Environ. Sci. Technol. 2006, 40, 7085-7093
A Terrestrial Biotic Ligand Model. 1. Development and Application to Cu and Ni Toxicities to Barley Root Elongation in Soils S A G A R T H A K A L I , † H E R B E R T E . A L L E N , * ,† DOMINIC M. DI TORO,† ALEXANDER A. PONIZOVSKY,† CORINNE P. ROONEY,‡ FANG-JIE ZHAO,‡ AND STEPHEN P. MCGRATH‡ Center for the Study of Metals in the Environment, Department of Civil and Environmental Engineering, University of Delaware, Newark, Delaware 19716, and Agriculture and Environment Division, Rothamsted Research, Harpenden, Hertfordshire, AL5 2JQ, UK
A Terrestrial Biotic Ligand Model (TBLM) was developed using noncalcareous soils from Europe based on Cu and Ni speciation and barley (Hordeum vulgare cv. Regina) root elongation bioassays. Free metal ion (M2+) activity was computed by the WHAM VI model using inputs of soil metal, soil organic matter, and alkali and alkaline earth metals concentrations, and pH in soil solution. The TBLM assumes that metal in soil and in the solution are in equilibrium. Metal ions react with the biotic ligand, the receptor site, and inhibit root elongation. Other ions, principally H+, Ca2+ and Mg2+, compete with M2+ and, therefore, affect its toxicity. Toxicity is correlated only to the fraction of the total biotic ligand sites occupied by M2+. Compared to other models using either the soil metal concentration or M2+ activity as the toxic dose, the TBLM provides a more consistent method to normalize and compare Cu and Ni toxicities to root elongation among different soils. The TBLM was able to predict the EC50 soil Cu and Ni concentrations generally within a factor of 2 of the observed values, a level of precision similar to that for the aquatic Biotic Ligand Model, indicating its potential utility in metals risk assessment in soils.
Introduction It is widely recognized that the soil metal concentration does not represent its bioavailability, and hence the level of its toxicity to soil biota (1). Nonetheless, soil quality criteria and risk assessment of metals are still predominantly based on soil metal concentrations (2). Since metal contamination in soils is a global problem with far reaching implications for essential soil functions (3, 4), a risk assessment and soil quality criteria based on bioavailable metal is needed to properly assess the level of risk. The interactions incorporated in the biotic ligand model (BLM), which account for the competitive binding of major cations and protons by the receptor sites on aquatic * Corresponding author phone: +1-302-831-8449; fax: +1-302831-3640; e-mail:
[email protected]. † University of Delaware. ‡ Rothamsted Research. 10.1021/es061171s CCC: $33.50 Published on Web 10/20/2006
2006 American Chemical Society
FIGURE 1. Schematic of the interactions considered in the TBLM (modified from Di Toro et al. (14)). organisms (5), have been suggested for terrestrial systems as well (1, 2, 6-10). A significant correlation reported between the soil pH and the critical Cu2+, Zn2+, Cd2+, and Pb2+ activities calculated by regression models is indicative of competitive interactions (11). The protective effects of protons, Mg2+, and Ca2+ on the rhizotoxicity of Cu2+ and Zn2+ to wheat seedlings (12) and the inhibition of Cu uptake by protons, Mg2+, and Ca2+ in lettuce (13) were demonstrated in hydroponic studies. These hydroponic studies, and studies entirely based on soil solution properties, are no different from the aquatic BLM but are not convenient for terrestrial systems since information on the chemical characteristics of soil pore water is not readily available, a situation similar to sediment pore waters (14). Plette et al. (6) and Van Gestel and Koolhas (7) developed quantitative models that account for the competitive binding of cations to the biotic phase and related the bioaccumulation to the toxic effects. Both of these studies quantify the metal accumulation in the sorbing phases in the soil, including the biota, using empirical isotherms with reasonable success. However, these models suffer from two limitations. First, the isotherms employed are purely empirical and therefore may lack wide-scale applicability, and, second, metal accumulation in organisms does not always correlate well with observed toxic effects (15). In this paper, we develop a theoretical model, the Terrestrial Biotic Ligand Model (TBLM), for ecotoxicity of metals in soils and apply it to Cu and Ni toxicities to barley root elongation as an example. Speciation Model. The interaction between the soil phases and the solution phase is shown in Figure 1. Metal removal from the soil solution is due to sorption by the solid phases, such as the soil organic matter (SOM), the mineral oxides of Fe, Al, and Mn, and clay. Competitive sorption of cations H+, Ca2+, and Mg2+ to the soil phase also affects the distribution of the metal between the soil and solution phases. Free metal ion (M2+) complexation by dissolved ligands is also depicted in Figure 1. For a TBLM, a speciation model is needed that is able to account for the partitioning of the metal between the soil and the solution phase and to determine its speciation in the solution. The assemblage model WHAM VI (16) considers all the interactions depicted in Figure 1 except metal removal from the solution due to precipitation. For this reason, WHAM VI is not an appropriate model for the speciation of metals in calcareous soils, where precipitation is