Critical Review
Solid-Solution Partitioning of Metals in Contaminated Soils: Dependence on pH, Total Metal Burden, and Organic Matter S EÄ B A S T I E N S A U V EÄ , * , † , ‡ , § W I L L I A M H E N D E R S H O T , ‡ A N D HERBERT E. ALLEN| QSAR Risk Assessment Service Inc., 360 St-Jacques West, Suite 800, Montre´al, Quebec, Canada, H2Y 1P5, Department of Natural Resource Sciences, McGill UniversitysMacdonald Campus, Ste-Anne-de-Bellevue, Quebec, Canada, H9X 3V9, INRS-Institut Armand-Frappier-Sante´, 245 Hymus, Pointe-Claire, Quebec, Canada H9R 1G6, and Department of Civil and Environmental Engineering, University of Delaware, Newark, Delaware 19716
Environmental risk assessment of metals depends to a great extent on modeling the fate and the mobility of metals based on soil-liquid partitioning coefficients. A large variability is observed among the reported values that could be used to predict metal mobility and bioavailability. To evaluate this, soil-liquid partitioning coefficients (Kd) for many elements but especially for the metals cadmium, copper, lead, nickel, and zinc were compiled from over 70 studies of various origins collected from the literature. The relationships between the reported values are explored relative to variations in soil solution pH, soil organic matter (SOM), and concentrations of total soil metal. The results of multiple linear regressions show that Kd values are best predicted using empirical linear regressions with pH (with R 2 values of 0.29-0.58) or with pH and either the log of SOM or the log of total metal and with resulting R 2 values of 0.42-0.76. A semi-mechanistic model based on the competitive adsorption of metal and H+ [dependent on solution pH, total metal content, and log(SOM)] was a better tool to predict dissolved metal concentrations (with R 2 values of 0.61-0.88), with the exception of Pb (at 0.35).
Introduction Simple determinations of total soil metal contents are a rather indiscriminate means of quantifying contamination and potential environmental and human health risks. Albeit overly conservative, in the absence of alternatives, “totals” remain the most advocated analytical means proposed by environmental protection agencies. Nevertheless, the evaluation of the potential risks and the toxicity of metals in soils requires an assessment of the proportion of the total metal that is in a mobile and possibly bioavailable form. This can be done using a relatively simple partitioning of the total metal burden between the fraction bound to soil solids and the part that * Corresponding author phone: (514)847-1714; fax: (514)845-2073; e-mail:
[email protected]. † QSAR Risk Assessment Service Inc. ‡ McGill University. § INRS-Institut Armand-Frappier-Sante ´. | University of Delaware. 10.1021/es9907764 CCC: $19.00 Published on Web 02/26/2000
2000 American Chemical Society
is dissolved in the soil solution. This partitioning approach assumes that dissolved metals are mobile and could possibly be taken up by adjacent plant roots or otherwise be detrimental to various soil biological organisms. The dissolved metal pool also reflects the soil metal fraction that could potentially be leached from the soil and contaminate groundwaters and surface waters. Conversely, the balance of the metal is assumed to be tightly retained by the soil solids and, hence, unavailable for biological uptake or movement into groundwater. For an organic contaminant, solid-liquid partitioning is related to its hydrophobic properties, as determined using octanol-water partitioning coefficients (Kow). In the absence of experimental data, Kow values can be estimated using various empirical regressions. The Kow of an organic compound is also used to estimate its organic carbon partitioning (1). The organic carbon partitioning is then used to determine solid-solution partitioning, proportionally to the organic carbon content of the material. Since metals do not have the hydrophobic and lipophilic characteristics of organic contaminants, a different approach is needed (2). Because metal adsorption is metal-specific and dependent on many different soil properties, environmental fate modeling of metals often requires actual experimental determinations of solid-liquid partitioning coefficients (Kd). Furthermore, obtaining a sitespecific Kd value would not ensure correct assessment of contaminant fate under transient system conditions. It is also important to realize that although the environmental impact assessment of large sites usually entails a significant analytical budget for site characterization, smaller sites or preliminary risk evaluations necessitate some representative default parametrization. It is our aim to compile data on soil solid-liquid partitioning of metals to provide default Kd values that are required by various risk assessment models as well as to evaluate the dependence of Kd coefficients on pH, total metal burden, and organic matter using empirical and semimechanistic regression models. We are not attempting to refine our mechanistic understanding of metal soil sorption processes but rather to improve the means by which we can estimate metal mobility and availability in contaminated soils. Although we would prefer using only data from field-collected soil, there is not enough published data to allow us to disregard studies using metal-spiked soils. VOL. 34, NO. 7, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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Kd Coefficents. The modeling of soil solution contaminant levels has often been modeled assuming that the ratio of the total contaminant bound to the solids relative to that found in the solution phase is constant (3):
Kd )
total metal (dissolved metal)
(1)
