemical conditions in the stream and sediment bed (21, 22, 29, 30). Since the flume is a closed system and the channel is composed of low-reactivity materials, the solute and particle concentrations in the stream change only due to the exchange with the streambed. Preliminary experiments were conducted to ensure that the added solutes and particles had no significant reactions with the channel. Bed Sediments. Ottawa #12 Flint silica sand was used as the bed sediment. This sand is commonly used in laboratory experiments because of its high purity (99.8% SiO2). Sieve analysis indicated that this sand has a quite narrow size distribution (29). Based on this analysis, an effective sand diameter of 500 µm was taken for all of the calculations in this paper. The sand has a hydraulic conductivity of 10.71 cm/min at 25 °C, and its total porosity at room temperature is 0.36. The sand was cleaned thoroughly using weak acid/ VOL. 38, NO. 24, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 2. Size distributions of colloids used in this study: (A) colloidal silica; (B) KTCAST kaolinite; and (C) Kaofill kaolinite. base for all of the experiments in this study following methods described in ref 29. Colloids. Silica and kaolinite were selected as representative natural particles to examine the effects of colloids on contaminant transport. Colloidal silica is usually present in surface waters as a stable dispersion of amorphous particles (33). Kaolinite is a clay mineral formed by weathering or hydrothermal alteration of other aluminum-rich minerals and is widely distributed in nature (34). Most soils contain kaolinite with a diameter of less than 2 µm. In highly weathered soils, such as those of Southeastern U.S. and tropical regions of Africa, Asia, and South America, kaolinite is usually the dominant clay mineral (34, 35). The surface charge of both silica and kaolinite in water is pH dependent and usually negative in natural surface waters (25). Silica colloids were obtained from Nissan Chemical Industries, Ltd. (Tokyo, Japan). These colloids have been extensively used in laboratory filtration experiments due to their simple composition and uniform size (29, 36, 37). Two sizes of these silica colloids were used in this study, with diameters of 0.45 µm (MP4540) and 0.10 µm (MP1040). Two types of kaolinite particles were also used: KTCAST from Kentucky-Tennessee Clay Company, (Sandersville, Georgia) and Kaofill supplied by Thiele Kaolin Company (Sandersville, Georgia). The size distributions of these particles were measured using a Brookhaven Instruments Corporation (BIC) ZetaPALS Multi-Angle particle size analyzer. The effective diameters (the average diameter weighted by the light scattering intensity) of KTCAST and Kaofill are 2.7 µm and 1.4 µm, respectively. Both Kaofill and KTCAST have a wide size distribution as shown in Figure 2. In addition, KTCAST has a bimodal size distribution. The effects of these size distributions on colloid and contaminant transport will be discussed in the results section. Metal Contaminant. Zinc was selected as a model metal contaminant because it is nontoxic, has a single oxidation state under our experimental conditions, and is a common 6574
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pollutant in streams. A.C.S. reagent grade ZnCl2 was used to prepare zinc solution in all experiments. 3.2. Flume Experiments. For each flume experiment, reverse-osmosis purified water was filled up to a desired level in the flume, and then the sand was poured into the main channel to the desired bed depth. Bedforms were created by making modest sediment mounds and then allowing the mounds to be shaped naturally under a velocity of approximately 30 cm/s for 10-30 min. The flow was reduced to the desired test condition when the bedforms had obtained a dune shape, as shown in Figure 1. The experimental flow conditions were chosen based on two criteria: (1) fully turbulent streamflow and (2) no bed sediment transport so as to keep stationary bedforms over the course of the contaminant transport experiments. Experimental stream velocities were typically in the range of 13 to 16 cm/s. These conditions are commonly found in streams at base flow. In each of the flume runs, NaCl was used as a conservative tracer to characterize the hydraulic stream-subsurface exchange rate. The salt concentration was determined by measuring the solution specific conductance using a Horiba ES-12 conductivity meter. A final well-mixed salt concentration of 10 mM was established in all experiments in order to mimic natural freshwater ionic strength conditions. After the salt became well-mixed through the stream and pore water, 0.2 mM NaHCO3 was added as a buffer, and the stream pH was adjusted to the desired value by adding either HCl or NaOH. These preparatory activities normally required 1 to 2 days. The main experiment involved simultaneous injection of colloids and zinc into the flume under steadystate hydrodynamic conditions. The colloidal suspension and zinc solution were poured slowly into the downstream endwell of the flume over one stream recirculation period, which was roughly 50 s in the experiments reported here. This procedure resulted in the entire volume of recirculating water being rapidly brought to approximately the same concentration. The streamwater was then sampled periodically and analyzed for zinc and colloids as described in next section.
