SEARCH

SEARCH BY CITATION

Keywords:

  • Copper;
  • Hydroxyl copper;
  • pH;
  • Biotic ligand model

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. Acknowledgements
  7. Reference

The effect of pH on the acute toxicity of Cu to barley (Hordeum vulgare) root elongation was investigated in solution culture. The results showed that the median effective concentrations (EC50s; i.e., the concentration that reduced root elongation by 50% based on free Cu2+ activity) were not significantly different in the low-pH range from 4.5 to 6.5, but in the high-pH range from 7.0 to 8.0, a significant effect of pH on EC50s was found. The nonlinear relationship between EC50 and H+ activity in the present study indicated that the increased toxicity with increasing pH in solution may not be caused by decreasing H+ competition. When we take account of CuOH+ activities, a good linear relationship (r2 > 0.97) between the ratio of CuOH+ activity to free Cu2+ activity and acute Cu toxicity to barley root elongation was achieved, which indicated that the observed toxicity in the high-pH range may be caused by CuOH+ plus free Cu2+ in solution. Linear-regression analysis suggested CuOH+ had a greater binding affinity than Cu2+ at the biotic ligand sites. The logistic dose–response curve showed that expressing the Cu dose as Cu2+ + 2.92·CuOH+ improved the data fit significantly compared to consideration of the free Cu2+ activity only. Thus, our results suggest CuOH+ was highly toxic to barley root elongation. The enhanced toxicity of CuOH+ therefore needs to be considered when modeling the effect of pH on Cu toxicity to barley for exposures having pH greater than 6.5.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. Acknowledgements
  7. Reference

Current soil-quality criteria and risk assessment of metals in soils are based on total or soluble metal concentrations. A large body of evidence, however, indicates that both total and soluble metals are not directly related to ecotoxicity [1,2]. Toxicity is affected by the bioavailability of the metal in the soil and by concentrations of other elements that moderate toxicity responses. Because the uptake of metals by plants occurs mainly via the soil solution and may lead to acute toxicity when metal concentrations are high, appropriate ecological risk assessment of metals must pay more attention to the speciation of metals in soil solutions [3].

Many studies have examined the link between solution metal speciation and uptake/toxicity of metals to organisms; thus, the free metal activity model (FIAM) was developed [4]. The concept of the FIAM was comprehensively reviewed by Pa-genkopf [5], Campbell and Stokes [6], and Brown and Markich [7]. The hypothesis that the activity of the free metal ion controls toxicity responses by organisms has been challenged, however, and several studies have found that pH and other cations can affect the toxicity of metal to plants and microbes [6–8].

Recently, a biotic ligand model (BLM) was developed to predict metal toxicity in aquatic ecosystems. The most important assumption of the BLM is that metal toxicity is caused by the free metal ions reacting with biological binding sites; H+, Ca2+, Mg2+, K+, and Na+ may compete with metal ions for these binding sites and decrease the toxicity of the free metal ions. The BLM has been successful in predicting metal bioavailability and toxicity as a function of water chemistry [9,10]. In recent studies, the BLM approach has been used to predict metal toxicity in terrestrial systems [11–17]. No prediction of toxicity at alkaline pH, however, was reported in these papers. In most BLMs developed for Cu, Ni, and Zn, the relationship between H+ and the median effective concentration (EC50; the free ion activity required to reduce plant or root growth by 50%) suggests that competition exists between these cations [12,13,18–20]. It also has been reported, however, that H+ activity has no significant effect on metal toxicity [10,14,15]; therefore, incorporating proton competition into the BLM is unjustified. The recent Cu-BLM developed for aquatic ecosystems assumed that increased toxicity per unit free Cu2+ as a function of increasing solution pH may result not only from the H+ competition at the biotic ligand (BL) sites but also from the formation of hydroxyl Cu ion (CuOH+) [10]. To our knowledge, no data are currently available to assess the possible toxicity of CuOH+ to plants in solution. The present study therefore aimed to investigate the effect of H+ competition on the toxicity of Cu2+ to barley (Hordeum vulgare) root elongation across a wide range of pH conditions and to determine if hydroxyl Cu species are implicated in toxicity responses.

