Introduction

With the acceleration of industrialization and the agriculture industry, large amounts of metals have been released into the soil, where they can pose risks to the environment1. For example, environmental copper (Cu) concentrations are frequently elevated because of sewage sludge and animal manure, burning of fossil fuels, industrial processes and widespread use of pesticides2. Nationwide surveys in China recently reported that 19% of agricultural soils are polluted, mainly with heavy metals and metalloids—among which 2.1% of samples exceeded China’s soil environmental quality for Cu3. The minerals cobaltite, smaltite and erythrite contain cobalt (Co), and anthropogenic activities such as mining and smelting can lead to Co contamination of soil in some areas4. Soil contamination is complicated, and organisms in the soil are often simultaneously exposed to multiple metal elements5. However, risk-evaluation is usually based on the effects of individual metals in soils6. Therefore, investigation and assessment of the environmental effects on mixtures of metals such as Cu–Co may have practical significance.

The concentrations of individual toxicants in a mixture cannot be assessed based simply on their added concentrations. One of the models most commonly used to assess the toxicity of chemical mixtures is the toxic unit (TU) approach5, which is employed to calculate the sum of the concentrations of individual chemicals divided by their median effective concentrations (EC50)7.

$${\rm{TU}}=\sum {{\rm{TU}}}_{i}=\sum _{i=1}^{n}\frac{{{\rm{M}}}_{{\rm{i}}}^{2+}}{{{\rm{EC50}}}_{i}}$$
(1)

where, i is the identity of the metal, n is the number of metals, \({{\rm{M}}}_{{\rm{i}}}^{2+}\) is the activity of free metal ions such as Cu2+ and Co2+, and EC50i is the median effective concentration of a single metal. The TU model is commonly used to normalize the toxic impacts and categorise the type of combined effect of metal mixtures. In multi-metal systems, a metal mixture is termed additive when TU is equal to 1 at 50% effect, while it is called synergistic or antagonistic when TU is less than or greater than 18. The TU approach has been successfully used to predict joint toxicity and as a tool to assess the corresponding combined effects. However, conventional studies using the TU approach to examine the combined toxicity of multiple metals do not consider the speciation and phytotoxicity of metals under different environmental conditions.

Recently, the biotic ligand model (BLM) has become popular for evaluating metal bioavailability and toxicity in aquatic and terrestrial systems9. The critical assumption of the BLM is that metal toxicity depends on free metal ions (or other reactive metal species), which can react with biological binding sites and form a metal–biotic ligand (BL) complex. This model assumes that the cations (e.g., K+, Na+, Mg2+, Ca2+) compete with metals for binding sites to mitigate the toxicity of free metal ions10. Generally, BLMs are considered to be useful tools for evaluating the toxic effects of metals on organisms. Most previous studies using the BLM to evaluate the impacts of metals have been applied to individual metals. However, it is also increasingly being applied to the assessment of the effects of mixtures of metals. Jho et al. found that data describing the toxicity of a single metal can be used in the BLM-based TU method to predict the joint toxicity of Cd–Pb mixtures to Vibrio fischeri 1. Although terrestrial higher plants also appear suitable for these types of studies, they have rarely been used in investigations of alternative methods for joint toxicity of metals. To the best of our knowledge, only three reports have employed the BLM to estimate the combined toxicity of metals on joint toxicity10,11,12.

When developing the BLM for individual metals, some studies indicated that the toxicity of metals to plants was partly dependent on inorganic complexes (e.g., carbonate and hydroxide species) when exposed to relatively high pH13. However, no published studies of combined toxicity of mixtures of metals have considered the potential effects of these metal complexes12, 14, 15. Furthermore, most BLMs for metal mixtures have considered Ca2+ competition, while ignoring Mg2+ competition1, 10. Earlier studies of BLM for single metals showed that Mg2+ has a greater impact on the toxicity of some single metals (e.g., Co and Cu) than Ca2+  16, 17. Therefore, the present study was conducted to investigate the joint toxicity of Cu–Co mixtures toward wheat (Triticum aestivum L.) in the presence of different Mg2+ concentrations and under a wide range of pH values using the BLM-based TU method1. In addition, the traditional free ion activity model (FIAM)-based TU approach18 was employed for combined toxicity prediction and the results were compared with those obtained using the BLM-based TU method.