likely to influence the speciation. The application of WHAM VI has been limited VOL. 40, NO. 22, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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primarily to metal speciation based on the composition of isolated soil solutions (17-19). This approach using solution composition as the staring point ignores the soil phase entirely, but this is an integral part of the TBLM. Most recently, WHAM VI has been used to predict Cu2+ activities (20) and dissolved Ni concentrations (21) in laboratory contaminated non-calcareous soils. The experimental conditions and the soils used in Ponizovsky et al. (20) and Thakali (21) are similar to those used for the barley root elongation experiments of this study. They used the bulk properties (background + added soil metal concentration and SOM content) and the soil solution properties (pH, dissolved organic carbon (DOC), and dissolved Ca, Mg, and Na) as inputs to speciate Cu and Ni in the separated soil solutions. The SOM in these soils is predominantly humic substances. Application of the partitioning model to soils for which a larger proportion of the SOM is not humic substances would require appropriate adjustment of the fraction active organic matter. The applicability of the whole soil approach using WHAM VI is discussed in greater detail in the Supporting Information. In principle, the TBLM should be applicable to all soils, irrespective of their properties. The current restriction to only noncalcareous soils with OC content >1% for Ni and only noncalcareous soils for Cu is due to the unavailability of a speciation model for Cu and Ni in these soils. Toxicity Model. The interaction of the cation activities with the biological phase (Figure 1) are considered in the toxicity model. The following are assumed in the toxicity sub model for the TBLM. (i) The active sites, the biotic ligand (BL) sites, which affect root elongation, are treated as a single ligand in this study. As in the aquatic BLM (5) we make no assumptions as to the nature of these sites and the mechanisms for toxicity. The cation-BL interaction is treated as a surface complexation reaction:
Xi + BL T XiBL
(1)
with the equilibrium relationship
[XiBL] -
[BL ]{Xi}
) KXiBL
100 f 1+ f50
(2)
()
β
(3a)
where R ) biological response as % of the control, f50 is the fraction of the total BL sites occupied by M2+ at which a 50% 7086
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100 1 + exp[β(f50 - f)]
(3b)
Although R ) 50 when f ) f50 in both eqs 3a and b, eq 3b does not have the proper asymptotic behavior (R ) 100 when f ) 0 and R ) 0 when f ) 1) that is required to fit the entire range of dose-response relationship. Metals may stimulate growth (“hormesis”) especially in soils with very low background metal concentration. However, the entire response curves in this study are modeled using the toxicity response (eq 3a) because it has less fitting parameters and the potential hormesis is similar in magnitude to the precision of the observed responses. (iii) The competing cations that we have consideredsH+, 2+ Ca , and Mg2+ (Figure 1)sare not toxic when they bind to the BL. Thus, they reduce metal toxicities. (iv) M2+ binding by the BL does not influence its speciation in soil solution to any appreciable extent, i.e., metal activities in the soil solutions are buffered by the solid phases as discussed previously. The total BL site concentration, [TBL], is given by
[TBL] ) BL- + [HBL] + CaBL+ + MgBL+ + MBL+ (4a) and using the equilibrium relationships (eq 2) yields
[TBL] ) BL-(1 + KHBL{H+} + KCaBL{Ca2+} + KMgBL{Mg2+} + KMBL{M2+}) (4b) The fraction (f) of the total BL sites bound by M2+ is then given by f)
where [XiBL] is the concentration (moles g-1) of BL sites bound by the cation Xi, [BL-] is the concentration (moles g-1) of free BL sites, {Xi} is the activity of the cation (moles L-1), and KXiBL is the conditional binding constant (L mol-1) for specific cation-BL interaction. The charges have been omitted for brevity. The assumption of equilibrium partitioning may not be valid either when the surface complexation reactions are slow relative to the internal transport and the subsequent expression of the biological response, or when the metal transport into the organism is limited due to diffusional control across the static boundary layer around the interfacial cells. Although both the kinetic control and the diffusional limitations have been shown to occur, they are much more likely only under metal deficient conditions (22) and thus not relevant to toxicity studies in general (23). (ii) The biological response is correlated to the fraction (f) of the total BL sites bound by the free metal ion (M2+) and follows the log-logistic dose-response relationship
R)
response is observed, and β is the shape parameter. In developing the aquatic BLM, an alternative relationship has been assumed (24):
[MBL+] ) [TBL] KMBL{M2+}
1 + KMBL{M } + KHBL{H+} + KCaBL{Ca2+} + KMgBL{Mg2+} 2+
(5)
Note that f is independent of [TBL] and thus is not related to biomass of the organism being considered. Substituting f from eq 5 in eq 3a, yields R) 1+
(
100 KMBL{M2+} +
)
β
f50(1 + KMBL{M } + KHBL{H } + KCaBL{Ca } + KMgBL{Mg }) (6) 2+
2+
2+
Equation 6 forms the mathematical basis for the TBLM that explicitly relates the biological response to the chemistry of the soil solution and thence to the soil which is in equilibrium with the soil solution.