where total metal is usually given in mg kg-1 and dissolved metal is given in mg L-1; hence, the units for Kd values are in L kg-1. Standard sorption experiments do not usually rely on total metal contents but rather upon sorbed metal, as defined by disappearance from solution. In our case, we are interested mostly with field applications where the amount of metal added is usually unknown and partitioning needs to be related to some analytical determination of total metal content. Also, from an ecotoxicological perspective, whether an element has been added or was already present is irrelevantsthe determinant factors are the actual field concentrations contributing to environmental exposure. Although somewhat simplistic, the Kd approach is easy to integrate into various chemical models and allows estimations of metal dissolved in soil solutions and predictions of mobility as well as potential leaching losses. As such, the large number of environmental fate models (e.g., refs 4-6) that integrate such an approach justify at least an effort to define the best values to input into those models, the variability (and proportion of unexplained variance) of Kd values, and their dependence on other soil physicochemical characteristics. Modeling metal sorption using a single-valued Kd approach presumes that the sorption capacity of a material is relatively independent of soil physicochemical properties. The dependence of Kd values on soil texture and soil organic matter content is recognized (7), and attempts have been made to segregate data into various soil textural categories (8). Although some default values are available for a large numbers of elements (8-10), many values are estimated from plant-soil-solution relationships, which rely on simplifying assumptions (8, 11). Furthermore, there is ample evidence that single-value Kd coefficients are not appropriate to represent metal solubility in soil chemistry models (12), and some considerations for chemical properties such as pH, organic matter, and total metal burden need to be considered (13-17). Freundlich Isoterms. The Freundlich isotherm is similar to the single-value Kd approach but introduces an n parameter that allows for variations in the Kd values according to the relative saturation of the sorbing surfaces. Specifically, as the solution concentration of the metal increases, the ratio adsorbed on the solids will vary. The Freundlich equation takes the following form:
(adsorbed metal) ) Kd(dissolved metal)n
(2)
where the units are identical to eq 1 in the case that the value of the dimensionless constant, n, has a value of 1. The relationship between adsorbed and dissolved metal is illustrated in Figure 1. Also, Buchter et al. (7) have measured Freundlich parameters (Kd and n) for 11 different soils and 15 trace elements. They also explored the correlation of the Freundlich parameters with selected soil properties and found that pH, cation-exchange capacity (CEC), and iron/aluminum oxide contents were the most important factors for correlation with the partitioning coefficients. The conclusions of Buchter et al. (7) on Kd values follow: “1. pH is the most important soil property that affects Kd and n, 2. CEC influences Kd for cation species, 1126
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FIGURE 1. Theoretical partitioning coefficients (Kd) as a function of total soil metal burden for an arbitrary constant dissolved metal concentration (2) or an arbitrary constant solid-liquid ratiosa single-valued Kd (9). Theoretical results for Freundlich isotherms of Pb (b) or Zn (×) are also included for comparison [using the constants reported by Buchter et al. (7)]. 3. The amounts of amorphous iron oxides, aluminum oxides, and amorphous material in soils influence both cation and anion retention parameters, 4. Except for Cu and Hg, transition metal (Co and Ni) and group IIB cations (Zn and Cd) have similar Kd and n values for a given soil, and 5. Significant relationships between soil properties and retention parameters exist even in a group of soils with greatly different characteristics.” This, as well as other studies (9, 10, 13-18) suggests that indeed metal partitioning between the solution and the solids in contaminated soils could be, at least partially, predicted from simple soil properties. Buchter et al. (7) also suggest that certain groups of elements will have similar sorption properties in a specific soil. Soil Total Metal Content. From a purely theoretical perspective, the Kd is autocorrelated to the total soil metal content (since it is equivalent to the numerator of the ratio, see eq 1). Figure 1 illustrates the comparison of a theoretical constant, single-value Kd (solution concentration increases proportionally to total loadingsFreundlich parameter n ) 1) contrasted to that derived for a soil-solution system in which the concentration of dissolved metal is controlled by mineral equilibrium with a solid phase (hence constant and independent of total metal content). The Freundlich parameter n was found to vary experimentally from ∼0.4 to ∼1.5 for 15 different chemical elements (7), suggesting that different elements have different sorption properties. Hence, as illustrated hypothetically in Figure 1 for Pb and Zn, it can be seen that these two divalent metals react in an opposite trend to increased concentrations. Larger quantities of Pb in solution will promote its relative adsorption and increase the apparent Kd (case of n > 1). Conversely for Zn, higher concentrations of Zn will decrease the apparent Kd, reflecting a lowered affinity of the solid phase for Zn as it moves toward saturation (n < 1). Hence, it is not advisable to use Kd values derived from data at low contamination levels and apply them for risk assessment modeling of contaminated conditions (or reverse). Except for Pb and possibly Hg, most of the elements studied by Buchter et al. (7) have an n parameter below 1 and should therefore react qualitatively like Zn (Figure 1). Solution pH and Electrolytes. The importance of pH on metal solubility is well recognized but difficult to segregate from the influence of other soil characteristics that are often autocorrelated (19). Furthermore, it is important to make the distinction among the various chemical species present in solution. The Kd values for divalent metals depend on total dissolved metal concentrations, i.e., the sum of the free metal pool (Me2+), plus inorganic ion pairs [namely, MeOH+, Me(OH)20, Me(OH)3+, MeHCO3-, MeCO30, Me(CO3)22-,
