Environ. Sci. Technol. 2001, 35, 4684-4690
Impacts of Heterogeneous Organic Matter on Phenanthrene Sorption: Different Soil and Sediment Samples H R I S S I K . K A R A P A N A G I O T I , * ,† JEFFREY CHILDS,‡ AND DAVID A. SABATINI‡ ICEHT/FORTH, Institute of Chemical Engineering and High-Temperature Processes, Foundation for Research and Technology Hellas, Patra, 26500, Greece, and School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, Oklahoma 73019
Organic petrography has been proposed as a tool for characterizing the heterogeneous organic matter present in soil and sediment samples. A new simplified method is proposed as a quantitative means of interpreting observed sorption behavior for phenanthrene and different soils and sediments based on their organic petrographical characterization. This method is tested under singe solute conditions and at phenanthrene concentration of 1 µg/L. Since the opaque organic matter fraction dominates the sorption process, we propose that by quantifying this fraction one can interpret organic content normalized sorption distribution coefficient (Koc) values for a sample. While this method was developed and tested for various samples within the same aquifer, in the current study the method is validated for soil and sediment samples from different sites that cover a wide range of organic matter origin, age, and organic content. All 10 soil and sediment samples studied had log Koc values for the opaque particles between 5.6 and 6.8. This range of Koc values illustrates the heterogeneity of opaque particles between sites and geological formations and thus the need to characterize the opaque fraction of materials on a site-bysite basis.
Introduction Research has demonstrated that chemical phase partitioning into soil or sediment organic matter (1, 2) is a satisfactory model for sorption of nonionic contaminants as these compounds approach their water solubility (3-6). However, several studies on the sorption of single solutes indicate that, at low relative concentrations, the solute sorption may become nonlinear with much higher sorption coefficients (selected refs 3-13). At low relative concentrations, nonlinear sorption is observed and surface adsorption is proposed as the possible sorption mechanism, whereas at high relative concentrations partitioning is dominant (3, 4). It is also found that in multiple-solute systems, the nonlinear sorption effect of a given solute can be greatly attenuated because of solute competition (3, 8, 12). Several researchers have proposed the existence of multiple organic matter domains (14-17), * Corresponding author present address: Department of Marine Sciences, University of the Aegean, Sapphous 5, 81100, Mytilene, Greece. Phone: (+30942) 464763; e-mail:
[email protected]. † ICEHT/FORTH. ‡ University of Oklahoma. 4684
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but only limited effort has attempted to quantify these different domains. Chiou (7) and other researchers suggest that the presence of small amounts of high surface area carbonaceous material (e.g., charcoal) may be responsible for the observed nonlinear sorption, even when present in small amounts (7, 4, 18, 19). Gustafsson et al. (20) suggested that a soot-carbon fraction in soil organic carbon can also affect soil sorption behavior. Ghosh et al. (21) observed that, although coal comprised only 5% of the sample, it sorbed 62% of the solute added. Thus, the influence of the organic particle fraction (e.g., charcoal or soot) on the contaminant sorption behavior depends on the contaminant concentration range, the amount of the organic particles present, and the concentration of coexisting contaminants. Even considering these factors, it is significant to elucidate the nature of organic particles in soils and sediments with a validated analytical method. Organic petrography has been proposed as such a method for characterizing the organic matter of different samples under the microscope. Previous studies related the types of organic matter with equilibrium sorption properties (18, 19, 5). Gustafsson et al. (20) predicted composite sorption distribution coefficient (Kd) values by including both a Kd value due to the soil organic matter and another Kd value due to the soot carbon fraction. Njoroge et al. (22) proposed an additive sorption model that predicted Kd values by assuming that there are two different types of organic matter, referred to as shallow-like and deep-like organic material. Karapanagioti and Sabatini (5) attempted to quantify the cumulative organic content normalized sorption distribution coefficient (Koc) value for samples by considering the sorption properties of organic matter subgroups within each sample. By summing the products of the subgroup Koc values (Koc OMi) and particle fractions [OMi, estimated based on the relative number of particles (% OMi ) OMi/OMb)], the cumulative Koc value for each sample was estimated using
Koc )
∑ % OM K i
oc OMi
(1)