TABLE 3. Oxide Surface Complexation Model Reactionsa
TABLE 1. Hydraulic Conditions for Flume Experiments
run #
stream depth (cm)
stream velocity (cm/s)
bed depth (cm)
bedform height (cm)
bedform wavelength (cm)
1 2 3 4 5 6
7.63 7.90 7.07 7.30 8.65 9.00
13.83 13.01 14.37 14.06 11.95 15.67
9.28 9.24 10.36 10.24 11.38 11.32
1.92 1.22 1.04 0.86 0.93 0.84
17.31 18.27 15.77 16.61 18.15 15.25
TABLE 2. Experimental Conditions for Flume Experiments run #
colloid type
colloid concn (mg/L)
zinc concn (mM)
pH
1 2 3 4 5 6
0.45 µm silica 0.45 µm silica 0.45 µm silica 0.10 µm silica 2.70 µm kaolinite 1.40 µm kaolinite
55.5 51.0 56.0 335.0 265.0 220.0
0.052 0.047 0.051 0.044 0.044 0.042
6.89 5.34 7.54 7.29 6.79 7.02
The main portion of each experiment typically had a duration of 3 days. Six flume experiments were conducted. The hydraulic conditions for each of these experiments are given in Table 1. The stream depth was between 7 and 9 cm, the stream velocity was 13-16 cm/s, the bed depth was 9-11 cm, the bedform height was 1-2 cm, and the bedform wavelength was 15-18 cm. These hydraulic conditions were chosen to allow examination of the multiphase contaminant transport processes under representative streamflow conditions. Previous results have demonstrated that the hydrodynamic exchange rate is scaleable to other flow conditions and system geometries (11, 23, 38). The colloid types, colloid concentrations, zinc concentrations, and stream pH for all flume experiments are given in Table 2. Three flume experiments were carried out with 0.45 µm silica colloids, one was conducted with 0.10 µm silica colloids, and two used kaolinite particles. The pH used was varied from 5 to 7.5, initial colloid concentrations were between 50 and 345 mg/L, and the initial zinc concentration in the stream was around 3 mg/L (0.05 mM). This range of colloid types, concentrations, and background chemical conditions illustrates the extent to which moderately reactive inorganic colloids can influence the stream-subsurface exchange of sorbing solutes. 3.3. Sampling and Analysis. Because in-stream mixing is very rapid relative to stream-subsurface exchange, a single sample completely characterizes the in-stream solute and colloid concentrations at any time. Samples were acquired manually from the end of channel. Each sample was analyzed for colloid concentration, total zinc, and dissolved zinc. Both silica and kaolinite particle concentrations were determined by light scattering using a spectrophotometer. The wavelength used for each type of colloids was chosen by examining the absorbance spectra of representative suspensions of each type of colloids over wavelengths from 300 to 900 nm. The most sensitive wavelength was then selected as the working wavelength for each type of colloids. A wavelength of 500 nm was selected for 0.45 µm silica colloids, 300 nm for 0.10 µm silica colloids, and 400 nm for kaolinite particles. Independent calibrations were developed for each type of colloid. Dissolved zinc concentrations were determined by physically separating colloidal zinc from samples. When the colloid diameter was 0.45 µm or greater, the particles were separated using a 0.2 µm syringe membrane filter. The 0.10 µm diameter silica colloids were separated by centrifuging at 7500 rpm for 80 min using a SORVALL RC 5B centrifuge (DuPont). Total zinc
#
species
component reaction
1 2 3 4
XOH XOH2+ XOXOZn+
XOH XOH + H+ S XOH2+ XOH S XO- + H+ XOH + Zn2+ S XOZn+ + H+
a
equilibrium constant
Ka1 Ka2 KZn
XOH is surface hydroxyl.