Experimental and Data Analysis Methods Selection of the Soils. The soils selected for this study (Tables A-1 and A-2 in Supporting Information) represent subsets of all non-calcareous soils used in Rooney et al. (25, 26) for which the whole soil approach using WHAM VI (20, 21) can fit the partitioning of Cu and Ni. The soils used for Cu and Ni study are generally from the same area but were sampled at different locations and at different times. Therefore, soils with the same name differ in their properties. The soils used in this study represent a wide range of properties. The ranges of soil pH and OC content are 3.6-6.7 and 1.1-33.1% for Ni
experiments. For Cu experiments the ranges were 3.4-6.8 for soil pH and 0.41-23.3% for OC content. Biological Test. Ni and Cu toxicities to barley root elongation tests were carried out using the ISO 11269-1 method (27); see Rooney et al. (25, 26). The soils were spiked with CuCl2 or NiCl2 to achieve 7 levels of dose including the control. The root elongation data and the soil solution properties for Ni and Cu experiments used in this study are presented in Tables A-3 and A-4, respectively, in the Supporting Information. The methods for collecting soil pore water and determining the solution chemistry are explained elsewhere (10, 26). The observed EC50 soil metal concentrations for individual soils (Tables A-1 and A-2 in Supporting Information) were estimated by fitting the log-logistic model using the Toxicity Relationship Analysis Program (TRAP), version 1.0 (28). Whole Soil Metal Speciation Using WHAM VI. The WHAM VI model was used with its default parameters to compute M2+ activities in the soil solutions at each of the 7 doses (including the control). The required bulk soil properties were the soil metal concentration (added + background in mg kg-1 soil) at each dose and the SOM content. The details of the speciation procedure are discussed in the Supporting Information. We used data for dissolved copper and nickel concentration in soil solution to validate the predicted partitioning between the soil and solution phase. To account for the DOC complexed fraction of the dissolved metal, 65% DOC was considered active as colloidal FA (19) and included in the above calculations. Estimation of Model Parameters. The TBLM model parameters f50, β, and the conditional binding constants (KXiBLs) were estimated by minimizing the root mean squared error (RMSE) of the predicted root elongation (see Supporting Information) using the SOLVER program in Microsoft Excel 2003. Two additional models based on the log-logistic relationship (eq 3a) were also fitted to the same dataset for comparison to the TBLM. The first is the total metal model (TMM) that uses the soil metal concentration as the dose. The second model is the free ion activity model (FIAM) and uses the computed M2+ activities as the dose. The fitted parameters are the EC50 soil metal concentration (mg M kg-1) and β for the TMM and the EC50 M2+ activities (moles L-1), and β for the FIAM. The standard errors of the fitted parameters were estimated using the Visual Basic for Applications (VBA) macro “SOLVER AID” (29).