MeNO3+, MeCl-, MeSO40, etc.] as well as complexes with dissolved organic matter (DOM). Since the total dissolved concentration actually reflects the sum of many different components, it is affected by any factor that would impact one of the individual components. The effect of pH dominates because it has a major influence on most of the chemical species (especially DOM and carbonate). Dissolved organic matter also has an important influence since, in most situations, a majority of the dissolved metal is found in metal-organic complexes [for example, more than 98% of dissolved Cu is bound to DOM in nonacidic soil solutions (20)]. Evidently, when most of the metal in solution is bound to DOM, any factor that influences organic matter solubility will also affect metal solubility (21-23). For example, high calcium concentrations will promote the coagulation of dissolved organic matter, reducing DOM solubility and therefore decreasing the total dissolved concentrations of metals that are bound to it (24, 25). On the other hand, calcium (and other cations) could also compete for adsorption sites on the solid phase, effectively increasing the solubility of certain metals (26-28). The partitioning of DOM to the particles, as well as the fulvic:humic acid ratio of the DOM, is a function of the soil solution pH (29, 30). Also, high concentrations of any of the inorganic ligands may solubilize significant quantities of metals, depending on the actual ligand concentrations and their respective dissociation constants. For example, the formation of cadmium chloride species is determinant in assessing the solubility and phytoavailability of Cd (31-33). The impact of variations in pH, DOM, and competing-complexing ions are not well described by the Kd approach. Even the normalized Kd that we are proposing is not able to discriminate the actual chemical species; Kd values only serve to distinguish the portion of a contaminant that is dissolved in the soil solution from that which is bound to the solids. No considerations are made for the relative bioavailability of the various chemical species present in the solution or for the desorption potential of what is sorbed to the solid phase. Competitive Adsorption Model. An alternative model to predict trace metal solubility in contaminated soils can be derived from a semi-mechanistic approach that assumes that free metal (Mex+) and H+ compete for adsorption on the soil’s exchange sites (see derivation and assumptions in refs 18, 34, and 35). This model accounts for pH, total metal content, and soil organic matter (SOM) and results in a simple equation:
log10(dissolved metal) ) a + b × pH + c × log10(total metal) + d × log10(SOM) (3) where dissolved metal is in µg L-1, a-d are coefficients determined using statistical regressions and appropriate sets of data, total metal is the soil metal content (in mg kg-1) measured using acid digestions, and SOM is soil organic matter (in % C). Such a model has been successfully applied to the predictions of Cd (18, 34, 36), Cu (18, 20, 34), Pb (18, 34, 37-39), and Zn solubility (35, 40). Albeit, most of the observed experimental variability seems to depend on solution pH and total metal burden (13-17), SOM has also been found to be significant (18, 34, 35).
Methods Literature Data. There is understandably much variability among the various experimental protocols compiled from over 70 different studies. It is not practical to thoroughly review all of the individual methodologies and analytical techniques. Briefly, the compiled data originated from those experimental results that reported soil total metal content (using some sort of acid digestion) along with the corresponding concentration of solution dissolved metal. Many
different acid digestions were included, but we have excluded data based on cold or dilute acid extractions. Only studies looking at soils were included, data for specific clays or oxides were not included due to the large differences in the properties of specific minerals vis-a`-vis whole soils. Studies reporting total metal content in the surface soil and soil water dissolved concentrations deeper below the surface were not included because this disregards the potential for adsorption or desorption from the soil layers in-between. The methods used for obtaining the dissolved metal pool vary widely, from water displacement of bulk field-moist soils to centrifugation and batch solution extractions using reagents varying from distilled water, dilute salts solutions, or relatively concentrated neutral salts extractants. Obviously, the exact method used to obtain the solution will impact metal partitioning (24, 25, 27, 28, 35, 41, 42). Although care was taken to restrict the compiled data to water displacement, lysimeter, and water or neutral salt extractions, a significant proportion of the variability simply arises from variations in the reagents and methods used to extract the solutions. The counter-cations and the anion pairs will influence competition for exchange sites, availability of complexation sites, and kinetics of reactions (43, 44). The Pb data from two studies (45, 46) were excluded because they showed solubility orders of magnitude higher than all the other gathered data (which we presume is an artifact due to the experimental setup that included an incubation period too short to allow the added metal salts to reach a pseudo-equilibrium with respect to soil sorption and mineral solubility processes). Statistical Treatment. All the extraction data were logtransformed in order to normalize their distribution. Statistical analyses and graphics were carried out using SYSTAT (47). The level of statistical significance is represented using * for p < 0.1, ** for p < 0.01, *** for p < 0.001, and NS when not significant (p > 0.1).