where the indices i and b are used to distinguish individual subgroup and bulk samples, respectively. This approach was used to determine cumulative Koc values for sediment samples from different depths at a given site. The values predicted were within the 95% confidence interval for the experimentally determined log Koc values. Ideally, this Koc prediction method could be extended to Kd prediction. By using literature values for the organic carbon fraction found in each organic matter subgroup, one could predict the fraction of organic carbon content (foc) found in each organic matter subgroup (23). However, additional knowledge is required on the sorption properties and organic carbon distribution in organic matter subgroups before this method can be applied. A limitation of the Karapanagioti and Sabatini (5) study is that the samples studied were all from the same site; while the distribution of organic matter subgroups varied between samples (depths), the nature of organic matter in each subgroup was fairly similar. The objectives of the present study were as follows: (1) to characterize organic matter heterogeneity in soils and sediments from different sites, (2) to quantitatively interpret Koc values for each soil or sediment based on organic petrography characterization of organic matter subgroups, and (3) to evaluate the ability to extrapolate petrographic and sorption data from one site to soils and sediments from different areas that cover a wide range of organic matter 10.1021/es010654n CCC: $20.00
2001 American Chemical Society Published on Web 10/25/2001
TABLE 1. Sediment Information sediment name
selected refs
texture
Florida Peat
(3, 6)
peat soil
Everglades, FL
Woodburn Cheshire Wagner CRAb14-16
(3) (8, 11) (9) (5)
fine-silty soil fine sandy loam sand sandy loam
CRAb28-30
(5)
medium sand
DGSLc OSCLd DGSe Duke Forest
(10, 4, 12) (10) (10) (10)
dark gray silt loam orange silty slay loam sand sand
Corvallis, OR Hamden, CT MI, subsurface sample Norman Landfill, OK collected at ∼5 m below surface Norman Landfill, OK collected at ∼10 m below surface Dover, DE collected at ∼15 m below surface Dover, DE collected at ∼14 m below surface Dover, DE collected at ∼8 m below surface Duke Forest, NC horizon
a f : fraction organic carbon content. b CRA: Canadian River Alluvium. c DGSL: dark gray silt loam. oc Dover gray sand. f IHSS: International Humic Substances Society.
origin, age, and organic content. Samples were selected based on previous research (by other researchers; see Table 1) that demonstrated nonlinear and/or nonequilibrium sorption, processes we are trying to capture in our approach. We propose a model that utilizes organic petrography properties, which are easily quantified to interpret Koc values for a given sample. This study is unique in not only characterizing the organic matter of different soils and sediments and quantitatively interpreting their sorption behavior based on the soil or sediment organic petrographical properties but also because it further evaluates this model for soil and sediment samples substantially different from those used by Karapanagioti and Sabatini (5) to originally validate the model. It should be noted that this model has been used for single solute conditions and for low solute concentrations relative to its solubility.
Materials and Methods Samples with a wide range of origin, age, and fractions of organic carbon content (foc) were selected for this present study. Table 1 presents the sample names for soils and sediments analyzed in this study. Table 1 also lists publications where characteristics and sorption properties of these samples have been studied before, the place of origin, the texture, and foc values, all of which are of interest for the present study. Phenanthrene was used as the model chemical and equilibrium sorption studies were conducted. Phenanthrene was prepared as a 100 mg/L stock solution in methanol, so that the percentage of methanol introduced in each sample was always below 1%. Test solutions were prepared in synthetic groundwater (deionized water with 44 mg/L CaCl2‚2H2O, 14 mg/L CaSO4, and 17 mg/L NaHCO3). Sodium azide (NaN3) was added at 200 mg/L to inhibit bacterial growth and thus biodegradation during batch studies. Aqueous phenanthrene concentrations were measured by an RF-551 Shimadzu variable wavelength fluorescence detector in cuvette mode. For each batch experiment triplicate blank samples were prepared and monitored (i.e., phenanthrene without soil or sediment). These blank samples did not indicate any significant phenanthrene degradation or sorptive losses on the glassware for the duration of the experiment; similar results were found in our previous work (19, 5). Soil and sediment blank samples without phenanthrene verified that the sorbent did not generate peaks in the same wavelength as phenanthrene. In our previous studies some samples were analyzed both in the HPLC and cuvette mode with both a UV and a fluorescence detector; these readings showed no significant differences (19, 5). All equilibrium sorption experiments were conducted in triplicate in 10 mL crimp-top glass vials. Soil or sediment