Results and Discussion WHAM VI Speciation. Dissolved Metal. The precision in the predicted dissolved Ni concentrations (log transformed) for the soils in this study was similar to that reported in Thakali (21). The RMSE was 0.39 and the coefficient of determination (R2) for the linear regression between the predicted and measured values was 0.93. Similarly, the RMSE of the predicted dissolved Cu concentrations (log transformed) was 0.43 and the R2 for the linear regression between the predicted and measured values was 0.86. These RMSE values represent less than a factor of 3 variation in a linear scale. Comparisons of predicted vs measured dissolved metal concentrations are given in Figure A-1 (Supporting Information). Cu Activity. The level of precision of the predicted dissolved Cu concentration is significantly greater than that associated with the prediction of pCu () -log {Cu2+}) for similar soils (RMSE ) 0.77) and under similar experimental conditions (20). The data from Ponizovsky et al. (20) were also used to compare model predictions. For this independent dataset, the dissolved Cu concentrations were predicted with a RMSE of 0.39 which is similar to 0.43 in this study. The predicted and measured Cu speciation results for the data
from Ponizovsky et al. (20) are compared in Figure A-1 (Supporting Information). A significantly smaller error in the prediction of dissolved Cu concentration (RMSE ) 0.39) than in the prediction of pCu (RMSE ) 0.77) indicates that the determination of dissolved Cu concentration is probably more precise than the determination of the Cu2+ activity. Antunes et al. (30) also reported that the determination of metal ion activities in soil pore waters is not likely to be better than within an order of magnitude of the actual values due to the uncertainties involved in sample collection, handling, and determination of free ion activities. An accurate prediction of dissolved metal in the soil solutions by WHAM VI requires an accurate prediction of M2+ concentration since the calibration of metal-DOC complexation is based on the equilibrium interaction between M2+ and DOC. The whole soil approach to compute Cu2+ activities using WHAM VI is more accurate than that indicated in Ponizovsky et al. (20). It is important to note that the inclusion of DOC in the calculations has no significant effect on the computed M2+ activities. The root-mean-square difference in the Ni2+ activities (log based) predicted with and without DOC was 0.15 and in the case of Cu2+ activities (log based) it was 0.06. Therefore, the metal activities in these soils depend largely on their binding to the soil solid phase as shown by other studies (18, 31-33). More than 99% of the total Cu was bound by soil solid phase in a sandy soil assumed to contain 0.8% OC, a water-filled porosity of 0.3, and an amount of 0.4 g biota kg-1 soil (33). Similarly, Weng et al. (18) reported that among the total bound metal only 0.3-2.9% Cu and 0.32.0% Ni are complexed with DOC in a sandy soil. For the soils used in this study, the OC content was >0.4% for Cu experiments and >1% for Ni experiments, and the contribution of the DOC in binding trace metal in soil systems is expected to be insignificant relative to that of the SOM. Therefore, the DOC can be reasonably excluded when computing metal ion activities in these soils. Estimation of the TBLM Parameters. In modeling acute Cu toxicity, De Schamphelaere and Janssen (24) and Steenbergen et al. (2) estimate KCuBL to be that giving the best linear correlation between the logit of the % mortality and f thereby using the full dose response (eq 3b). Instead of using a logit transformation which is not defined at R ) 0 and 100%, the TBLM parameters in this study were estimated by globally fitting the entire dose-response data for all the soils to eq 6. The Ni toxicity results showed that there was not a unique set of the TBLM parameters. However, β remained stable at approximately 2.60 and was fixed at this value to analyze the interdependence of the remaining parameters. Initially, no competing cations were included. Thus eq 6 becomes
R) 1+
(
100 KNiBL{Ni2+}
)
(7)
β
f50(1 + KNiBL{Ni2+})
The lack of a unique optimal set of parameters can be seen by examining the computed RMSE of the predicted % root elongation for the entire data set as a function of f50 and log KNiBL without the competing cations (Figure 2a). The nearly level trough rather than a unique minimum RMSE indicates that there is not a unique optimum solution. This result indicates that KNiBL{Ni2+} , 1 in eq 7 and the optimization is under constrained. Thus, either f50 or KNiBL must be fixed. However, the optimum ratio, KNiBL/ f50 indicated by the slope of the trough in the x-y plane in Figure 2a, is approximately constant. When f50 was varied from 0.005 to 0.5, estimated log KNiBL values varied from 1.58 to 3.88. The corresponding RMSE of predicted % root elongation was the same at 14.9. VOL. 40, NO. 22, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 2. RMSE of the predicted % root elongation as a function of log KNiBL (y-axis) and various TBLM parameters (x-axis): (a) f50, (b) log KCaBL, (c) log KHBL, and (d) log KHBL. In the calculations, the value of β is fixed at 2.60 for in all cases. None of the competitive cations were included in (a). Only Ca was included in (b), only H+ was included in (c), and both Ca2+ and H+ were included in (d) with log KCaBL fixed at 1.5. This shows that the model’s goodness of fit does not depend on the value of f50 that is chosen. For the prediction of EC50, the absolute values of f50 and KNiBL are not necessary. Only their relative values are required as shown below. From eq 7, the EC50 Ni2+ activity is given by