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. Acknowledgements
  7. Reference

Solution composition

Root elongation in solution culture was used to examine the phytotoxicity of Cu2+ as affected by solution pH and solution Cu speciation. For all exposures, the concentration of calcium (as CaCl2) was 0.2 mM. Solution pH was maintained constant by buffers. At pH less than 7.0, MES (2-[N-mor-pholino] ethane sulfonic acid, 1 mM) was used; at pH 7.0 or greater, MOPS (3-[N-morpholino] propane sulfonic acid, 3.6 mM) was used. The buffers of MES and MOPS did not form the complexes with Cu [14,15]. The pH was adjusted to the desired value using 1 M NaOH and 1 M HCl. At each pH value there were six concentrations of Cu (as CuCl2) plus one treatment without added Cu as a control. All solutions were made using deionized water. The concentration of Cu in solution, which ranged from 0.08 to 6.30 μM, was verified using a graphite-furnace atomic absorption spectrophotometer (Varian AA240FS/GTA120; Melbourne, Australia), and the pH of the solutions was determined using a pH meter (Delta 320; Mettler, Zurich, Switzerland).

Toxicity assays

Barley seeds (Hordeum vulgare cv. Pinggu No. 1) were germinated at 20°C in the dark for 24 h on filter paper moistened with distilled water. When the radicle emerged (length, <2 mm), six seeds were transplanted to a nylon net that was fixed on the surface of plastic culture pots containing 250 ml of test solution. The test solution was completely changed every 2 d to maintain the composition. Solutions were unstirred instead of agitation and aeration by bubbling air through the solution, because no difference between unstirred and stirred systems was identified during preliminary experiments of Cu toxicity to barley at fixed Cu concentrations of 0.2, 0.4, and 0.8 μM at pH 6. Unstirred solutions also were preferred to mimic diffusional limitations to metal uptake in soils.

The culture pots were placed randomly in the growth chamber. The air temperature was 20°C during the 16:8-h lightdark cycles. The lighting was 22 klux in the growth chamber. After 5 d, root length was measured. The percentage of barley root elongation with respect to controls (RE, %) in a test medium was calculated as

  • equation image(1)

where REt is the root length in the test medium and REc is the root length in the control.

Calculation and statistics

Speciation was calculated by WHAM 6.0 (Windermere Humic Aqueous Model [21]) with adjusted stability constants for the inorganic Cu complexes [22]. Input data for WHAM were pH and concentrations of Cu, Ca, Na, and Cl. The Na ions in solution originated solely from the use of NaOH for pH adjustment. Because the experiments were carried out in an open system, a CO2 partial pressure of 3.5 × 10−4 atm was assumed in the calculation of WHAM [21]. The observed EC50 (5 d) was calculated from the root growth at each total Cu concentration in solution and free Cu2+ ion activity by fitting a logistic model. The dose-response curves were plotted in terms of Cu2+ + CuOH+ (OH-BLM) and free Cu2+ activity (FIAM), also by fitting a logistic model. When comparing different models, the lower value of the root-mean-squared error is indicative of a better model:

  • equation image(2)

where n is number of data, Rpredicted is the predicted RE (as % control), and Robserved is the observed RE (as % control).

Mathematical description of the BLM

The total number of Cu-binding sites on the BL is called the complexation capacity of the BL (TBL). According to the BLM concept, the concentration of Cu bound to the BL can be expressed as a function of Cu2+, Ca2+, H+, Mg2+, and Na+. So, the balance equation on the BL can be written as

  • equation image(3)

where TBL is, as mentioned, the complexation capacity of the BL (mol/L), [XBL] is the concentration of the specific cation-BL complex (mol/L), and [BL] is concentration of unoccupied BL sites (mol/L).

According to the BLM assumption, when the competing cations H+, Ca2+, Mg2+, and Na+ are considered, the fraction (f) of the TBL sites bound by Cu2+ is then given by

  • equation image(4)

where Kxbl is the conditional binding constant for the binding of cation X to the BL sites (L/mol) and curly brackets ({}) indicate ion activity, such as {Xn+}, which is the activity of Xn+ (mol/L). Binding constants are formulated, using Cu as an example, as follows:

  • equation image(5)

Equation 4 can be rewritten as

  • equation image(6)

where EC50(Cu2+) is the free Cu2+ activity that results in 50% RE (50% of barley root elongation with respect to controls) and Fcubl50% is the fraction of the BLs that results in 50% RE when occupied by Cu. Equation 6 shows that linear relationships should be observed between EC50(Cu2+) and the activity of one cation when other cation activities are kept constant.