Results

Toxicity of Cu2+ and Co2+ individually

A summary of the median effective concentration (EC50) for wheat root elongation expressed as free Cu2+ and Co2+ activity at different Mg2+ concentrations and pH level is given in Table 1. Dose–response curves for both free Cu2+ and Co2+ activities were established in Fig. 1. The increasing Mg2+ activities resulted in corresponding reductions in free Cu2+ activity by 2.5-fold and free Co2+ activity by 10-fold, respectively (Table 1). Furthermore, a linear relationship was found between Mg2+ activities and EC50 of Cu2+ or Co2+ activities (Cu2+: p < 0.01, R 2 = 0.97; Co2+: p < 0.01, R 2 = 0.97). These results suggest that Mg2+ can compete with Cu2+ and Co2+ for binding sites on wheat root and that it can alleviate the toxicity of Cu and Co. Previous studies showed that K+ and Na+ did not significantly influence the toxicity of Cu and Co to wheat and barley, and that the effects of Ca2+ on the toxicity of Cu2+ and Co2+ were only slightly larger than K+ and Na+, and smaller than the effects of Mg2+ 13, 17, 19. Therefore, the effects of K+, Na+ and Ca2+ were ignored and not incorporated into the BLM in the present study.

Table 1 Composition of the test media, and the fitted individual EC50 (expressed as free ion activity) of Cu-only, Co-only and TU50 of Cu–Co mixture in the various bioassay sets using the log-logistic relationship.
Full size table
Figure 1
figure 1

Dose–response relationships between relative net elongation (RNE, %) of wheat and free metal ion activity: free Cu2+ activity (first column) and free Co2+ activity (second column) under different Mg treatments (first row) and pH levels (second row for Cu2+, third row for Co2+). Each series point represents the observed RNE at the corresponding solution of Mg and pH treatment. The solid lines are fitted using log-logistic curves (RNE = 100/{1 + [M2+ /EC50{M2+}]β M}). EC50 values and the slopes estimated based on these log-logistic curves are reported in Table 1.

Full size image

In the pH test, dose-response curves overlapped in the low pH range, while they differed at high pH values (Fig. 1c–f). At pHs of 4.5 to 7.6, the values of EC50(Cu2+) varied by 4.5-fold and those of EC50(Co2+) varied by a factor of 4.3 (Table 1). However, there was a non-linear relationship between EC50{M2+}(M2+: Cu2+, Co2+) and H+ activities, which indicated that the effect of pH on EC50{M2+} cannot be explained by H+ competition. Early research indicated that the pH effect on metals may be related to the speciation of metals13. Thus, we determined the relationship between Cu or Co species and their toxicity using a previously described method13. We found that the contribution of CuCO3(aq) and CuOH+ to toxicity should be considered at high pH values. Similar results were found for Co. Furthermore, we determined stability constants (logK) for binding of Mg2+, Cu2+ and Co2+ of Cu-only and Co-only systems based on the BLM method for single metals (Table 2). For details, refer to De Schamphelaere20. In addition, the conditional binding constants of the inorganic metal complexes such as CuOH+ were also evaluated based on single toxicity data. The results were \(\mathrm{log}\,{K}_{{{\rm{CuHCO}}}_{3}{\rm{BL}}}\) = 5.67, \(\mathrm{log}\,{K}_{{{\rm{CuCO}}}_{3}{\rm{BL}}}\) = 5.44, logK CuOHBL = 5.07 and \(\mathrm{log}\,{K}_{{{\rm{CoHCO}}}_{3}{\rm{BL}}}\) = 5.81.

Table 2 Fitted parameters for the BLM derived from Cu-only and Co-only; and fitted parameters from the FIAM-TU and BLM-TU models derived from Cu–Co mixture.
Full size table

Combined toxicity of Cu–Co mixtures

The observed percentage of wheat root elongation (RNE%) of individual Cu and Co and Cu–Co mixtures expressed as the TUM (calculated on the basis of free Cu2+ and Co2+ activity) for each Mg2+ treatment and pH values are shown in Fig. 2. The observed toxic effects of individual Cu, Co, and Cu–Co mixture showed no obvious deviations in any sets, which indicates that the observed toxicity of Cu–Co mixtures was the same for the individual Cu and Co systems. Then, the TUM values for each Mg2+ treatment and pH values of Cu–Co mixtures were calculated based on Eqn. 1, and the TUM at 50% RNE (TU50) were fitted based on Eqn. 5. The values of TU50 for all Cu–Co sets were close to 1 (0.88–1.15; Table 1). These results show that the Cu–Co mixture followed a trend of additive effects.