{Ni2+}50 )
f50
(8)
(1 - f50)KNiBL
Substitution of the estimated values of f50 and KNiBL in eq 8 results in an identical EC50 Ni2+ activity of 10-3.88 mol L-1 when f50 ) 0.005 or 0.5. A similar independence of the EC50 Ni2+ activity from the choice of f50 values is expected when the competitive cations are included. To date the BL sites have been analyzed only for fish gills (34) and f50 value is directly determined. For the rest of the organisms to which the BLM has been tested, f50 values are fitted model parameters. The f50 values estimated for aquatic and sediment BLMs show no apparent consistency. For BLM, f50 estimates were 0.39, 0.33, and 0.2 for Daphnia magna, Pimephales promelas, and earthworms, respectively, for acute Cu toxicity (2, 24, 35). For sediment BLM, reported f50 values were 0.002, 0.94, 0.03, 0.02, and 0.01 for Cu, Cd, Pb, Ni, and Zn, respectively, for acute metal toxicity to D. magna (14). We have selected f50 ) 0.05 to be used for further analyses as the literature offers no guidance for a reasonable f50 value for metal toxicity to higher plants. This value is between 0.005 and 0.5, which represent the orders of magnitude variation in the above-reported values resulting in identical EC50 Ni2+ activity. Next, the competitive effect of Ca, Mg, and protons was analyzed individually by their inclusion in eq 6 which becomes
R) 1+
(
100 KNiBL{Ni2+}
f50(1 + KNiBL{Ni2+} + KXBL{X})
)
β
(9)
where X is H+, Ca2+, or Mg2+, and KXBL is the corresponding conditional binding constant. When only Ca2+ is included in eq 9, there is not a unique combination of KNiBL and KCaBL which results in a unique minimum RMSE (Figure 2b). A 7088
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similar situation is observed with Mg2+ as the only competing cation (not shown). There is no apparent relationship between the optimum KNiBL and KHBL when only the protons were included as the competing cation (Figure 2c). However, when competition due to both protons and Ca2+ was considered with log KCaBL fixed at 1.5 (see below), a unique solution was observed (Figure 2d). These results can be understood by examining eq 6 under the condition KCaBL{Ca2+}or KMgBL{Mg2+}or KHBL{H+} . 1+ KNiBL{Ni2+}:
R) 1+
(
100 {Ni2+}
)
β
f50 (K {H+} + KCaBL{Ca2+} + KMgBL{Mg2+}) KNiBL HBL (10)
With f50 ) 0.05, only the ratios KHBL/KNiBL, KCaBL/KNiBL, and KMgBL/KNiBL can be determined. Fixing one of the four binding constants allows the determination of the relative values of the rest and facilitates a better illustration of the relative affinity of the cations to the biotic ligand. The log KCaBL values reported for Ca sorption on wheat root plasma membrane generally varies between 1.0 and 1.7 and an average value of 1.5 is used in modeling the sorption of cations (36). Since there appear to be no cation binding constants specifically associated with the barley root BL sites, we chose to fix log KCaBL ) 1.5. A unique combination of the rest of the parameters was then obtained (Table 1). A similar analysis for Cu toxicity showed that RMSE was insensitive to β, which was approximately equal to 1. Therefore, β ) 1 was fixed. No unique f50 and KCuBL are found (Figure A-2a in Supporting Information). The interdependence between these two parameters is similar to the interdependence of f50 and KNiBL (Figure 2a) indicating that KCuBL{Cu2+} , 1 in the analogous eq 6 for Cu and, therefore, f50 was fixed at 0.05. The competitive effect of protons is reflected in the significant decreases in the RMSE and an apparent minima at approximately log KCuBL ) 7.5 and log KHBL ) 6.5 (Figure A2-b in Supporting Information). Similar analyses were carried out with either Ca2+ or Mg2+ as the only competing
TABLE 1. Model Fit Summary and the Optimum Parameters (( Standard Errorsa) Associated with the Three Models for Ni and Cu Toxicities to Barley Root Elongation dose response parameters metal model Ni Cu
TMM FIAM TBLM TMM FIAM TBLM
EC50 or f50 kg-1
306.3 ( 61.6 mg Ni (1.44 ( 0.15) × 0-4 mol L-1 0.05c 156.8 ( 16.7 mg Cu kg-1 (1.50 ( 0.44) × 10-7 mol L-1 0.05c
model fit β
RMSEb
1.18 ( 0.28 1.50 ( 0.23 2.37 ( 0.33 1.61 ( 0.28 0.54 ( 0.28 0.96 ( 0.11
28.3 13.6 10.8 23.4 20.2 15.4
cR2
conditional binding constants (log)
KCuBL
KNiBL
KHBL
KMgBL
KCaBL
0.53 0.89 0.93 3.60 ( 0.53 4.52 ( 0.62 3.81 ( 0.60 1.50d 0.66 0.75 0.85 7.41 ( 0.23 6.48 ( 0.26
a Estimated using SOLVER AID (29). b Root-mean-squared error of the predicted % root elongation. c The coefficient of determination of the linear regression between the predicted and observed % root elongation. d Fixed values (see text).