RESULTS AND DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. Acknowledgements
  7. Reference

Dose-response relationships (logistic curves) were established for both total Cu concentrations in solution and free Cu2+ activity in the range of test pH values. The EC50s at each pH are given in Table 1. A summary of the dose-response relationships, highlighting the range of RE values under conditions of varied pH, are provided in Figures 1 and 2 for total Cu and free Cu2+ activity, respectively.

In the pH range of 4.5 to 8.0, the observed EC50 varied from 0.48 to 1.52 μM for total Cu and from 0.05 to 0.49 μM for free Cu2+ activity (Table 1). The results were in the range reported for Cu rhizotoxicity in the literature. Lock et al. [23] assessed pH effects on Cu toxicity to barley (H. vulgare) in nutrient solution and found the calculated EC50(Cu2+) in solution to vary from 0.08 to 0.44 μM in the pH range of 4.46 to 7.74. Parker et al. [24] carried out similar root elongation experiments on wheat seedlings using a growth solution with a background of CaCl2 (0.2 mM) and buffered at pH 6 with MES (0.25 mM), and those authors found the calculated EC50 for total Cu (EC50(CuT)) in solution to be approximately 0.25 μM. A greater value for wheat was found by Taylor et al. [25], with a calculated EC50 in their study of approximately 7.5 μM. Fortunati et al. [26] observed that the EC50 of free Cu2+ activity to wheat seedlings was 0.39 μM at a solution pH of 4.3. Antunes et al. [17] reported that the EC50 of free Cu2+ activity varied from 0.26 to 0.39 μM for hydroponically grown barley exposed to solutions having pH 6.0.

Table Table 1.. Median effective concentration (EC50) expressed as total copper (CuT) and free Cu2+ (Cu2+) activity and 95% confidence interval (CI)
  95% CI95% CI 
Treatment (pH)EC50 (CuT) (μM)Min.Max.EC50 (Cu2+) (μM)Min.Max.
4.500.500.400.630.450.360.56
5.000.500.420.590.440.370.52
5.500.480.410.560.410.350.48
6.000.600.470.770.470.360.60
6.500.780.660.910.490.420.57
7.000.920.471.820.350.180.70
7.501.110.671.820.170.100.27
7.751.320.802.180.100.060.17
8.001.520.952.460.050.030.09

The EC50(CuT) in solution was related to solution pH (Table 1 and Fig. 1). When the pH increased from 4.5 to 8.0, EC50(CuT) was increased by a factor of three, suggesting that pH affects Cu toxicity either through speciation changes or through ion-competition effects. Studies in aquatic toxicology showed that the speciation of metals greatly affects their toxicity, with the activity of the free metal ion species being most important [4]. The values of EC50(Cu2+) were not significantly different (p > 0.05) when the pH increased from 4.5 to 6.5; however, when the pH increased from 7.0 to 8.0, the values of EC50(Cu2+) decreased significantly (p < 0.05) with increasing solution pH (Table 1 and Fig. 2). In some studies [6,27,28], toxicity of Cu was considered to be related to the activity of the free Cu2+ ion and to the Cu2+ activity combined with H+ activity to account for proton competition with Cu for binding sites at the cell surface ligands on the biological membrane at low solution pH. Based on this model, the relation between H+ and EC50(Cu2+) should be linear (Eqn. 6). It was found, however, that EC50(Cu2+) was nonlinear with pH and H+ activity (Fig. 3), which suggests that H+ competition with Cu on cell membrane ligands of barley roots may not be consistent.

thumbnail image

Figure Fig. 1.. The percentage of barley (Hordeum vulgare) root elongation with respect to control (RE) as a function of total Cu in the solution pH range of 4.5 to 8.0. Each series point represents the RE at the corresponding solution pH. The solid line is the fitted logistic curve based on data from all pH treatments.

Download figure to PowerPoint

thumbnail image

Figure Fig. 2.. The percentage of barley (Hordeum vulgare) root elongation with respect to control (RE) as a function of free Cu2+ activity (pCu2+ = -log Cu2+) in the solutions with pH 4.5 to 6.5 (A) and in the solutions with pH 6.5 to 8.0 (B). Each series point represents the RE at the corresponding solution pH. The solid lines are the fitted logistic curves at the corresponding solution pH values.