foca (%)
collected and characterized by
origin
d
reference sample from the IHSSf (30, 31) (8, 33) (33-35) ( 5)
49 1.3 1.4 0.15 0.20
( 5)
0.026
(36, 37) (36, 37) (36, 37) (38, 22)
1.5 0.15 0.023 0.54
OSCL: orange silt clay loam. e DGS:
samples for equilibrium studies were pulverized to ensure equilibrium in a reasonable time (7 days). Kleineidam et al. (18) demonstrated that phenanthrene sorption is organic matter-dominated, and thus that pulverization does not impact sorption uptake. Variable phenanthrene concentrations were added to the soil and sediment samples and shaken for 7 days in the dark at room temperature (around 23 °C). Headspace in the vials was kept to a minimum. Solid-towater ratios were varied to account for differing sorption capacities of the subsamples (i.e. to achieve sufficient sorption so that it could be easily quantified while keeping aqueous concentration above the detection limit). These methods have been presented in detail in previous studies (18, 19, 5). Four of the samples (i.e. DGSL, OSCL, DGS, Duke Forest) were tested by other researchers, and details have been given elsewhere (10, 4, 12). In these studies (i.e. 10, 4, 12), the sorption experiments were conducted in glass centrifuge tubes with aluminum foil facing added to the inside of PTFElined solid phenolic screw caps. An amount between 0.01 and 8 g of sorbent was added to each centrifuge tube, followed by the addition of synthetic groundwater containing 0.005 M CaCl2 and 0.02% NaN3 (by weight). The sorption tubes were mixed end-over-end for 7 days at room temperature (22 ( 1 °C). After the completion of each experiment, the samples were centrifuged at 2500 rpm (about 550 g) for 30 min, and between 1 and 2 mL of the supernatant was taken for scintillation counting. The 14C activity was determined by injection into scintillation fluid followed by counting (Model LS 3801, Beckman Instrument, Fullerton, CA).
Organic Petrography Methods The organic matter of the different soil and sediment samples was isolated and strew slides of the concentrated organic matter were prepared by Global Geolab Limited, Alberta, Canada. Organic matter concentrates were prepared using hydrochloric acid (10%) to remove the carbonates and hydrofluoric acid (70%) to remove the silicates, leaving an organic residue for microscopic examination. Mineral residues were separated from organic matter by centrifuging (2000 rpm; 1000 g), ultrasonic vibration, and by zinc bromide flotation (ZnBr2 has a specific gravity of 2). Organic matter isolates were mixed with polyvinyl alcohol and mounted on a glass slide for visual observation. Microscopic investigations were conducted at the University of Oklahoma using a Vickers M17 Research Microscope. Organic matter was identified and characterized using the microscope in a white transmitted light mode. The percentages of each organic matter subgroup were determined using a point counter; between 530 and 890 particles were counted per slide. For our samples, we observed that counting over 500 particles in several VOL. 35, NO. 23, 2001 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 1. Palynological kerogen classification scheme for organic mater subgroups present in this study (for more details see ref 24). This is a simplified version of the scheme presented in Karapanagioti and Sabatini (5). sections of the slide reduced the variability in the measured fraction opaque particles (i.e., by this number of counts the cumulative variation is reduced to 3% or lower). While this approach produced reproducible values in our research, this may vary for other samples. Since this method is used to interpret the sorption behavior of the samples and not for quantitative predictions, the reproducibility and accuracy have not been further evaluated. These issues should be evaluated in future research. The organic matter classification used in this study is based on the palynological characterization scheme for organic matter in thermally immature to marginally mature sediments, as proposed by Tyson (24). Figure 1 summarizes the
palynological classification scheme used, which is a simplified version of the method proposed in Karapanagioti and Sabatini (5). The organic matter found in the samples is divided into two main groups: (a) structureless formations, called the “amorphous group” and (b) structured particles, both translucent (observable at particle edge where section is thin enough to observe) and opaque (even at particle edge) called the “phytoclast group”. The amorphous group is either heterogeneous, consisting of a truly amorphous matrix derived from phytoplankton or bacteria, or intra- and extracellular amorphous material produced by biodegradation of land plants. The translucent particles of the phytoclast group include recent woody fragments as well as spores, pollen, and cuticles. The opaque particles of the phytoclast group are oxidized or carbonized