Results and Discussion The data compiled for 13 different elements are tabulated with means, standard deviations (SD), coefficient of variability (CV, the ratio of the SD to the mean), span of the Kd values, and medians. The number of data points for each element varies from 4 (Hg and Mo) up to 830 for Cd (Table 1). The trace metals Cd, Cu, Ni, Pb, and Zn each have a large number of data points (93-830), thus allowing a more detailed analysis of the factors influencing the experimental variability of Kd values. Comparison with Other Compilations. Our results show a stronger metal retention when compared with the Kd values reported in a similar compilation of published soil-liquid partitioning data in which the authors have segregated the soil into four different textural classes [sand, loam, clay, and organic (8)]. Only Mo and Sr are in a similar range (they reported Kd of 10-125 Mo vs 36 in this study and 15-150 Sr vs 137). Their Kd range was lower for the metals: Cd, 40-800 vs 7900; Cr, 30-1500 vs 8700; Ni, 300-1100 vs 24 700; Pb, 270-22 000 vs 217 000; and Zn, 200-2400 vs 13 400. As such, these differences only reflect the wide variability of solidliquid partitioning coefficients. The higher mean values reported in this study may be a reflection of the care taken to incorporate as many studies as possible that used fieldcollected, in-situ contaminated soils. These soil samples would be more representative of the field environmental conditions than soil samples brought into the lab and spiked to various levels using metal salts. The long-term field stabilization of these soils will also move the metal contaminant closer to an equilibrium with respect to metal sorption, mineral precipitation, and specific retention mechanisms (also called the “aging effect”) (48, 49). Although it would have been better to restrict our database to fieldVOL. 34, NO. 7, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 2. Partitioning coefficients (Kd) as a function of soil solution pH for Cd, Cu, Ni, Pb, and Zn (data calculated from refs 34, 36, 37, 40, 46, and 51-83). The upper and lower lines represent the 95% prediction intervals.
TABLE 1. Partitioning Coefficients (Kd in L kg-1): Arithmetic Means of Untransformed Kd Values, Corresponding Standard Deviations, and Coefficients of Variability, Medians, Minimum, Maximum, log (Kd), and Number of Data Points element
Kd (mean)
SD
CV
median
min
max
log10 Kd
N
As B Ba Cd Cr Cu Hg Mo Ni Pb Se Sr Zn
13119 160 3434 2869 14920 4799 8946 36 16761 171214 43937 137 11615
65086 96 3152 12246 16899 9875 5641 19 45350 304089 119534 42 30693
4.96 0.60 0.92 4.27 1.13 2.06 0.63 0.52 2.71 1.78 2.72 0.31 2.64
1825 136 2455 390 4778 2120 7500 38 2333 102410 15 130 1731
1.6 61 1414 0.44 125 6.8 4286 14 8.9 60.56 1.6 89 1.4
530000 389 14375 192000 65609 82850 16500 52 256842 2304762 600000 195 320000
4.12 2.20 3.54 3.46 4.17 3.68 3.95 1.55 4.22 5.23 4.64 2.14 4.07
66 12 15 830 64 452 4 4 139 204 63 10 302
TABLE 2. Linear Regressions of Kd (L kg-1) against the Soil Solution pH, the Coefficients with Standard Errors (R 2), Standard Errors of Estimates, and Number of Data Points log10 Kd
parameters ) ) ) ) )
Cd Cu Ni Pb Zn
constant
0.49 ( 0.02 • pH - 0.60 ( 0.49 0.27 ( 0.02 • pH + 1.49 ( 0.13 0.72 ( 0.05 • pH - 1.75 ( 0.36 0.49 ( 0.04 • pH + 1.37 ( 0.25 0.62 ( 0.03 • pH - 0.97 ( 0.21
R 2a
SEE
N
0.467*** 0.288*** 0.576*** 0.473*** 0.557***
0.71 0.60 0.70 0.65 0.73
830 447 138 204 298
a Statistical significance: * for p < 0.1, ** for p < 0.01, *** for p < 0.001, and NS for p > 0.1.