Download figure to PowerPoint

In some studies of Cu toxicity to terrestrial plants, H+ was clearly observed to decrease Cu toxicity [8,12,13,29]. For example, Cheng and Allen [29] found free Cu2+ partitioning between roots of lettuce (Lactuca sativa) and nutrient solution was linearly related to H+ concentrations in the solution. Also, Voigt et al. [8] found that H+ inhibited rhizotoxicity of Cu to lettuce. In contrast, it was reported that difficulties occurred when H+ competition was used to explain the pH effects on Cu toxicity to barley root elongation [23]. De Schamphelaere and Janssen [10] also found a poor linear correlation between EC50(Cu2+) and H+ activity in the pH range of 5.98 to 7.92. Parker et al. [24] found that H+ alleviated Cu toxicity to wheat by measuring the root growth in solutions with pH 4.5, 5.5, and 6.5 at a fixed Cu2+ activity of 0.5 μM; unfortunately, the EC50s for Cu2+ activity at these pHs were not examined. To examine the differences between our results and those of Parker et al. [24], we performed 5-d root elongation tests with wheat in a medium of 0.2 mM CaCl2 to examine the change of EC50(Cu2+) at solution pH 4.5 and 5.5. The increase in H+ activity at pH 4.5 did not increase root elongation significantly at Cu2+ activities of less than 10−6 M (Fig. 4). We also found the EC50(Cu2+) for wheat was not significantly different between pH 4.5 (0.44 μM) and pH 5.5 (0.31 μM).

thumbnail image

Figure Fig. 3.. Median effective concentration expressed as minus common logarithm of free Cu2+ activity (EC50(pCu2+)) for barley (Hordeum vulgare) root elongation and the ratio of CuOH+ activity to free Cu2+ activity as a function of pH. Error bars indicate 95% confidence intervals.

Download figure to PowerPoint

The values of EC50(Cu2+) decreased significantly (p < 0.05) at higher solution pH (7.0–8.0) (Table 1 and Fig. 2). This presents evidence that the change in the concentration of H+ affects Cu toxicity at such high solution pH, because most nutrient solution experiments reported in the literature have not examined effects of H+ on Cu toxicity at pH greater than 7.0. A change in solution pH from 7.0 to 8.0, however, has a marked effect on Cu speciation. From speciation predictions, CuOH+ plus Cu2+ were the main Cu species to explain the pH effect on Cu toxicity. With increasing solution pH, the values of EC50(Cu2+) decreased markedly, but the Cu2+ activity decreased. Meanwhile, activities of CuOH+ species increased markedly, and the ratio of CuOH+ to Cu2+ followed closely the decline in EC50(Cu2+) (increased apparent toxicity of Cu2+) (Fig. 3). It is difficult to separate pH effects on toxicity from pH effects on solution speciation, because both occur concurrently. The lack of a pH effect on EC50(Cu2+) up to solution pH 6.5, however, suggests that proton competition at the BL was not strong, and the sharp reduction in EC50(Cu2+) once the solution pH increases sufficiently to hydrolyze Cu and create hydroxy species suggests some direct link between rhizotoxicity and activities of CuOH+.

To determine the effects of CuOH+ species on the toxicity to barley root elongation, CuOH+ was included in the BLM construct. The contribution of CuOH+ to barley root rhizotoxicity can be accommodated best in terms of the formation of a CuOH+-BL complex (CuOHBL). In this case, toxicity will be determined by the total amount of Cu bound to the BL, being the sum of CuBL+ and CuOHBL. When the concentration of Ca2+ is too low to have a competitive effect [10] and other cations are excluded from consideration, Equation 4 can be transformed to

  • equation image(7)

Equation 6 can be rewritten as

  • equation image(8)
thumbnail image

Figure Fig. 4.. The percentage of wheat root elongation with respects to control (RE) as a function of free Cu2+ activity at solution pH 4.5 and 5.5. The solid and dotted lines are the fitted logistic curves at pH 4.5 and 5.5, respectively. Vertical bars represent the standard error.