woody tissues including charcoal. In this study the structured, opaque particles are called opaque particles, and the rest of the organic matter is grouped together as nonopaque particles. If a particle is observed to be partly opaque, then it is counted as nonopaque. Figure 2 presents photomicrographs illustrating these two different organic matter groups. Nonopaque particles are easy to identify in these photomicrographs, but opaque particles are more difficult to identify. Since strew slides are not polished surfaces, it is not possible to focus on the whole particle at one time. Portions of the opaque particles that are not in focus may appear nonopaque in the photomicrographs. However, with the microscope one can focus onto different depths and verify that the entire particle is opaque. This illustrates the importance of sample analysis and classification on the microscope rather than relying on photomicrographs. In addition, these pictures do not dem-
FIGURE 2. Photomicrographs illustrating opaque and nonopaque particles present in selected sediment samples: (a) nonopaque particles found in DGS and (b) opaque particles found in Duke Forest sample, opaque particle among nonopaque particles found in (c) Cheshire and (d) Florida Peat. All pictures were taken with a Vickers M17 Research Microscope in transmitted white light mode, oil immersion, and magnification of 200. The field of view is 0.320 mm in width. Organic matter has been isolated from the mineral matter and is mounted on strew slides with poly(vinyl alcohol), which gives the yellow background. Note that the strew slides are not polished surfaces, and thus it is not possible to focus on the whole particle. Parts of the opaque particles not in focus may appear nonopaque in these photomicrographs. This uncertainty is artificial; with the microscope one can focus onto different depths and verify the opacity of the whole particle. 4686
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onstrate the details presented in Karapanagioti et al. (19), since the method used in the present study is a simplification of the one used in this previous publication. Limitations of using strew slides and transmitted white light for characterization are discussed in detail elsewhere (5). The basic uncertainty of the method is that differentiation between opaque and nonopaque is accomplished by looking at the particle edges. In a strew slide the thickness of the particles vary. On the other hand, one can focus at different depths and limit this uncertainty. Despite these limitations, this simpler method can provide valuable information for interpreting and predicting sorption properties, as presented in Karapanagioti and Sabatini (5) and further demonstrated below.
Data Modeling In this study nonlinear isotherms are described by the Freundlich equation, which is a widely accepted model for describing nonlinear sorption of heterogeneous materials. For nonlinear isotherms, the sorption distribution coefficient Kd (and thus, the organic content normalized sorption distribution coefficient Koc) is concentration-dependent. In this approach, Koc is calculated at a given chemical concentration (18, 19, 5) as
Koc ) Kd/foc
(2)
where foc is the fraction organic carbon content of the sample and Kd is the sorption distribution coefficient at the given concentration. The sorption distribution coefficient Kd can be described by
Kd ) qe/Ce
(3)
(4)
where Kfr is the Freundlich sorption constant [(µg/Kg)(L/ µg)N] and N is the Freundlich exponent. For nonlinear isotherms Kd can be considered concentration-dependent as follows
Kd ) KfrCeN-1
(5)
At a chemical concentration of unity (i.e., 1 µg/L), Kd equals Kfr and thus
Koc ) Kfr/foc
to opaque particles when it is applied to low relative concentrations. The validity of this approach for a wide range of soils and sediments is the subject of this paper. In this approach, it is assumed that opaque particles are freely accessible for sorption. Opaque particles are usually highly oxidized particles where only the basic structure remains (24). The solute will not be required to diffuse through other organic matter types as happens with the less oxidized material (i.e., the nonopaque particles). In previous studies (5), we have observed the presence of organic aggregates in the samples prior to pulverization. In the same study we found that the nonopaque organic matter (i.e., lignin, humic acids, etc.) demonstrated linear isotherms. This means that even if lignin blocks the surface of an opaque particle it would not be the limiting factor in terms of capacity.
Results and Discussion
where qe is the mass of chemical sorbed per unit mass of soil (µg/Kg) and Ce is the equilibrium concentration (µg/L). Nonlinear isotherms can be described by the Freundlich equation
qe ) KfrCeN
FIGURE 3. Phenanthrene sorption isotherms. Phenanthrene in the sorbed phase [qe (µg of phenanthrene/Kg of sediment)] versus phenanthrene equilibrium aqueous concentration [Ce (µg/L)]. CRA: Canadian River Alluvium.