collected contaminated soils, there would not be enough data available to attempt a normalization for physicochemical parameters. Dependence on Soil Total Metal Content and pH. The theoretical relationships between Kd and total soil metal content were described earlier in Figure 1. The actual compiled data are described in Figure 2 and Table 1. Although most regressions are significant due to the very large N, the values of Kd cover a range of more than 5 orders of magnitude. For Cd, Cu and Zn, the linear relationship between the Kd and total soil metal only explains >2.5% of the variability, and for Pb and Ni the R 2 values rise up to 28 and 31%, respectively (data not presented). The linear relationships between Kd and soil solution pH explain more of the variability, from 29 to 47% for Cd, Cu, and Pb and from 56 to 58% for Zn and Ni (Figure 2 and Table 2). Again, some metal-specific behavior is observed. We qualitatively observe that the metals Cd, Cu, and Pb behave in a similar fashion, with relatively low pH coefficient values (0.27-0.49) as compared to those for Zn (0.62) and Ni (0.72). Nevertheless, this pH-specific Kd approach explains from ∼30 to ∼60% of the variability. Although this is an improvement from a single-value Kd, this is not satisfying for model 1128
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predictions aimed at human health and ecotoxicological environmental protection. Furthermore, the predicted R 2 values are for Kd, which, being a ratio, is sensitive to small errors and tends to aggravate the impact of predictions inaccuracies on the model outputs (dissolved metal concentrations). The linear pH regressions can be improved by adding a second component for log(SOM) or for log(total metal) (Table 3). Although this significantly improves the R 2 for all the metals studied, the relative amelioration is metal-specific (marginal for Zn and between 9 and 18% for the other metals). This would suggest that the most beneficial and universal site-specific Kd adjustment is pH (as illustrated in Figure 2 and Table 2). Albeit significant, the addition of total metal or SOM to the regression equations complicates the computations and requires the use of metal-specific models. As illustrated in Table 3, SOM was only a significant contributor for Cd, Cu, and Ni. For Pb and Zn, total metal was a better predictor. Although it is not practical to use different regression models for different metals, proper assessment of metal behavior requires the use of metal-specific tools, and a single universal regression may not be possible. A sample calculation (using the data tabulated in Table 2) is illustrated in eq 4 for the pH dependence of the Kd of Zn:
log10(KZn d ) ) 0.62pH - 0.97 ∴ for pH ) 6, Zn log10(KZn d ) ) 2.75 and Kd ) 562 (4)
Soil Organic Matter and Other Physicochemical Parameters. Sufficient data were available for SOM to warrant its integration into the database. The availability of data for other physicochemical parameters (such as iron oxide, clay, manganese, sulfides, etc.) was quite variable and inconsistent, and those were therefore not included in the statistical analyses. Competitive Adsorption Model. The linear regressions tabulated earlier do not yield a completely satisfactory
TABLE 3. Coefficients and Standard Errors for the Linear Regressions of Kd (L kg-1) against the Soil Solution pH Combined with either Soil Organic Mattera or Log10 (Total Metal)b log10 Kd Cd Cu Ni Pb Zn a
parameters ) ) ) ) )
0.48 ( 0.02 0.21 ( 0.02 1.02 ( 0.09 0.37 ( 0.04 0.60 ( 0.03
• • • • •
pH*** pH*** pH*** pH*** pH***
+ + + + +
0.82 ( 0.05 0.51 ( 0.06 0.80 ( 0.20 0.44 ( 0.07 0.21 ( 0.06
• • • • •
log (SOM)*** log (SOM)*** log (SOM)*** log (total)*** log (total)***
+ + -
constant
R 2c
SEE
N
0.65 ( 0.10 1.75 ( 0.12 4.16 ( 0.60 1.19 ( 0.22 1.34 ( 0.23
0.613*** 0.419*** 0.758*** 0.562*** 0.573***
0.62 0.55 0.61 0.59 0.72
751 353 69 204 298
SOM, in % C. bIn mg (kg dry soil)-1. c Statistical significance: * for p < 0.1, ** for p < 0.01, *** for p < 0.001, and NS for p > 0.1.
TABLE 4. Coefficients and Standard Errors for the Linear Regressions of Dissolved Metal Concentrations (mg L-1) against Soil Solution pH, Soil Organic Mattera and Log10 (Total Metal)b log (dissolved) Cd Cu Ni Pb Zn a
) ) ) ) )
-0.47 ( 0.02 • pH*** -0.21 ( 0.02 • pH*** -1.05 ( 0.09 • pH*** -0.37 ( 0.04 • pH*** -0.55 ( 0.04 • pH***
SOM, in % C.
b
parameters + + + + +
1.08 ( 0.02 • log (total)*** - 0.81 ( 0.05 • log (SOM)*** 0.93 ( 0.05 • log (total)*** - 0.21 ( 0.02 • log (SOM)*** 1.21 ( 0.22 • log (total)*** - 0.85 ( 0.21 • log SOM)*** 0.56 ( 0.07 • log (total)*** log (SOM)NS 0.94 ( 0.08 • log (total)*** - 0.34 ( 0.12 • log SOM)**
+ + + + +
constant
R2c
SEE
N
3.42 ( 0.11 1.37 ( 0.14 7.02 ( 0.62 1.81 ( 0.22 3.68 ( 0.31
0.884*** 0.611*** 0.727*** 0.347*** 0.618***
0.62 0.47 0.61 0.53 0.72
751 353 69 204 212
In mg (kg of dry soil)-1. c Statistical significance: * for p < 0.1, ** for p < 0.01, *** for p < 0.001, and NS for p > 0.1.