Figure 2
figure 2

Response of wheat roots exposed to single Cu (•), Co (○) and their combinations (Δ) shown as the relationship between RNE and toxic unit values (TUM, calculated on the basis of free Cu2+ and Co2+ activity and Eqn. 1) under different Mg treatments (first row), at pH 4.5 to 6.0 (second row) and at pH 6.5 to 7.6 (third row). Each data point represents the RNE at the corresponding Mg and pH treatments.

Full size image

Predicted Cu–Co mixture toxicity based on the BLM

The TU50 being equal to 1 for all mixed sets revealed that there was no interaction between Cu2+ and Co2+. Therefore, TU f and TUM were proposed for use in predicting Cu–Co toxicity according to Eqn. 5 and in developing the BLM-based and FIAM-based TU models in the present study. For every treatment (12 bioassays × 8 concentrations of Cu–Co mixture) the TU f or TUM value was calculated using Eqn. 4 for varying f or M (free Cu2+ and Co2+ activity), respectively; where f can be calculated based on the activities of free Cu2+, Co2+, Mg2+ and inorganic metal complexes in the Cu–Co mixture and LogK values derived from the individual Cu and Co systems (Eqn. 3). Thus, the combined toxicity of Cu–Co mixtures could be expressed using the TU f or TUM values (Eqn. 5). The predicted dose–response curves and fitted parameters of all sets with the BLM-based and FIAM-based TU approaches are shown in Fig. 3 and Table 2. The root mean square error (RMSE) was calculated for each prediction (Fig. 3). The BLM-based TU approach performed better for predicting root elongation than the FIAM-based TU model based on RMSE and R 2 values. Specifically, the RMSE value between the observed and predicted RNE of the BLM (6.70) was much lower than that of the FIAM (16.31). This was likely due to the inclusion of the competition between Cu or Co and Mg2+ for binding sites on wheat roots and consideration of the effects of CuHCO3 +, CuCO3(aq), CuOH+ and CoHCO3 +, under various pH levels, on the estimation of the f values. Therefore, the proposed BLM-based TU was a better method to predict the toxicity of Cu–Co mixtures for wheat.

Figure 3
figure 3

Dose–response curves for toxicity of Cu–Co combinations shown as the relationship between RNE and TU calculated by TUM (A) and TU f (B). R 2 is the determination coefficient of the models between the measured and predicted RNE. RMSE is the root mean square error of the models. The scatter points represent observed mixture toxic effects and the solid line is the predicted mixture toxic effects. In A, data were fitted using: RNE (%) = 100/{1 + [(MCu/EC50Cu + MCo/EC50Co)/\({{\rm{TU}}}_{{\rm{M}}}^{{\rm{50}}}\)]β M}. In B, the data were fitted using Eqn: RNE (%) = 100/{1 + [(f Cu /\({f}_{{\rm{CuBL}}}^{50 \% }\) + f Co/\({f}_{{\rm{CoBL}}}^{50 \% }\))/\({{\rm{TU}}}_{f}^{{\rm{50}}}\)]β M}.

Full size image

Discussion

The observed EC50 for free Cu2+ or Co2+ activity increased up to 2.5-fold and 10-fold with increasing Mg2+ concentrations, respectively. These findings indicate that Mg2+ had a protective effect against the toxicity of Cu or Co, which is similar to previous studies[13,17,19]. For instance, Luo et al. reported that Mg2+ can alleviate Cu toxicity for wheat in nutrient solution, and calculated that the EC50 for free Cu2+ increased by up to 3.7-fold. Wang et al. carried out similar root elongation tests on barley using a growth solution with a series of Mg2+ (0.05–2 mM) concentrations and found an increase in EC50 of free Cu2+ activity by a factor of 3. Lock et al.19. reported that the increase of Mg2+ concentrations could alleviate Co toxicity to barley, and calculated a 15.6-fold increase in EC50 of free Co2+ activity in solution. The effect of Mg2+ on Cu2+ and Co2+ toxicity may be due to similar ionic radii of Mg2+, Co2+ and Cu2+, or due to competition for transporters21.