Download figure to PowerPoint

Equilibrium equations of Cu2+ + OH+ = CuOH+ (log10KCuOH+ = 6.48) can be expressions of the form

  • equation image(9)

The value of KCuOH+ was from WHAM 6.0 [21]. From Equations 8 and 9, if CuOH+ contributes to Cu toxicity, a linear relationship should exist between CuOH+/Cu2+ and 1/EC50(Cu2+) in the pH range where no significant cation competition occurs. We found a good linear relationship (r2 > 0.99) between CuOH+/Cu2+ and 1/EC50(Cu2+) in the pH range of 4.5 to 8.0, demonstrating that the Cu toxicity likely was caused by rhizotoxicity of both Cu2+ and CuOH+ (Fig. 5). These data suggest that the toxicity of CuOH+ should be considered in the terrestrial Cu-BLMs. The toxicity of CuOH+ to freshwater organisms was supposed when some BLMs for freshwater organisms were developed [10,30–32].

Linear-regression analysis of 1/EC50(Cu2+) versus OH (Eqn. 8) gives

  • equation image(10)

Comparing Equation 8 with Equation 10, the values of the slope and intercept can be expressed as

  • equation image(11)
  • equation image(12)

Dividing Equation 12 by Equation 11 gives Equation 13:

  • equation image(13)

Equation 13 indicates that the ratio of the conditional binding constants of CuOH+ (KCuOHBL) and Cu2+ (KCuBL) at the BL is 2.92. Hydroxyl Cu (CuOH+) has threefold greater binding affinity compared to that of Cu2+, which from a mechanistic point of view means that CuOH+ is more toxic than Cu2+, although it was suggested that the toxicity of CuOH+ is two-to fourfold lower relative to that of Cu2+ to aquatic organisms [10,32,33]. Similarly, higher affinity of monohydroxylated metal ions for adsorption on soils and soil components compared to that of hydrated metal ions has been suggested [34,35]. The high toxicity of CuOH+ compared with Cu2+ probably results from easy dehydration, diffusion, and penetration through the membrane of plant cells. Lock et al. [23] showed that the EC50(Cu2+) to barley increased fivefold when pH increased from 4.46 to 7.74. When their data are recalculated by taking the CuOH+ species into account, their results also show that CuOH+ has a threefold greater binding affinity compared with that of Cu2+ to barley roots. This value is consistent with the result in the present study.

thumbnail image

Figure Fig. 5.. The linear-regression relationships between the median effective concentration (EC50) and the ratio of CuOH+ activity to free Cu2+ activity in the pH range of 4.5 to 8.0.

Download figure to PowerPoint

From Equation 13, root elongation is proportional to Cu2+ + 2.92·CuOH+. Therefore, the dose-response was plotted in terms of Cu2+ + 2.92 · CuOH+ (OH-BLM) based on the logistic model, and the dose-response also was plotted in terms of free Cu2+ (FIAM) (Fig. 6). The OH-BLM was found to better predict Cu rhizotoxicity than the FIAM (Fig. 6) based on root-mean-squared error and r2 values. The root-mean-squared error was reduced from 17.67 (FIAM) to 7.44 (OH-BLM), and the r2 value increased from 0.78 for the FIAM to 0.96 for the OH-BLM. Also, the OH-BLM clearly showed the best fit with the observed versus predicted intercept nearest zero and the slope nearest one. These results suggest that the toxicity change across this wide range of solution pH values results from rhizotoxicity of Cu2+ and CuOH+. Thus, models to explain pH effects on Cu toxicity to barley need to consider the formation and higher apparent toxicity of CuOH+ at solution pH greater than 6.5.

thumbnail image

Figure Fig. 6.. Toxicity of Cu to barley (Hordeum vulgare) root elongation expressed as different dose–response curves: (A) the dose as only Cu2+ activities, and (B) the dose as Cu2+ + 2.92·CuOH+. The lines are the fitted logistic curve based on all treatments. Also shown are predicted versus observed root elongation (C) based on the dose as only Cu2+ activities and (D) based on the dose as Cu2+ + 2.92·CuOH+. The dotted lines represent the 1:1 ratio, and the solid lines represent the linear-regression relationships between the predicted and the observed root elongation.

Download figure to PowerPoint

Acknowledgements

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. Acknowledgements
  7. Reference

This work received financial support from the Natural Science Foundation of China (project 20677077 and 40620120436), the International Copper Association, Rio Tinto Limited, and the Nickel Producers Environmental Research Association.