FIGURE 3. Ni toxicity to barley root elongation. Dose-response relationships (first row): (i) the TMM, (ii) the FIAM, (iii) the TBLM. Predicted vs measured root elongation (second row) based on (i) the TMM, (ii) the FIAM, (iii) the TBLM. The lines represent the models (first row). The dotted lines represent the 1:1 ratio. The linear regression relationships between the predicted and the observed % root elongation (solid lines) are also shown (second row). cations (Figure A2-c and A2-d in Supporting Information). There is no strong dependence of optimum log KCuBL on either log KCaBL or log KMgBL. When all of the competitive cations (protons, Ca2+, and Mg2+) were included in the parameter estimation with only f50 ) 0.05 fixed, the optimum values were β ) 0.96, log KCuBL ) 7.41, log KHBL ) 6.48, and log KCaBL ) log KMgBL = 0. Therefore, for the final Cu TBLM only the protons were included as a competing cation. The resulting optimum parameters are listed in Table 1. Additionally, the optimum parameters for the TMM and the FIAM are also presented in Table 1 for both Ni and Cu. Future users of the conditional binding constants in Table 1 should note that strictly speaking these constants are only consistent with the conditions of this study. Dose-Response Relationships. Nickel Toxicity. Based on the RMSE of the predicted % root elongation for the Ni toxicity models (Table 1), the FIAM fits the data better than the TMM, indicating that the Ni2+ ions in the pore waters represent the bioavailability much better than the soil metal concentration (background + added). However, the interaction of other cations with the BL, which the TBLM explicitly considers, reduces the RMSE from 13.6 for the FIAM to 10.8 for the TBLM. The dose-response relationships and the predicted vs observed % root elongations are compared in Figure 3. The slope, the intercept, and the R2 of linear regressions in Figure 3 (second row) indicate the goodness of fit of the models. The predictions by the TMM deviate significantly from the 1:1 line and the R2 is only 0.53 (F ) 61.3, P < 0.001). The FIAM provides considerable improvement with R2 of
0.90 (F ) 441, P < 0.001). The predictions by the TBLM are very close to the 1:1 line with an R2 of 0.93 (F ) 739, P < 0.001). Copper Toxicity. The FIAM fits the data better than the TMM and the inclusion of the competitive binding of protons to the BL in the TBLM improves the data fit significantly (Table 1). The RMSE is further reduced from 20.2 (FIAM) to 15.4 (TBLM) and the R2 value increases from 0.75 (F ) 225, P < 0.001) for the FIAM to 0.85 (F ) 435, P < 0.001) for the TBLM. The corresponding dose response relationships are shown in Figure 4 (first row). Of the three predictions, that for the TBLM clearly shows the best fit as the intercept is nearest to 0, the slope is nearest to 1, and the R2 value is the highest in the linear regressions of the predicted vs observed % root elongations (Figure 4 second row). Interaction of Cations with the Biotic Ligand. Figure 5 shows the relationship between the EC50 Cu2+ activities computed by WHAM VI whole soil speciation using as inputs the SOM, EC50 soil Cu concentration, and the interpolated soil solution properties (Table A-2 in Supporting Information). As the H+ activity increases (i.e., pH decreases), the EC50 Cu2+ activity increases (i.e., the toxicity decreases). Clearly, the Cu toxicity to barley root elongation is mitigated by protons. This mitigation is correctly reflected by the TBLM predictions (solid line in Figure 5). In contrast, the FIAM prediction is a constant irrespective of H+ activity (dotted line in Figure 5). Additionally, as the H+ activities increase by approximately four log units (i.e., four units decrease in pH), the EC50 Cu2+ activity also increases by approximately VOL. 40, NO. 22, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 4. Cu toxicity to barley root elongation. Dose-response relationships (first row): (i) the TMM, (ii) the FIAM, (iii) the TBLM. Predicted vs measured root elongation (second row) based on (i) the TMM, (ii) the FIAM, (iii) the TBLM. The lines represent the models (first row). The dotted lines represent the 1:1 ratio. The linear regression relationships between the predicted and the observed % root elongation (solid lines) are also shown (second row).
FIGURE 5. Relationship between the Cu2+ and H+ activities at EC50 for barley root elongation. The observed data (b) based on WHAM VI speciation and the model predictions based on the FIAM (dotted line) and the TBLM (solid line) are shown.