(6)
In our previous studies opaque particles accounted for the majority of the sorption (19, 5). To simplify the petrographical analysis, Karapanagioti and Sabatini (5) recommended that only the percent opaque particles be analyzed in each sample with sample Koc values interpreted using a simplified form of eq 1
Koc ) % OMopKoc op + (1 - % OMop)Koc partitioning (7) where % OMop is the fraction of opaque particles in the sample, Koc op is the Koc value for opaque particles of the sample, and Koc partitioning is the Koc value assigned to the remainder of the sample organic matter. Equation 7 simplifies the petrographical characterization and demonstrates the importance of the opaque particles in interpreting the sorption behavior of a sample. This equation can be used to determine the maximum sorption affinity of a sample due
Equilibrium Sorption Isotherms. Figure 3 presents results of the isotherm studies performed during this research. The Freundlich model was used to fit the isotherm data. Table 2 presents the isotherm constants derived from these equilibrium experiments. Whereas the foc of these samples vary by 3 orders of magnitude, the sorption capacity {Kfr in [(µg/Kg)(L/µg)N]} varies by 4 orders of magnitude, suggesting the presence of different types of organic matter in the various soils and sediments. Note that for soils or sediments where others had already quantified phenanthrene sorption we did not repeat the isotherms here (10, 4, 12). However, while revising the present paper, a study was published by Braida et al. (25) that included data on phenanthrene sorption with two of the sorbents tested in the present work (Florida peat named Pahokee peat in Braida et al. (25) and Cheshire). Results from Braida et al. (25) are presented and discussed along with the data determined in the present study. The isotherm results follow the same general trends observed in previous studies with CRA samples (19, 5). However, including samples with widely varying origins increased the data scattering. One example is presented in Figure 4, where an inverse relationship is observed between log Koc (at equilibrium concentration equal to 1 µg/L) and N. This result suggests that as the sorption affinity increases the mechanism changes from primarily partition to soil organic matter to mainly a surface adsorption onto a small amount of opaque particles (14, 26, 3, 4). CRA subsamples from a single sample followed this inverse trend very closely (19), while CRA samples from different locations and depths followed the trend but with more spread in the data (5). Samples evaluated in this study also follow this global trend, but increased data scattering is observed relative to previous studies (see Figure 4), further illustrating the impact heterogeneous organic matter has on sorption. Table 3 presents log Koc values calculated or experimentally observed at equilibrium concentration equal to 1, 5, 10, 50, VOL. 35, NO. 23, 2001 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 2. Isotherm Resultsk sample Florida peat (Pahokee peat) i Woodburn Cheshire Wagner CRAf14-16g CRAf28-30g
foca (%) 49 (44)i 1.3 1.4 0.15 0.20 0.026
Nb
Kfrc (µg/Kg) (L/µg)N
obsd
R2
Ce rangee in µg/L
0.34 ( 0.031 (0.62)h (0.73)i 0.60 ( 0.022 0.87 ( 0.032 (0.79)i 0.86 ( 0.024 0.74 ( 0.023 0.89 ( 0.028
230000 ( 28000 (71000)h (53000)i 990 ( 91 2800 ( 250 (900)i 82 ( 7.5 180 ( 18 10 ( 1.2
15 (12)h
0.90 (0.99)h (0.99)i 0.98 0.98 (0.99)i 0.99 0.99 0.99
0.13-210 (13-210)h (0.3-180) j 4.1-280 1.8-85 (2-770)j 5.3-210 27-220 37-130
14 15 15 15 12
a f : fraction organic carbon content. b N: Freundlich exponent. c K : Freundlich constant. d Obs: number of observations for each isotherm. oc fr Ce range: range of equilibrium concentrations experimentally observed for each samplein µg/L. f CRA: Canadian River Alluvium. g Data for these samples are taken from Karapanagioti and Sabatini (5) (determined in our laboratory in previous research using the same technique). h Values in parentheses calculated when the nonlinearity caused by the lower value is disregarded. i Data presented in ref 25. j Data provided by Braida and Pignatello (39). k Note: ( corresponds to ( standard deviation. e
TABLE 3. Organic Carbon Normalized Sorption Coefficient (Koc) Calculated at Different Phenanthrene Chemical Equilibrium Concentrations (Ce) µg/L