The present study revealed that the EC50 of Cu or Co decreased by up to 4.1-fold with increasing pH values; however, there was no obvious linear relationship between H+ and EC50. These findings differ from those for the BLM with H+, which can compete with free metal activity ions at BL. Therefore, it is unjustified to incorporate H+ competition into the BLM. The present study indicated that the effects of pH on the toxicity of metals may be due to the contribution of inorganic metal complexes to toxicity. For instance, De Schamphelaere and Janssen suggested that CuOH+ contributed to Cu toxicity in Daphnia magna 22. For terrestrial plants, Wang et al.13 indicated that when incorporating inorganic species of Cu2+ into the BLM, the regression coefficient (R 2) between the measured and predicted EC50{Cu2+} values was as high as 0.97. These findings suggested that some species of inorganic metal complexes were toxic and should be considered in BLMs with high pH values. The present study indicates that incorporating inorganic metal complexes when assessing the joint toxicity of Cu–Co improves the predictive capacity of the BLM.

The binding constants derived in the present study for wheat can be compared with those reported for Cu-BLM13 and Co-BLM19. The values of logK CuBL (5.87), \(\mathrm{log}\,{K}_{{{\rm{CuHCO}}}_{3}{\rm{BL}}}\) (5.67), \(\mathrm{log}\,{K}_{{{\rm{CuCO}}}_{3}{\rm{BL}}}\) (5.44), logK CuOHBL (5.07) and logK MgBL (2.93) in the present study were closer to the results reported by Wang et al.13. In addition, the values of logK CoBL (4.72) were slightly lower than those of (logK CoBL = 5.13) reported by Lock et al.19, whereas they were very similar to those (logK CoBL = 4.70) published by Garnham et al.23. The logK MgBL (3.84) of Co was similar to that reported by Lock et al. (logK MgBL = 3.95)19. Different exposure duration, endpoint, target tissue or BL, or mechanisms of Cu or Co uptake resulted in differences in binding constants16.

The present study showed that CoHCO3 + had 11-fold higher binding affinity than Co2+, similar to the results of Deleebeeck et al. and Wang et al., who reported that the affinities of inorganic complexes of metals for the BL were higher relative to the free metal ion[13,24,25]. However, it is mechanistically very unlikely that BL constants for complexes of metals are very close to or higher than for the free metal ion. Therefore, the binding of inorganic metal complexes with BL needs further investigation and direct evidence.

It is widely believed that most metal combinations act additively. For example, Ownby and Newman et al. reported that inhibition of bioluminescence in Microtox assays was approximately additive in Cd–Cu mixtures26. Marra et al. found that interactions of Cu–Co mixtures were additive for rainbow trout and duckweed27. The present study indicated that the joint toxicity of Cu and Co was additive for wheat. These results are similar to those reported by Ownby and Newman et al. and Marra et al.26, 27. However, some researchers pointed out that the joint toxicity of mixtures of metals may exhibit synergistic or antagonistic effects, rather than simply being additive. Versieren et al.10 showed significant (p < 0.05) antagonistic interactions between Cu–Zn and lettuce at low Ca2+ concentrations. Ince et al. described Cu–Co interactions that were synergistic at most test levels for Vibrio fisheri but that had additive effects under some individual conditions28. Thus, the difference in interactions between metal ions may be due to exposure time and test species29. The present study indicated that the BLM-based TU approach, which accounted for metal speciation and the integrated competition effects of the major cations, was more accurate at predicting the combined toxicity of Cu–Co mixtures than the FIAM-based TU model. Future studies should investigate the biological actions of metals in plant cell compartments following exposure to mixtures of metals to provide better insight into the mechanisms.

Conclusion

In summary, a BLM-based TU was developed to predict the combined toxicity of Cu–Co mixtures at different Mg2+ activities and at various pH levels for wheat in nutrient solutions. Using the estimated constants based on the individual Cu and Co toxicity data, the BLM-based TU approach more accurately predicted the joint Cu–Co toxicity than the FIAM-based TU approach. Further research is required to investigate the toxicity of metal mixtures in a wide range of natural or field soils before BLMs can be used for risk assessments of metal co-contaminated soil in the field.