FIGURE 3. Solution-dissolved concentrations (µg L-1) as a function of soil solution pH and soil total metal (mg kg-1) for Cd, Cu, Ni, Pb, and Zn data calculated from refs 34, 36, 37, 40, 46, and 51-83. The graphic surfaces are obtained using a distance-weighted smoothing technique (47) and serve to illustrate the extent of the fit. explanation of the variance for many metals, we therefore also tried using the competitive adsorption model (eq 3). The results are presented in Table 4 and Figure 3. Not only does this model significantly improves the R 2 values, it is actually predicting dissolved metal concentrations not simply the solid-solution ratio (Kd). In this case, the explained variance varies from 61 to 88% except for Pb, which has a lower R 2 than earlier at 35% (since it is predicting real values and not ratios, this model may still be better). Since the Kd values are actually used in environmental risk and fate models to predict the amount of dissolved metal in solution, it may be better to use the competitive adsorption model to predict dissolved metals directly. Prediction of Partitioning Coefficients. The correlative approaches that we have discussed will allow suitable predictions of the concentration of dissolved metals for many applications. The most important factors affecting dissolved
metal concentrations are the total concentrations of metal in the soil, soil solution pH, and organic matter content. Higher degrees of predictive ability require consideration of additional components of both the solid and the solution phases. This would allow the metal to be simultaneously partitioned into a number of discrete solid phases and to be complexed by a number of soluble components. Geochemical speciation models are well-suited to perform the necessary computations. In addition to the solid-phase components that have been discussed, both iron and manganese oxides are important sorbants of trace metals. Although we have treated organic matter as a single chemical entity, its heterogeneous nature in both the particulate and the solution phases will need to be taken into account. Multiple binding site models, such as that developed by Tipping (50), are necessary to adequately describe the interaction of metals with organic matter over a wide range of metal concentraVOL. 34, NO. 7, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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tions. Further development of models to predict partitioning of metals in both soils and natural waters is being actively studied in a number of laboratories due to its importance in controlling the fate of metals and in regulating their bioavailability. The experimentally determined solid-solution partitioning of Cd, Cu, Ni, Pb, and Zn is dependent on soil solution pH, total metal content, and SOM (similar relationships could presumably be obtained for other trace elements). The best regression model is metal specific. Also, site-specific modifications of Kd values to input into environmental fate models should minimally correct for pH effects using an empirical approach similar to the linear regressions described in Table 2. Alternatively, metal partitioning coefficients would be better predicted using empirical regressions combining pH and either SOM or total metal (Table 3). Using a more direct approach, better model predictions of solution dissolved metal concentrations will be obtained using a semi-mechanistic competitive adsorption model (except Pb). Given the very wide variation in dissolved metal concentrations, the applicability of any of these predictive models to a particular contaminated site could be questioned. Further work is needed if we are to provide environmental risk models with predictive equations that will significantly decrease the uncertainty of exposure estimates.
Acknowledgments Financial support for this project was provided partly through an operating grant to W.H. and a postdoctoral fellowship to S.S., both from the Natural Science and Engineering Research Council of Canada.