was obtained by reducing the sample pH to 2 and shaking for 2 days so as to desorb all zinc from the colloids. After separation, both dissolved and total zinc solutions were preserved by 2% HNO3 and then stored at 4 °C until analyzed. Zinc concentrations were measured by Atomic Absorption Spectroscopy (AAS) using a GBC Scientific Equipment model 932. Zinc and colloid distributions in the bed sediments were not measured in this study. In general terms, point measurements of solute and particle concentrations in pore waters are not very useful for interpretation of overall streamsubsurface exchange behavior because of the strong spatial variation in exchange fluxes and pore water transport. Instream data are thus preferable for assessment of net streamsubsurface interactions because they reflect the effects of all interfacial and sedimentary processes operating in the system (11). When observed simultaneously, solute and particle concentrations in overlying streamwaters have been found to be consistent with pore water concentration profiles (e.g., refs 11, 39, 40). Visualizations of colloid deposition profiles under conditions similar to those described here are presented in ref 41. 3.4. Characterization of Colloid and Contaminant Transport Model Parameters. All input parameters required for the multiphase contaminant transport model are independently measurable, allowing true prediction of both colloid and contaminant transport. Acid/base surface titrations were used to characterize the surface site densities and surface reaction constants of all particles (sand, colloidal silica, and kaolinite). For each type of particle, 50-300 m2/L of suspended solids were used. Samples were purged for 1 h using N2 gas to remove CO2 before titration. The titration was then carried out using a ME-10 autotitrator (McIntosh Analytical Systems, Inc.), while N2 gas was continuously applied. Titration data were analyzed using FITEQL 4.0 to estimate equilibrium constants and surface site densities (42). Batch experiments were conducted to characterize zinc sorption/desorption interactions with all sediments over the pH range used in the flume experiments. Batch experiment results were simulated using PHREEQC based on the equilibrium constants estimated from titration data (43). Equilibrium constants and/or surface site densities were adjusted where necessary in order to fit the batch sorption data well. Table 3 is a summary of the major oxide surface complexation model reactions considered when FITEQL and PHREEQC were applied to simulate the titration and batch experimental results. Figure 3 shows representative titration and batch data along with model simulations. Table 4 lists all of the parameters obtained by fitting titration data with FITEQL and batch data with PHREEQC. Input parameters used to simulate flume experiment results are given in Table 5. Specific surface areas, As and Ac, were calculated based on the particle sizes listed in Table 2, assuming spherical particles. Surface site densities, Ss and Sc, were estimated by fitting batch data using PHREEQC as described above. For each flume experiment, an appropriate sorption isotherm was constructed using PHREEQC by simulating zinc sorption at different total zinc concentrations based on the experimental conditions given in Table 2 and the sediment properties listed in Table 4. The PHREEQC VOL. 38, NO. 24, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 3. (A) Acid/base titration data and fitting from FITEQL for 0.45 µm silica and (B) batch experiment data and fitting from PHREEQC for 0.45 µm silica.
TABLE 4. Parameters Estimated by Applying FITEQL to Titration Data and PHREEQC to Batch Data particles
surface site density data type (site/nm2) LOGKa1
sand
titration batch silica, 0.45 µm titration batch silica, 0.10 µm titration batch kaolinite, 2.70 µm titration batch kaolinite, 1.40 µm titration batch
20.00 27.90 1.41 21.70 2.50 10.22 6.40 59.55 6.80 42.10
LOGKa2
LOGKZn
7.47 -9.45 0.39 -11.52 0.75 21.01 9.52 21.01 9.52 11.55 22.05 10.73 22.05 10.73 12.50 5.33 -2.32 5.33 -2.32 -2.20 4.52 -2.76 4.52 -2.76 -2.90
TABLE 5. Parameters Used To Model Colloid and Contaminant Transport in Each Flume Experimenta run λf* # (-) 1 2 3 4 5 6
0.3 0.3 0.3 0.02 0.6 0.07
vs* (-) 0.0 0.0 0.0 0.0 0.3 0.23
ms (L/g)
Ac (m2/g)
8.70E-4 5.0314 1.97E-4 5.0314 2.30E-3 5.0314 1.22E-3 22.64 6.38E-4 0.8386 8.40E-4 1.6173
Sc (sites/ nm2) 21.70 21.70 21.70 10.22 59.55 42.10
isotherm types and parameters linear: mc ) 5.98E-2 L/g linear: mc ) 6.69E-5 L/g linear: mc ) 0.70 L/g linear: mc ) 0.56 L/g linear: mc ) 0.55 L/g Langmuir: Γmax ) 2.60 mg/g Kads ) 3.15 L/mg
a For all experiments, the sand properties were A ) 0.0045 m2/g and s Ss ) 27.90 sites/nm2.