Reference

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. Acknowledgements
  7. Reference
  • 1
    Smolders E, Buekers J, Oliver I, McLaughlin MJ. 2004. Soil properties affecting toxicity of zinc to soil microbial properties in laboratory-spiked and field-contaminated soils. Environ Toxicol Chem 23: 26332640.
  • 2
    Broos K, Warne MStJ, Heemsbergen D, Stevens D, Barnes MB, Correll RL, McLaughlin MJ. 2007. Soil factors controlling the toxicity of copper and zinc to microbial processes in Australian soils. Environ Toxicol Chem 26: 583590.
  • 3
    Nolan AL, McLaughlin MJ, Mason SD. 2003. Chemical speci-ation of Zn, Cd, Cu, and Pb in pore waters of agricultural and contaminated soils using Donnan dialysis. Environ Sci Technol 37: 9098.
  • 4
    Morel FMM. 1983. Principles of Aquatic Chemistry. Wiley InterScience, New York, NY, USA.
  • 5
    Pagenkopf GK. 1983. Gill surface interaction model for trace metal toxicity to fishes: Role of complexation, pH and water hardness. Environ Sci Technol 17: 342347.
  • 6
    Campbell PGC, Stokes PM. 1985. Acidification and toxicity of metal to aquatic biota. Can J Fish Aquat Sci 42: 20342049.
  • 7
    Brown PL, Markich SJ. 2000. Evaluation of the free ion activity model of metal–organism interaction: Extension of the conceptual model. Aquat Toxicol 51: 177194.
  • 8
    Voigt A, Hendershot WH, Sunahara Gl. 2006. Rhizotoxicity of cadmium and copper in soil extracts. Environ Toxicol Chem 25: 692701.
  • 9
    Di Toro DM, Allen HE, Bergman HL, Meyer JS, Paquin PR, Santore RC. 2001. Biotic ligand model of the acute toxicity of metals. 1. Technical basis. Environ Toxicol Chem 20: 23832396.
  • 10
    De Schamphelaere KAC, Janssen CR. 2002. A biotic ligand model predicting acute copper toxicity for Daphnia magna: The effects of calcium, magnesium, sodium, potassium, and pH. Environ Sci Technol 36: 4854.
  • 11
    Antunes PMC, Berkelaar EJ, Boyle D, Hale BA, Hendershot W, Voigt A. 2006. The biotic ligand model for plants and metals: Technical challenges for field application. Environ Toxicol Chem 25: 875882.
  • 12
    Thakali S, Allen HE, Di Toro DM, Ponizovsky AA, Rooney CP, Zhao FJ, McGrath SP. 2006. A terrestrial biotic ligand model. 1. Development and application to Cu and Ni toxicity to barley root elongation in soils. Environ Sci Technol 40: 70857093.
  • 13
    Thakali S, Allen HE, Di Toro DM, Ponizovsky AA, Rooney CP, Zhao FJ, McGrath SP, Criel P, Van Eeckhout H, Janssen CR, Oorts K, Smolders E. 2006. A terrestrial biotic ligand model. 2. Application to Ni and Cu toxicities to plants, invertebrates, and microbes in soil. Environ Sci Technol 40: 70947100.
  • 14
    Lock K, De Schamphelaere KAC, Becaus S, Criel P, Van Eeck-hout H, Janssen CR. 2007. Development and validation of a terrestrial biotic ligand model predicting the effect of cobalt on root growth of barley (Hordeum vulgare). Environ Pollut 147: 626633.
  • 15
    Lock K, Van Eeckhout H, De Schamphelaere KAC, Criel P, Jans-sen CR. 2007. Development of a biotic ligand model (BLM) predicting nickel toxicity to barley (Hordeum vulgare). Chemosphere 66: 13461352.
  • 16
    Lock K, De Schamphelaere KAC, Becaus S, Criel P, Van Eeck-hout H, Janssen CR. 2006. Development and validation of an acute biotic ligand model (BLM) predicting cobalt toxicity in soil to the potworm Enchytraeus albidus. Soil Biol Biochem 38: 19241932.
  • 17
    Antunes PMC, Hale BA, Ryan AC. 2007. Toxicity versus accumulation for barley plants exposed to copper in the presence of metal buffers: Progress towards development of a terrestrial biotic ligand model. Environ Toxicol Chem 26: 22822289.