FIGURE 4. Logarithm of organic content normalized distribution coefficient (log Koc) versus Freundlich exponent (N) at chemical equilibrium concentrations (Ce) equal to 1 µg/L. Diamonds correspond to subsamples used in Karapanagioti et al. (19), whereas circles correspond to bulk samples used in Karapanagioti and Sabatini (5). Triangles correspond to samples evaluated in this study and squares to samples from Xia (10). 100, 200, and 1000 µg/L. From Table 3 it is observed that there is a decreasing trend of Koc values with increasing equilibrium concentration, as observed in previous studies (3, 4, 19). Thus, in agreement with earlier observations and the nonlinear nature of the isotherm, with decreasing contaminant concentration the sorption mechanism changes from primarily a partitioning with soil organic matter to mainly a surface adsorption onto a small amount of opaque particles (14, 26, 3, 4). Sediment Analysis. Table 4 presents the percent of opaque particles quantified for each soil or sediment sample. Most soil and sediment samples contained all the various types of organic matter discussed above, including amorphous organic matter, nonopaque phytoclasts, and opaque particles. Sample DGS contained only amorphous organic matter and nonopaque phytoclasts and sample OSCL contained mainly amorphous organic matter and traces of opaque and nonopaque phytoclasts. Figure 2 demonstrates the difference in opacity, size, and shape of the opaque and nonopaque particles, for these widely varying samples. Koc Interpretation. Table 4 summarizes a number of parameters and properties of the soils or sediments and sorption isotherms, including the following: (a) the percent of opaque particles measured with the organic petrography method outlined in the method section, (b) the experimentally observed or calculated log Koc at equilibrium phenanthrene concentration of 1 µg/L, (c) the log Koc op necessary to predict the log Koc value from (b) based on eq 7 and assuming log Koc partitioning equal to 4.1 (as in ref 5 and discussed below), at equilibrium phenanthrene concentration of 1 µg/L, (d) the log Koc predicted based on eq 7 assuming log Koc partitioning and log Koc op equal to 4.1 and 5.8 at equilibrium phenanthrene concentration of 1 µg/L, respectively (as in 4688
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sample
1
5
10
50
100
200
1 mg/L
Florida peat Woodburn Cheshire Wagner CRAa14-16b CRAa28-30b
5.7(5.2)c
5.2 4.6 5.2 4.6 4.8d 4.5d
5.0 4.5 5.2 4.6 4.7d 4.5d
4.6 4.2 5.1 4.5 4.5 4.4
4.4 4.1 5.0c 4.5 4.4 4.4
4.2 4.0 5.0c 4.4 4.4 4.3c
3.7d(4.1)c 3.7d 4.9d 4.3d 4.2d 4.3d
4.9d 5.3d 4.7d 5.0d 4.6d
a CRA: Canadian River Alluvium. b Data for these samples are taken from Karapanagioti and Sabatini (5) (determined in our laboratory in previous research using the same technique). c Values in parentheses calculated when the nonlinearity caused by the lower value is disregarded. d These values were extrapolated based on Freundlich isotherm constants presented in Table 2.
ref 5), (e) the experimentally observed or calculated log Koc at equilibrium phenanthrene concentration of 1 mg/L, and (f) the log Koc op necessary to predict the log Koc value from (b) based on eq 7 assuming log Koc partitioning equal to the log Koc at equilibrium phenanthrene concentration of 1 mg/L from (e), at equilibrium phenanthrene concentration of 1 µg/L. Several important observations can be made from the data in Table 4. We will begin by evaluating log Koc values for an equilibrium phenanthrene concentration of 1 µg/L for two reasons: (1) since the isotherms are nonlinear, Koc is concentration dependent and must be evaluated at a given concentration. The equilibrium concentration value of 1 µg/L is used as a standard value in order to compare the present values with previous research (18, 19, 5), and (2) according to Xia and Ball (4), for relative phenanthrene concentrations (Csolution/Csolubility) of 0.00083 (equal to 1 µg/L) the sorption mechanism is a combination of a surface adsorption component and a partitioning component. At this low relative concentration the surface adsorption sites are not saturated, and, thus, it is meaningful to use an additive Koc prediction model. Effect of Ce Range on Sorption Isotherms. Most of the Koc values at 1 µg/L are estimated based on the Freundlich equation with constants determined at concentrations of the same order of magnitude. The range of log Koc values at 1 µg/L (4.1-5.7) is consistent with literature data (e.g., 4.36.0 in ref 27) if they are calculated at the same Ce (1 µg/L) and in the same units (µg/Kg)(L/µg). Florida peat and samples tested by Xia (10) include concentrations lower than 1 µg/L. When the nonlinearity caused by the lower value is disregarded for the Florida peat sample, the Koc value at 1 µg/L is lower than the Koc value calculated if the lower concentra-
TABLE 4. Organic Petrography Results and Koca Predictions at Equilibrium Phenanthrene Concentration (Ce) of 1 µg/L