analysis showed that linear adsorption isotherms were sufficient to describe the adsorption of Zn to silica sand in all flume results, to silica colloids in runs 1-4, and to Kaofill kaolinite in run 6. For these interactions, Table 5 lists the sorption capacities for the sand and colloids, ms and mc, at the pH of each experiment. The nonlinear Langmuir isotherm was required to describe the adsorption of Zn to KTCAST kaolinite in run 5, with the isotherm parameters given in Table 5. The difference in zinc adsorption to the two types of kaolinite is due to the difference in their surface properties. The Kaofill kaolinite was treated by the manufacturer with H2SO4, alum, sodium-hydrosulophite (bleach), sodium polyacrylate (dispersant), and soda ash, while the KTCAST kaolinite was only rinsed by the manufacturer. Several different methods were used to determine colloid deposition model parameters. The colloid filtration coefficients (λf*) used in runs 1-3 were determined by an inverse method, i.e., by fitting the colloid exchange data with the colloid exchange model of Packman et al. (21). This best-fit deposition parameter was then used in the calculation of 6576
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multiphase contaminant transport. In runs 4-6, filtration coefficients were determined by means of independent experiments, i.e., by applying the classical colloid filtration equation to independent column experiment results, following the methods described in ref 29. Colloid settling velocities (vs*) were determined from the measured particle sizes. The submicron silica particles used in runs 1-4 do not settle. For the kaolinite used in runs 5-6, the settling velocity was calculated from the measured effective kaolinite particle diameter using Stokes Law. For run 5, discrepancies were found because of the bimodal nature of particle size distribution (shown previously in Figure 2). The effects of this bimodal particle size distribution will be discussed in Section 4, Results. For run 6, a dimensionless colloid settling velocity of vs* ) 0.04 was calculated from the measured size distribution (dp ) 1.4 µm), but application of the colloid exchange model suggested a dimensionless colloid settling velocity of vs* ) 0.23 (dp ) 3.3 µm). Because this difference is within the range of experimental error of the particle sizing instrument, the best-fit settling velocity was used to predict contaminant transport.
4. Results 4.1. Typical Flume Experiment Results. Figure 4 shows typical results from one flume experiment with silica (run #4) and one with kaolinite (run #5). Results are presented as the normalized concentrations (C* ) C/C0) of conservative NaCl, dissolved zinc, total zinc, and colloidal particles in the stream plotted as a function of time. The in-stream concentrations of all injected substances decrease over time due to stream-subsurface exchange. Figure 4a shows zinc exchange in the presence of 0.10 µm silica colloids, and Figure 4b shows zinc exchange in the presence of 2.7 µm kaolinite particles. In Figure 4a, the concentration of 0.10 µm silica particles decreases less rapidly than the zinc concentration, which implies that zinc sorption to these colloids is expected to increase the mobility of zinc. On the other hand, Figure 4b shows that 2.7 µm kaolinite particles are removed from the stream more rapidly than zinc, and, as a result, the kaolinite particles should decrease zinc mobility. These results are of course dependent on the specific chemical conditions extant during these experiments. 4.2. Application of Colloid and Contaminant Transport Model. For each flume run, the salt concentration data were analyzed first using the finite bed exchange model of Packman et al. (21) in order to verify that pore water exchange followed expected behavior. Colloid data were then predicted and fitted if necessary using the particle deposition model of Packman et al. (21). The model predictions of colloid exchange were carefully evaluated since any errors in representing colloid exchange would propagate to the prediction of contaminant exchange. By ensuring that colloid
FIGURE 4. Typical flume experiment results: (A) run #4 and (B) run #5.