  • 18
    Erickson RJ, Benoit DA, Mattson VR, Nelson HP Jr, Leonard EN. 1996. The effects of water chemistry on the toxicity of copper to fathead minnows. Environ Toxicol Chem 15: 181193.
  • 19
    Meyer JS, Santore RC, Bobbitt JP, Debrey LD, Boese CJ, Paquin PR, Allen HE, Bergman HL, Di Toro DM. 1999. Binding of nickel and copper to fish gills predicts toxicity when water hardness varies, but free-ion activity does not. Environ Sci Technol 33: 913916.
  • 20
    Heijerick DG, De Schamphelaere KAC, Janssen CR. 2002. Predicting acute zinc toxicity for Daphnia magna as a function of key water chemistry characteristics: Development and validation of a biotic ligand model. Environ Toxicol Chem 21: 13091315.
  • 21
    Lofts S, Tipping E. 2002. Windermere Humic Aqueous Model-Equilibrium Chemical Speciation for Natural Waters, Ver 6.0.8. Centre for Ecology and Hydrology, CEH Windermere, Ferry House, Far Sawrey, Ambleside, Cumbria, UK.
  • 22
    Martell AE, Smith RM, Motekaitis RJ. 1997. Critical Stability Constants of Metal Complexes Database, Ver 4.0. NIST Standard Reference Database 46. National Institute of Standards and Technology, Gaithersburg, MD.
  • 23
    Lock K, Criel P, De Schamphelaere KAC, Van Eeckhout H, Jans-sen CR. 2007. Influence of calcium, magnesium, sodium, potassium and pH on copper toxicity to barley (Hordeum vulgare). Ecotoxicol Environ Saf 68: 299304.
  • 24
    Parker DR, Pedler JF, Thomason DN, Li H. 1998. Alleviation of copper rhizotoxicity by calcium and magnesium at defined free metal ion activities. Soil Sci Soc Am J 62: 965972.
  • 25
    Taylor GJ, Stadt KJ, Dale MRT. 1991. Modeling the phytotoxicity of aluminum, cadmium, copper, manganese, nickel, and zinc using the Weibull frequency distribution. Can J Bot 69: 359367.
  • 26
    Fortunati P, Lombi E, Hamon RE, Nolan AL, McLaughlin MJ. 2005. Effect of toxic cations on copper rhizotoxicity in wheat seedlings. Environ Toxicol Chem 24: 372378.
  • 27
    Peterson HG, Healey FP, Wagemann R. 1984. Metal toxicity to algae: A highly pH-dependent phenomenon. Can J Fish Aquat Sci 41: 974978.
  • 28
    Morel FMM, Hering JG. 1993. Principles and Applications of Aquatic Chemistry. Wiley InterScience, New York, NY, USA.
  • 29
    Cheng T, Allen HE. 2001. Predicted of uptake of copper from solution by lettuce (Lactuca sativa romance). Environ Toxicol Chem 20: 25442551.
  • 30
    O'Sullivan TN, Smith JD, Thomas JD, Drake CF. 1989. Copper molluskicides for control of schistosomiasis. I. Effect of inorganic complexes on toxicity. Environ Sci Technol 23: 11021106.
  • 31
    Meador JP. 1991. The interaction of pH, dissolved organic carbon and total copper in the determination of ionic copper and toxicity. Aquat Toxicol 19: 1332.
  • 32
    Markich SJ, Brown PL, Jeffree RA, Lim RP. 2003. The effects of pH and dissolved organic carbon on the toxicity of cadmium and copper to a freshwater bivalve: Further support for the extended free ion activity model. Arch Environ Contam Toxicol 45: 479491.
  • 33
    Niyogi S, Wood CM. 2004. Biotic ligand model, a flexible tool for developing site-specific water quality guidelines for metals. Environ Sci Technol 38: 61776192.
  • 34
    James RO, Healy TW. 1972. Adsorption of hydrolyzable metal ions at the oxide–water interface. J Colloid Interface Sci 30: 6581.
  • 35
    McBride MB, Blasiak JJ. 1979. Zinc and copper solubility as a function of pH in an acid soil. Soil Sci Soc Am J 43: 866870.