FIGURE 6. Fraction (f) of the total biotic ligand on the barley roots bound by various cations (Ni2+, H+, Ca2+, and Mg2+) at Ni EC50 for the 8 soils.
four units. This observed 1:1 linearity between the EC50 pCu () -log {Cu2+}) and pH for pH < 6 in Figure 5 is compatible with the model of 1:1 interaction between the cations and the BL as assumed in the model development. Similar competitive effects of protons and the lack of competitive effects of Ca and Mg have been reported in the accumulation of Cu by plant roots (10, 37). The absence of appreciable competitions by Ca2+ and Mg2+ does not, however, mean that they are entirely absent. It just indicates that their competitive effects, if any, are insignificant compared to that of the protons for the conditions of these experiments. In the case of Ni toxicity, it appears that the protons, Ca2+, and Mg2+ all compete with Ni2+ to bind to the BL. These competitive effects have been observed in hydroponic studies assessing Cu and Zn toxicity to wheat root elongation (12). Whether the BLs for Ni and Cu are the same or not, their relative affinity for the cations appears to follow the order Cu2+ > H+ for Cu and H+ > Ni2+ ≈ Mg2+ > Ca2+ for Ni (Table 1). This order is similar to those associated with the BL for aquatic invertebrates (5, 24, 35) and consistent with studies showing protons to be 70 times more effective than Ca and Mg in alleviating Cu rhizoxicity to hydroponically grown wheat (12). Unlike the significant improvements in the data fit by accounting for cation competition in this study, Hough et al. (38) observed only slight improvements and Dutta and Young
(39) observed no improvements in the data fit when proton competition was considered in an extension of the FIAM in the uptake of metals by plants. The apparent lack of model improvements with the incorporation of proton competition in these studies can be explained by Figure 6, which shows the fraction of the total BL sites bound by each of the cations at the 50% effect level for each soil in Ni toxicity study. These fractions were calculated by using eq 5, the binding constants (Table 1), and the activities of the cations interpolated at EC50. This figure shows that protons and Mg dominate the occupancy of the biotic ligand sites. The soils (left to right) have increasing soil pH. While fH decreases with increasing pH, fMg is significant at all pHs and more pronounced at higher pH values. The relative occupancy of the BL sites by these cations reflects their binding affinity and cation activities. Therefore, the protons exert their competition only at low pH conditions. Consequently, its overall effect may not have been significant at higher pH values (6.2-7.2) in Dutta and Young (39). Even at low pH conditions the competitive effect of the protons may be suppressed if the Mg and Ca activities are high, which may be the case in Hough et al. (38). These types of interactions may explain why the competitive effect of protons may not be apparent for a given condition, which the TBLM is able to recognize. The limitations of the model developed here arise out of the inherent difficulty in controlling the pore water chemistry in the experimental designs involving soils, such as in this study. Of primary importance is the ionic strength which
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FIGURE 7. Comparison of the predicted vs observed EC50 soil metal concentrations for Cu toxicity (open symbols) and Ni toxicity (filled symbols) to barley root elongation based on (a) the TMM, (b) the FIAM, and (c) the TBLM. The solid lines represent the 1:1 ratio and the dotted lines represent a factor of 2 variations above and below the 1:1 lines. The linear regression relationships between the predicted and observed EC50 values (log transformed) are also given. may increase in the pore water as the metal dose increases. Ionic strength may affect the root growth indirectly by altering the pore water chemistry and directly by affecting the electrostatic binding of cations by root surfaces (40). Another concern may be the effect of aluminum (Al) on root growth, particularly at low pH conditions. Kinraide et al. (12) reported Al3+ to be more toxic than H+ but less toxic than Cu2+. In such a case, the amelioration of Cu2+ toxicity by Al3+ could be confounded with apparent amelioration by H+ since Al3+ activity is correlated with pH. The performance of the TBLM developed here appears to be independent of this distinction between proton and Al3+ effect. The hydroponic investigations of Al, Cu, and Zn rhizotoxicity, the protective effects of Ca and Mg (12, 23), and the studies of cation binding to root plasma membranes (40) may be used to resolve and/or refine the current TBLM for the barley root elongation. However, the possible effect of Al3+, which cannot be isolated in this study, and the effects of the remaining cations, should ultimately be isolated and directly verified, using studies similar to that of Steenbergen et al. (2), to lend credence to the theory behind the TBLM. Prediction of EC50. Ultimately the TBLM can be used to predict the soil concentrations that are ecologically protective, as is the case with the aquatic BLM that is being used to establish the EPA freshwater Cu criteria (41). Setting R ) 50% in eq 6 and rearranging, the EC50 M2+ activity is given by
{M2+}50 )
f50 (1 - f50)KMBL
(1 + KHBL{H+}50 +