Literature Cited (1) Karickhoff, S. W. Chemosphere 1981, 10, 833-846. (2) Lee, C. M.; Allen, H. E. Human Ecol. Risk Assess. 1998, 4, 605617. (3) Kurbatov, M. H.; Wood, G. B.; Kurbatov, J. D. J. Phys. Colloid Chem. 1951, 55, 1170-1182. (4) Sheppard, M. I.; Elrick, D. E.; Peterson, S. R. Can. J. Soil Sci. 1997, 77, 333-344. (5) Laniak, G. F.; Droppo, J. G.; Gnanapragasam, E. K.; Mills, W. B.; Strenge, D. L.; Whelan, G.; Yu, C. Risk Anal. 1997, 17, 203-214. (6) Hedden, K. F. J. Toxicol. Clin. Toxicol. 1984, 21, 65-95. (7) Buchter, B.; Davidoff, B.; Amacher, M. C.; Hinz, C.; Iskandar, I. K.; Selim, H. M. Soil Sci. 1989, 148, 370-379. (8) Sheppard, M. I.; Thibault, D. H. Health Phys. 1990, 59, 471-482. (9) Anderson, P. R.; Christensen, T. H. J. Soil Sci. 1988, 39, 15-22. (10) Gooddy, D. C.; Shand, P.; Kinniburgh, D. G.; van Riemdsjik, W. H. Eur. J. Soil Sci. 1995, 46, 265-285. (11) Sheppard, S. C.; Evenden, W. G. J. Environ. Radioact. 1988, 7, 221-247. (12) Ro¨mkens, P. F.; Salomons, W. 9th International Conference on Heavy Metals in the Environment, Toronto, September 1993; Vol. 2, pp 496-499. (13) Jopony, M.; Young, S. D. Eur. J. Soil Sci. 1994, 45, 59-70. (14) Lee, S.-Z.; Allen, H. E.; Huang, C. P.; Sparks, D. L.; Sanders, P. F.; Peijnenburg, W. J. G. M. Environ. Sci. Technol. 1996, 30, 3418-3424. (15) Janssen, R. P. T.; Pretorius, P. J.; Peijnenburg, W. J. G. M.; van den Hoop, M. A. G. T. Determination of field-based partition coefficients for heavy metals in Dutch soils and the relationships of these coefficients with soil characteristics; Report 719101023; RIVM: Bilthoven, The Netherlands, 1996. (16) de Groot, A. C.; Peijnenburg, W. J. G. M.; van den Hoop, M. A. G. T.; van Veen, R. P. M. Heavy metals in Dutch field soils: an experimental and theoretical study on equilibrium partitioning; Report 607220 001; RIVM, Bilthoven, The Netherlands, 1998. (17) Janssen, R. P. T.; Peijnenburg, W. J. G. M.; Postuma, R.; van den Hoop, M. A. G. T. Environ. Toxicol. Chem. 1997, 16, 2470-2478. (18) McBride, M. B.; Sauve´, S.; Hendershot, W. Eur. J. Soil Sci. 1997, 48, 337-346. (19) Basta, N. T.; Pantone, D. J.; Tabatabai, M. A. Agron. J. 1993, 85, 1054-1057. (20) Sauve´, S.; McBride, M.; Norvell, W. A.; Hendershot, W. Water Air Soil Pollut. 1997, 100, 133-149. (21) Harter, R. D.; Naidu, R. Adv. Agron. 1995, 55, 219-263. 1130
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(22) Naidu, R.; Harter, R. D. Soil Sci. Soc. Am. J. 1998, 62, 644-650. (23) Christensen, J. B.; Jensen, D. L.; Christensen, T. H. Water Res. 1996, 30, 3037-3049. (24) Ro¨mkens, P. F. A. M.; Dolfing, J. Environ. Sci. Technol. 1998, 32, 363-369. (25) Fotovat, A.; Naidu, R. Geoderma 1998, 84, 213-234. (26) Christensen, T. H. Water Air Soil Pollut. 1987, 34, 293. (27) Pardo, M. T. Soil Sci. 1997, 162, 733-740. (28) Escrig, I.; Morell, I. Water Air Soil Pollut. 1998, 105, 507-520. (29) Yin, Y.; Allen, H. E.; Li, Y.; Huang, C.-P.; Sanders, P. F. J. Environ. Qual. 1996, 25, 837-846. (30) You, S.-J.; Yin, Y.; Allen, H. E. Sci. Total Environ. 1999, 227, 155-160. (31) Lumsdon, D. G.; Evans, L. J.; Bolton, K. A. J. Soil Contam. 1995, 4, 137-150. (32) Smolders, E.; McLaughlin, M. J. Soil Sci. Soc. Am. J. 1996, 60, 1443-1447. (33) Smolders, E.; Lambregts, R. M.; Mclaughlin, M. J.; Tiller, K. G. J. Environ. Qual. 1998, 27, 426-431. (34) Sauve´, S. Chemical speciation, solubility and bioavailability of lead, copper and cadmium in contaminated soils. Ph.D. Dissertation, Cornell University, 1999. (35) Sauve´, S. Chemical speciation of metals in soils. In Bioavailability of Metals in Terrestrial Ecosystems: Importance of Partitioning for Bioavailability to Invertebrates, Microbes and Plants; Allen, H. E., Ed.; Society for Environmental Toxicology and Chemistry: in press. (36) Sauve´, S.; Norvell, W. A.; McBride, M.; Hendershot, W. Environ. Sci. Technol. 2000, 34, 291-296. (37) Sauve´, S.; McBride, M. B.; Hendershot, W. H. Environ. Pollut. 1997, 98, 149-155. (38) Sauve´, S.; McBride, M.; Hendershot, W. Soil Sci. Soc. Am. J. 1998, 62, 618-621. (39) Sauve´, S.; McBride, M.; Hendershot, W. Environ. Sci. Technol. 1998, 32, 388-393. (40) Tambasco, G.; Sauve´, S.; Cook, N.; McBride, M.; W, H. Can. J. Soil Sci. 2000 (in press). (41) Saar, R. A.; Weber, J. H. Geochim. Cosmochim. Acta 1980, 44, 1381. (42) Bond, W.; Smiles, D. E. Soil Use Manage. 