FIGURE 5. Comparisons of model predictions with flume results for runs with silica colloids. exchange was modeled well, any errors in predicting contaminant concentrations can then be specifically attributed to the multiphase contaminant transport model. It should be noted that both the colloid exchange model and the new contaminant exchange model can be used as purely predictive models, but the colloid deposition parameters were adjusted here to allow independent evaluation of the contaminant transport model. Zinc exchange data are compared with the model predictions in Figures 5 and 6. In general, the model predictions agree well with the flume experiment results. Total zinc exchange was predicted within 10% in all cases (Errors are reported here as absolute errors.). After simulating Zn exchange in the presence of 0.45 µm silica colloids (runs 1-3), the model was applied to the case for finer silica and more reactive colloids, runs 4-6. There is little colloidalphase contaminant transport in runs 1-3 because the colloid concentrations were low and zinc sorption was dominated by the interaction of Zn2+ with bed sediments. Both dissolved zinc and total zinc are generally predicted well by the new multiphase contaminant transport model. Note that total zinc was not measured in run 1. Total zinc exchange in the presence of 0.10 µm silica colloids was predicted very well
using the multiphase transport model, but the dissolved Zn concentrations were underpredicted by around 10%. We attribute this discrepancy to the incomplete separation of 0.10 µm silica colloids from the flume water samples so that the dissolved-phase Zn measurement includes some colloidal zinc. We found it is very difficult to separate the 0.10 µm silica colloids even with the high-speed centrifuge (7500 rpm). The separated colloids were easily resuspended, so that the supernatant can be expected to include some colloidal zinc. The model comparisons with the flume experiment data for runs #5 and #6 are presented in Figure 6. The model predictions for the exchange of Zn agreed well with the flume experiment results in run #5 and fairly well in run #6 (less than 10% discrepancy). However, the exchange of colloids was underpredicted in run #5 using the dimensionless colloid settling velocity of vs* ) 0.3 calculated from the mean particle size of 2.7 µm. When the colloid exchange model of Packman et al. (21) was applied, the flume experiment results suggested a dimensionless colloid settling velocity of vs* ) 1.2, which corresponds to a particle diameter of 5.5 µm. When vs* ) 1.2 was used to analyze the zinc exchange, both total and dissolved Zn exchange are overpredicted, as shown in Figure VOL. 38, NO. 24, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 6. Comparisons of model predictions with flume results for runs with kaolinite colloids.
FIGURE 7. Simulation of kaolinite deposition and zinc exchange in run #5 for vs* ) 1.2. 7. Thus, kaolinite and zinc exchange cannot be modeled simultaneously with a single colloid diameter. This apparent discrepancy indicates that both kaolinite and zinc transport are very sensitive to the size distribution of the clay colloids. KTCAST kaolinite has a bimodal size distribution, as shown in Figure 2. The experimental results can be explained by considering the distinct effects of the bimodal size distribution on kaolinite deposition and zinc transport. The larger of the two bimodal size classes dominates the colloid deposition behavior because it represents around 75% of the kaolinite mass. However, the smaller size class dominates the colloidal-phase zinc transport because it represents around 75% of the kaolinite surface area. Thus, the particles that carry the bulk of the colloidalphase zinc are significantly smaller and correspondingly more mobile than the bulk of the particle mass. Such a system cannot be modeled with a simple mean colloid diameter. The results presented in Figures 6 and 7 indicate that both colloid deposition and multiphase contaminant transport can still be adequately modeled when the two components of the bimodal colloid size distribution are considered. The colloid exchange observed in all runs is compared in Figure 8. The 0.10 µm silica colloids (run #4) were the most mobile, while the 2.7 µm kaolinite colloids (run #5) were the least mobile. The 0.10 µm silica colloids have a very low deposition rate because of their high mobility and low affinity for the bed sediment (low filtration coefficient). The deposition of 0.45 µm silica colloids was about the same for runs 1-3. The 1.4 µm kaolinite (run #6) deposited more than either size of silica colloids but less than the 2.7 µm kaolinite. The deposition of 2.7 µm kaolinite colloid is dominated by colloid 6578