KCaBL{Ca2+}50 + KMgBL{Mg2+}50) (11) where the subscript 50 represents the activities of cations corresponding to 50% inhibition of root length. Using the soil solution activities of H+, Mg2+, and Ca2+ in eq 11, {M2+}50 can be predicted. This predicted {M2+}50 is then fixed in the WHAM VI whole soil model to compute the EC50 soil metal concentration (mg metal kg-1 soil) using the specific soil properties SOM, and solution pH, Mg2+, and Ca2+ activities. In addition, similar EC50 soil metal concentrations can be calculated using the FIAM predicted EC50 M2+ activities (Table 1). The EC50 soil metal concentrations predicted by the three models for Cu and Ni toxicities to barley root elongation are compared in Figure 7. The TMM predictions are constant (156.8 mg Cu kg-1 soil and 306.3 mg Ni kg-1 soil) and show no correlation with the observed values (Figure 7a). The FIAM predictions (Figure 7b) were based on WHAM VI whole soil speciation using the constant EC50 Cu2+ activity (1.50 × 10-7 mol L-1) and EC50 Ni2+ activity (1.44 × 10-4 mol L-1) for all soils and their respective properties. These predictions (Figure 7b) show a better correlation with the observed values than
the TMM predictions. The TBLM predictions show the best correlation with the observed EC50 soil metal concentrations (Figure 7c). The TMM fails to account for the effects of partitioning and the competitive effects of cations at the BL sites in Cu and Ni toxicities to barley root elongation. Therefore, it is unable to explain the variability of the EC50 concentrations for Cu and Ni between soils (Figure 7a). The FIAM accounts for the effects of partitioning and thus is able to estimate soil-specific EC50 values. Consequently, as evidenced in Figure 7b, the FIAM is able to explain some of variability in the EC50 Cu concentrations (R2 ) 0.49) and most of the variability in the EC50 Ni concentrations (R2 ) 0.90). The TBLM accounts for the effects of competitive cations at the BL sites, in addition to the effects of partitioning. Therefore, the TBLM achieves the best overall relationship between the predicted and observed EC50 soil metal concentrations as indicated by the slope (the closest to one) and the intercept (the closest to zero) of the linear regressions and the R2 values (the greatest values) (Figure 7). The RMSEs of the predicted EC50 soil metal concentrations (log transformed) for Cu and Ni were 0.24 and 0.15, respectively. These RMSE values represent a factor of less than two variation on a linear scale. This level of precision in the predictions is similar to that of the aquatic BLM (5, 24, 35) and is remarkable given the wide ranging properties of the soils involved. An empirical method to set critical metal loading in soils has been reported by Lofts et al. (11). Their method relies heavily on linear regressions to predict metal activities from soil properties without regard to the covariance of the soil properties themselves. Other empirical models have correlated a critical metal concentration (e.g., EC50) to various soil properties such as pH and cation exchange capacity (25, 42) without directly linking the soil chemistry to the biological endpoints. Unlike these empirical relationships, the TBLM provides a theoretical basis to directly relate a toxicological endpoint to the soil solution chemistry, which in turn is directly linked to the soil properties. Therefore, a TBLM provides a theoretical and practical means to compare the wide variation in toxicological responses among soils by accounting for the interactions between soil, soil solution, and biota. Our findings indicate that the TBLM is a reasonable framework for directly linking Cu and Ni chemistry in soils and their ecotoxicity. Once reasonable speciation models for calcareous soils and low OC content soils are available, these can be integrated into the current framework to encompass all types of soils. The TBLM approach is applicable to a wide range of noncalcareous soils under experimental conditions similar to those in this study and has been applied to additional biological endpoints for both Ni and Cu with similar level of performance as in this study (43). This direct linkage of metal chemistry and biological effect in soils has VOL. 40, NO. 22, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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the potential for providing a general theoretical framework to model metals ecotoxicity in terrestrial systems and to predict critical metal concentrations specific both to a soil and a biological endpoint.
Acknowledgments The Center for the Study of Metals in the Environment (CSME), University of Delaware, the International Copper Association (ICA), and the Nickel Producers Environmental Research Association (NiPERA) funded this work. Rothamsted Research receives grant-aided support from the UK Biotechnology and Biological Sciences Research Council. We thank E. Smolders and K. Oorts, Division of Soil and Water Management, K.U. Leuven, Belgium, for soil collection, processing, and analysis of soil properties.
Supporting Information Available Additional background information, experimental and model details, and tables of data. This material is available free of charge via the Internet at http://pubs.acs.org.
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Received for review May 16, 2006. Revised manuscript received August 17, 2006. Accepted September 13, 2006. ES061171S
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