1988, 4, 115-120. (43) Hering, J. G.; Morel, F. M. M. Environ. Sci. Technol. 1990, 24, 242-252. (44) Hering, J. G.; Morel, F. M. M. In Aquatic chemical kinetics: reaction rates of processes in natural waters; Stumm, W., Ed.; John Wiley & Sons: New York, 1990; pp 145-171. (45) MacLean, A.; Hasltead, R. L.; Finn, B. J. Can. J. Soil Sci. 1969, 49, 327-334. (46) Lebourg, A.; Sterckeman, T.; Ciesielski, H.; Proix, N. J. Environ. Qual. 1998, 27, 584-590. (47) Wilkinson, L. SYSTAT for windows: Graphics version 5; SYSTAT Inc.: Evanston, IL, 1992. (48) Martin, H. W.; Kaplan, D. I. Water Air Soil Pollut. 1998, 101, 399-410. (49) Hooda, P. S.; Alloway, B. J. J. Soil Sci. 1993, 44, 97-110. (50) Tipping, E. Comput. Geosci. 1994, 21, 973-1023. (51) Janssen, R. P. T.; Posthuma, L.; Baerselman, R.; Den Hollander, H. A.; Van Veen, R. P. M.; Peijnenburg, W. J. G. M. Environ. Toxicol. Chem. 1997, 16, 2479-2488. (52) Elsokkary, I. H. Z. Pflanzenernaehr. Bodenkd. 1980, 143, 74-83. (53) Frost, R. R.; Griffin, R. A. Soil Sci. Soc. Am. J. 1977, 41, 53-57. (54) Knight, B. P.; Chaudri, A. M.; McGrath, S. P.; Giller, K. E. Environ. Pollut. 1998, 99, 293-298. (55) Mench, M.; Vangronsveld, J.; Didier, V.; Clijsters, H. Environ. Pollut. 1994, 86, 279-286. (56) Esnaola, M. V.; Milla´n, E. Environ. Pollut. 1998, 99, 79-86. (57) Krishnamurti, G. S. R.; Huang, P. M.; Kozak, L. M.; Rostad, H. P. W.; Van Rees, K. C. J. Can. J. Soil Sci. 1997, 77, 613-619. (58) McGrath, S. P.; Knight, B.; Killham, K.; Preston, S.; Paton, G. I. Environ. Toxicol. Chem. 1999, 18, 659-663. (59) Atanassova, I. Environ. Pollut. 1995, 87, 17-21. (60) Sheppard, M. I.; Thibault, D. H.; Mitchell, J. H. J. Environ. Qual. 1987, 16, 273-284. (61) Kalbitz, K.; Wennrich, R. Sci. Total Environ. 1998, 209, 27-39. (62) Holm, P. E.; Christensen, T. H.; Lorenz, S. E.; Hamon, R. E.; Domingues, H. C.; Sequeira, E. M.; McGrath, S. P. Water Air Soil Pollut. 1998, 102, 105-115. (63) Vuori, E.; Va¨a¨rikoski, J.; Hartikainen, H.; Vakkilainen, P.; Kumpulainen, J.; Niinivaara, K. Agric. Ecosyst. Environ. 1989, 25, 111-118. (64) Levy, D. B.; Barbarick, K. A.; Siemer, E. G.; Sommers, L. E. J. Environ. Qual. 1992, 21, 185-195.
(65) Tiller, K. G.; Nayyar, V. K.; Clayton, P. M. Aust. J. Soil Res. 1979, 17, 17-28. (66) Campbell, D. J.; Beckett, P. H. T. J. Soil Sci. 1988, 39, 283-298. (67) MacLean, A. J.; Dekker: A. J. Can. J. Soil Sci. 1978, 58, 381-389. (68) Gerritse, R. G.; van Driel, W. J. Environ. Qual. 1984, 13, 197204. (69) Ramos, L.; Hernandez, L. M.; Gonzalez, M. J. J. Environ. Qual. 1994, 23, 50-57. (70) Zhan, T. Environ. Pollut. (Ser. B) 1986, 12, 265-280. (71) Winisto¨rfer, D. Commun. Soil. Sci. Plant. Anal. 1995, 26, 10731093. (72) Brun, L. A.; Maillet, J.; Richarte, J.; Herrman, P.; Remy, J. C. Environ. Pollut. 1998, 102, 151-161. (73) Abuzid, M. M.; Obukhov, A. I. Moscow Univ. Soil Sci. Bull. 1992, 47, 37-39. (74) Pierzynski, G. W.; Schwab, A. P. J. Environ. Qual. 1993, 22, 247254. (75) Li, J. Fractionation and speciation of trace metals in contaminated urban soils from Montre´al, Canada. M.Sc. Thesis, McGill University, 1997. (76) Richards, B. K.; Steenhuis, T. S.; Peverly, J. H.; McBride, M. B. Environ. Pollut. 1998, 99, 365-377.
(77) Sauve´, S.; Cook, N.; Hendershot, W. H.; McBride, M. B. Environ. Pollut. 1996, 94, 154-157. (78) Ma, Y. B.; Uren, N. C. Aust. J. Soil Res. 1997, 35, 727-738. (79) McLaren, R. G.; Crawford, D. V. J. Soil Sci. 1973, 24, 443-452. (80) Ying, G.; Murray, P.; Hendershot, W. Environ. Pollut. 2000, 107, 137-144. (81) Yamada, H.; Kang, Y.; Aso, T.; Uesugi, H.; Fujimara, T.; Yonebayashi, K. Soil Sci. Plant Nutr. 1998, 44, 385-391. (82) Allen, H. E.; Lee, S.-Z.; Huang, C. P.; Sparks, D. L. The Fate and Transport of Inorganic Contaminants in New Jersey Soils; Final Report to New Jersey Department of Environmental Protection and Energy; 1994. (83) Chaudri, A. M.; Knight, B. P.; Barbosa-Jefferson, V. L.; Preston, S.; Paton, G. I.; Killham, K.; Coad, N.; Nicholson, F. A.; Chambers, B. J.; McGrath, S. P. Environ. Sci. Technol. 1999, 33, 1880-1885.
Received for review July 12, 1999. Revised manuscript received January 4, 2000. Accepted January 10, 2000. ES9907764
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