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FIGURE 8. Comparisons of colloid exchange observed in all flume experiments. settling. The colloid exchange can be simulated well using the physicochemical model developed by Packman et al. (21), which shows that the colloid exchange in these flume experiments can be interpreted successfully in terms of bedform-induced advective pumping exchange, particle settling, and filtration. 4.3. Effects of Colloids on Total Zinc Exchange. The zinc exchange observed in all flume experiments is compared in Figures 9 and 10. The model predictions for zinc exchange in the absence of colloids indicate the amount of zinc that would be removed by stream-subsurface exchange with sorption to the streambed sediments only. As shown in Figures 9 and 10, colloid type, colloid concentration, particle size, stream pH, and the hydrodynamic exchange rate all affect zinc transport. Figure 9 clearly shows that pH has a significant effect on total zinc exchange in the presence of silica colloids. Zinc retention in the streambed increases with increasing pH, as expected from the knowledge of zinc sorption chemistry with these sediments. Zinc exchange in the presence of silica colloids is predicted well by the contaminant transport model, but the colloids have little effect on zinc transport in these experiments because zinc sorption occurs predominantly to the bed sediments. The increase in zinc exchange with increasing pH occurs because of the interaction between zinc and the bed sediments. Higher pH results in greater adsorption of zinc to the silica sand, with correspondingly more zinc immobilization in the streambed.
FIGURE 9. Effect of pH on total zinc exchange in the presence of 0.45 µm silica colloids. Figure 10 compares total Zn exchange in the presence of different types of colloids. Figure 10A shows that 2.7 µm kaolinite particles cause more zinc to be retained in the bed compared to 0.45 µm silica colloids at the same pH. Figure 10B compares total Zn exchange in the presence of 0.10 µm silica colloids (run #4) and 2.7 µm kaolinite (run #5). Total zinc exchange in run #4 is less than that in run #5, despite the fact that the pH is higher in run #4. The presence of kaolinite produces a substantial increase in zinc exchange because of the higher adsorption capacity of zinc for the kaolinite surface and also because of the greater deposition rate of kaolinite relative to silica colloids. As shown in Table 4, kaolinite particles have a greater surface site density than do silica colloids, which results in a greater adsorption capacity. In addition, kaolinite particles deposit more rapidly than silica colloids, as shown in Figure 8, which causes additional removal of colloidal-phase zinc. The combination of these processes is responsible for the overall strong effect of kaolinite on the exchange of zinc.
5. Discussion We studied zinc exchange with a sand streambed in the presence of colloidal silica and kaolinite using a recirculating laboratory flume. Hydrodynamic stream-subsurface exchange delivered both dissolved and colloidal-phase zinc to the streambed, where zinc was immobilized by a combination of sorption to the bed sediments and colloid deposition. Zinc was immobilized to a significantly greater extent in the presence of kaolinite particles than in the presence of silica colloids. These observations emphasize that reactive colloids can substantially mediate the stream-subsurface exchange of contaminants and that colloid deposition can provide a mechanism of contaminant immobilization that is generally
not considered in field studies of contaminant transport in streams. A new predictive model for the hyporheic exchange of colloids and contaminants was applied to relate observations of net transport behavior to the controlling physical and chemical processes. The required model input parameters were estimated from independent batch and column experiments, with the chemical reaction models FITEQL and PHREEQC used to analyze particle surface properties and zinc sorption behavior. Model predictions for total zinc exchange generally agreed well with flume experiment results, indicating that the physicochemical processes governing zinc transport were represented well by the multiphase reactive modeling approach applied along pore water flow paths. Model simulations showed that two distinct processes were responsible for the greater influence of kaolinite on zinc transport: zinc sorbed more strongly to kaolinite than to silica, and kaolinite deposited more rapidly in the sand bed than did silica colloids. The relative importance of these processes could not have been distinguished without use of the reactive-transport model with explicit separation of the physical and chemical processes that influenced net zinc transport. Zinc exchange and colloid deposition also depended greatly on the size distribution of the colloidal particles in each experiment. When the colloids had a significantly bimodal size distribution, the bulk of the colloidal surface area occurred in the smaller particle size class, while the bulk of the colloidal mass occurred in the larger particle size class. Because contaminant sorption depends strongly on the particle surface area, the smaller and more mobile particles had a greater influence on zinc transport than did the larger particles. The net result was that colloidal-phase zinc was much more persistent in the stream than would have been expected from observations of the bulk suspended sediment deposition. For this case, a simple