sediment
focd (%)
Florida peat 49 Woodburn 1.3 Cheshire 1.4 Wagner 0.15 CRAg14-16f 0.20 CRAg28-30f 0.026 DGSLh,i 1.5 OSCLj,i 0.15 k,i DGS 0.023 Duke Foresti 0.54
log Koca log Koc opc value necessary to predict the overall at log Koca predicted using % log Koca using eq 4 and eq 4 log Koc partitioningb ) opaque Ce ) 1 log Koc partitioningb ) 4.1f 4.1f and log Koc opc ) 5.8f particlese µg/L 8.1 7.5 5.8 3.1 14 5.0 6.4 0.2 0 9.8
5.7 4.9l 5.3l 4.7l 5.0l 4.6l 5.6 4.3 4.1 4.7
6.8 6.0 6.5 6.1 5.8 5.7 6.8 6.6 5.6
4.8 4.8 4.7 4.5 5.0 4.6 4.7 4.1 4.1 4.9
log Koca at Ce ) 1 mg/L
log Koc opc value necessary to predict the overall log Koca using eq 4 and log Koc partitioningb ) log Koca at Ce ) 1 mg/L
3.7l(4.1)m 3.7l 4.9l 4.3l 4.2l 4.3l 4.8 3.9 4.1 4.2
6.8 6.0 6.3 6.0 5.8 5.6 6.7 6.8 5.6
a K : organic carbon normalized sorption coefficient calculated at chemical equilibrium concentration (C ) equal to 1 µg/L. b Log K oc e oc partitioning ) 4.1 is from ref 5. Koc partitioning: organic carbon normalized sorption coefficient for nonopaque particles. c log Koc op ) 5.8 is from ref 5. Koc op: organic d e carbon normalized sorption coefficient for opaque particles. foc: fraction organic carbon content. % Opaque particles: Number of opaque particles per total number of organic particles counted. f Data for these samples are taken from ref 5. g CRA: Canadian River Alluvium. h DGSL: dark gray silt loam. i Data for these samples are taken from ref 10. jOSCL: orange silt clay loam. k DGS: dover gray sand. l These values were extrapolated based on Freundlich isotherm constants presented in Table 2. The rest of the values in this column were experimentally measured. m Values in parentheses calculated when the nonlinearity caused by the lower value is disregarded.
tion results are considered (Table 3). This suggests that the Koc values for the other samples at 1 µg/L might be even higher than the values calculated in Table 3. For Florida peat soil, Chiou and Kile (3) have observed that surface adsorption reaches a maximum at low concentrations and that linear behavior dominates at high concentrations (note the higher N value in Table 2 when the lower concentration data in the Florida peat experiment are not considered). Xia and Ball (4) have observed similar isotherm trends in their data and have determined an equilibrium concentration where the surface sorption reaches its maximum. The concentration-dependent nature of the sorption isotherms may also account for variations in sorption parameters measured in this research and recently reported in Braida et al. (25). For example, for the Florida Peat (Pahokee Peat) sample Braida et al. (25) reported Kfr and N values more similar to those determined for our higher phenanthrene concentration range. At the same time, variations in the foc of the two samples may suggest that heterogeneities present in differing batches of the peat material are responsible for the variation. While the exact reasons for the variation are unclear, the impact of opaque particles on contaminant sorption is reinforced. Selection of Koc partitioning Values. According to Chiou and Kile (3) and Xia and Ball (4) for concentrations close to solubility (i.e. 1 mg/L for phenanthrene) the Koc value represents the Koc partitioning. Initially, the value used for log Koc partitioning in Table 4 was 4.1, as was used in Karapanagioti and Sabatini (5). This value was a good representation of the partitioning fraction in Karapanagioti and Sabatini (5). Also, Laor et al. (28) reported values between 3.9 and 4.4 for sorbing media where partitioning is expected to dominate (i.e. humic acids) and isotherms are linear. Kleineidam et al. (18) and Karapanagioti et al. (19) reported log Koc values lower than 4.7 for humic substances at equilibrium concentration equal to 1 µg/L. Karapanagioti et al. (19) reported log Koc values between 4.1 and 5.1 for all sorbent materials at equilibrium concentration equal to 1 mg/L. For the samples tested in the present study, log Koc range of 3.7-4.9 was determined based on the Freundlich constants in Table 2 at equilibrium concentration equal to 1 mg/L. Xia (10) experimentally provided a similar range of log Koc partitioning values for phenanthrene (3.9-4.8). In the present analysis, Koc op is estimated using log Koc partitioning values equal to (a) 4.1 as in Karapanagioti and Sabatini (5) for all samples and (b) log Koc values of each sample calculated or experimentally observed at equilibrium concentration equal to 1 mg/L (i.e., 3.7-4.9)s