model that predicts colloidal-phase contaminant deposition based simply on a mean particle settling velocity and an effective partitioning coefficient would have greatly overpredicted the actual zinc immobilization. This effect of the particle size distribution on contaminant transport is expected to be extremely significant in natural streams because natural colloidal particles are normally highly polydisperse. It should be noted that much of the work on colloid filtration has been derived with the assumption that particles have a uniform size. Advances in the understanding of the transport of polydisperse colloidal suspensions will also improve the analysis of contaminant transport behavior in streams and pore waters. The process-based model employed here was very useful for interpretation of the relative importance of contaminant sorption to colloids and bed sediment grains, colloidsediment interactions, and pore water transport in controlling
FIGURE 10. Effects of colloids on total zinc exchange. VOL. 38, NO. 24, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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the stream-subsurface exchange of zinc. The model assumptions were shown to be sufficient for analysis of zinc transport in the presence of silica and kaolinite colloids in a large laboratory flume system. However, a variety of issues limits the application of this type of model in natural systems. One critical limitation arises from the difficulty in characterizing both hydrodynamic transport and chemical reactions in heterogeneous natural river systems. Small-scale structural heterogeneity of streambed sediments has been shown to greatly influence both stream-subsurface exchange flux and pore water flow patterns (40). Larger-scale physical heterogeneity makes analysis of hyporheic exchange difficult over length scales of practical interest in streams (44, 45). Chemical conditions in sediments show similar variability, which makes it extremely difficult to determine average sorption properties or reaction rates (e.g. refs 46 and 47). Further, surfacechemical conditions and sorption properties have been shown to co-vary with physical sedimentary properties such as grain size (e.g. refs 48 and 49). Hyporheic zones also typically have strong gradients in a variety of important chemical conditions, such as redox conditions and pH, and the local conditions depend on both the hydrodynamic environment and sedimentary biogeochemical processes (e.g. 50-54). All of these effects present substantial difficulties in estimating required model input parameters in natural systems. Because both pore water fluxes and contaminantsediment interactions are influenced by the sediment structure, net contaminant exchange cannot, in general, be adequately parametrized in terms of interactions with the bulk sediments (i.e., with model input parameters based on batch experiments with homogenized sediment samples). In addition to the major challenge posed by the heterogeneity of natural systems, a wide range of additional processes including particle aggregation, precipitation/ dissolution reactions, and a variety of biochemical processes are also expected to influence contaminant transport in natural streams but have not been adequately represented in any stream-subsurface exchange model. Further, while zinc sorption could be adequately considered to be a reversible equilibrium process here, slow desorption kinetics will often provide a key control on contaminant release from natural sediments (55, 56). Clearly, fundamental, predictive, process-based modeling of contaminant transport in streams remains a long-term research objective instead of an immediate possibility. Nonetheless, one clear implication of this study is that explicit reactive-transport modeling along flow paths is required to relate net contaminant fluxes to driving system variables such as the streamflow rate, background chemical conditions, and sediment composition. At the current time, such models are extremely useful for heuristic purposes, such as the study of the interplay of physical and chemical processes under controlled conditions or assessment of controls on local-scale reactive transport in relatively simple natural systems. Additional effort will be required to adequately parametrize models for general application to environmental problems such as analysis of contaminant spills, assessment of releases from contaminated sediments, and design of contaminant remediation schemes.
Acknowledgments This work was executed at Northwestern University and supported by the National Science Foundation via CAREER award BES-0196368. The authors would like to thank JeanFranc¸ ois Gaillard for providing access to analytical instruments, Douglas Jerolmack and Kristin Rehg for their help in conducting some of the experiments, and Peter Wightman for assistance with the analysis of surface titration data. 6580
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Received for review October 1, 2003. Revised manuscript received June 16, 2004. Accepted September 9, 2004. ES035090X
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