FIGURE 5. Logarithm of organic content normalized distribution coefficient (log Koc) versus percent of opaque particles present in each sample. The solid and dashed lines are from eq 4 using log Koc op values of 5.8 and 6.7, respectively. see last two columns in Table 4. Using these two different values for Koc partitioning, the resulting Koc op values are quite similar (see fifth and eighth column in Table 4). This illustrates that at low concentrations the partitioning fraction is not as significant. Koc op Interpretation. Relative to Koc op values it is instructive to look at similar data from Kleineidam et al. (18) and Karapanagioti and Sabatini (5), who reported log Koc op values of 6.3-6.7 and 5.8, respectively. Bucheli and Gustafsson (29) reported log Koc op values between 5.7 and 7.0 for soot particles which have similar sorption behavior but different physicochemical properties. All 10 sediments in the present study have log Koc op values between 5.6 and 6.8 and are thus consistent with the above values from the literature. This order of magnitude variation in Koc op values illustrates that even the opaque particle subgroup of organic matter is heterogeneous in nature. These results also illustrate that, while Karapanagioti and Sabatini (5) were able to use a single Koc op value for different samples at a given site, it is not possible to use this single Koc op value across different sites (with different source materials, activation scenarios, etc.). Figure 5 illustrates this variation in Koc op values between samples and as a function of % opaque particles. The lines illustrate that all 10 samples fall between estimates based on the above Koc op values. Thus, we not only see a high variability in Koc op values at different concentrations, but the current research shows that Koc op values vary by as much as an order of magnitude for different sites (for phenanthrene concentrations of 1 µg/L). Thus, at this point in time it is logical to either determine sample specific Koc op values for a given site and geologic VOL. 35, NO. 23, 2001 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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formation or evaluate a range of Koc op values, as identified in this research, if site specific Koc op values are not known. Nonetheless this work reinforces the importance of opaque particles in establishing the sorption characteristics of a soil or sediment sample. Correlating Koc op values to the origin/ history of organic matter and surface area of its opaque constituents should be the subject of future research.
Acknowledgments This study was partially funded by the U.S. National Science Foundation through the EPSCoR program. Four of the samples have been studied by Xia G. in his Ph.D. Dissertation (10). The authors would like to thank Gavin James and Tuan Van Doan for their help with the laboratory experiments and Cary Chiou, Joseph Pignatello, Martin Johnson, and Bill Ball for providing the soil and sediment samples studied here. We are also grateful to Bill Ball and Cary Chiou for their comments during the review process of this manuscript. The authors would also like to thank Peter Grathwohl and Sybille Kleineidam (Universita¨t Tu ¨bingen, Germany) for introducing us to organic petrography as a characterization tool and for helping guide us in the application of this tool. The authors would especially like to thank Brian Cardott (Oklahoma Geological Survey) for his help in identifying the organic petrography of these samples and for providing us with access to his Vickers M17 Research Microscope. The lead author would also like to acknowledge the support of Vasilis Burganos (ICEHT/FORTH) for allowing me to continue pursuit of this research area.
Nomenclature b
as an index refers to the bulk sample
C
chemical aqueous concentration
Ce
equilibrium aqueous concentration of chemical (µg/L)
foc
fraction organic carbon content (%)
i
as an index refers to individual organic matter subgroups
Kd
sorption distribution coefficient (L/Kg)
Kfr
Freundlich sorption constant [(µg/Kg)(L/µg)N]
Koc
organic content normalized distribution coefficient (L/Kg)
Koc OMi
Koc value assigned to each organic matter subgroup
Koc op
Koc value assigned to opaque particles
Koc partitioning Koc value assigned to the partitioning media of the sample Kow
octanol-water partition coefficient
OM
as an index refers to organic matter subgroups as opposed to the whole sample
% OMi
fraction of each organic matter subgroup in the sample
N
Freundlich exponent
qe
mass of chemical sorbed per mass of sediment (µg/Kg)
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Received for review February 16, 2001. Revised manuscript received July 23, 2001. Accepted August 27, 2001. ES010654N