Materials and Methods

Experimental design

Wheat root elongation tests were used to evaluate the toxicity of individual Cu, Co and Cu–Co mixtures (molar ratio of Cu:Co = 1:100) in a simplified culture solution. Two sets of experiments were conducted: a Mg-set and a pH-set (Table 1). Each set included a series of media with different Mg2+ concentrations and varied pH values. The concentrations of background electrolytes were selected based on previously reported data13 (Table 1). For single-metal system tests, seven Cu2+ (0.2–12.8 μM), seven Co2+ (20–1280 μM), seven Cu2+–Co2+ concentrations and one control solution (without Cu2+ or Co2+) were prepared. All treatments were performed in triplicate.

Preparation of test media

All experiments used chemicals of analytical reagent grade or higher, and deionized water was used throughout. Tested solutions were prepared by adding different volumes of stock solutions of CaCl2, MgSO4, NaCl and KCl into deionized water. Additionally, the buffer solutions used were 1 mM 2-[N-morpholino] ethane sulfonic acid for pH < 7.0 treatments and 3.6 mM 3-[N-morpholino] propane sulfonic acid for pH ≥ 7.030. Moreover, dilute NaOH or HCl were used to adjust pH to the desired level for each pH-set, and pH was controlled at 6.0 for the Mg-set. The pH values of solutions were measured before and after the bioassay. The different chemical characteristics of the test media are shown in Table 1.

Toxicity tests

Wheat root elongation tests were performed following ISO Guideline 11269-1(1993). The test seeds of wheat (T. aestivum L. cv. Zongmai 335) were purchased from the Chinese Academy of Agricultural Sciences (Beijing, China). Wheat seeds were sterilized for 30 min in 30% H2O2, then thoroughly rinsed with deionized water and germinated at 25 ± 1 °C in darkness for 48 h on moistened filter-paper22. After the radicle emerged (about 1 cm in length), six seeds were transferred to a nylon net fixed on the surface of the plastic culture pots containing 350 mL of the solution. The air temperature of the growth chamber was maintained at 20 ± 1 °C for 48 h in darkness and the culture pots were randomly placed in the growth chamber. The lengths of the longest roots on each seedling were measured after 2 d, and the mean value of the three replications for each test was used for data analysis. The relative net elongation (RNE) was calculated and expressed as the percentage relative to the control (Eqn 2):

$$RNE \% =\frac{(R{E}_{t}-R{E}_{0})}{(R{E}_{c}-R{E}_{0})}$$
(2)

where RE t is the root length in the metal treatment, RE 0 is the original length and RE c is the root length in the control.

Chemical analyses

The concentrations of Cu, Co and Mg in test solutions were measured by inductively coupled plasma optical emission spectrometry (720-ES, Varian, Palo Alto, CA, USA). The solution pH was determined using a pH meter (Delta 320; Mettler, Zurich, Switzerland).

Prediction of Cu and Co speciation in solutions

WHAM 6.0 (Windermere Humic Aqueous Model) was used to calculate the speciation of Cu and Co31. The pH and measured total concentrations of Cu2+, Co2+, K+, Na+, Ca2+, Mg2+, Cl− and SO4 2− were inputted into WHAM. For details, refer to Lofts et al.31. The calculated proportions of free Cu2+ and Co2+ activity (% of total Cu or Co) accounted for 11.7–78.6% and 61.5–78.6% with pH increasing from 4.5 to 7.6, respectively. The calculated proportions of CuHCO3 +, CuCO3, CuOH+ and CoHCO3 + were 0.25–46.6, 0.00–25.0, 0.05–9.54 and 0.01–9.74%, respectively.

The Data Processing System 9.0 (DPS9.0), developed by Tang and Feng32, was used to estimate the parameter values of the BLM-based and FIAM-based TU models.

Mathematical description of the BLM and derivation of parameters

The BLM methodology is based on the assumption that stability constants remain the same under various physico-chemical conditions20. The following is a short mathematical description of the BLM along with the equations required to understand the calculations. Based on the BLM assumption, the fraction (f) of the total number of BL sites occupied by Cu2+ or Co2+ is given by the following equation when the competing cations and toxicity of inorganic metal complexes are considered:

$$f=\frac{{K}_{{\rm{MBL}}}\{{{\rm{M}}}^{2+}\}+\sum {K}_{{\rm{IMBL}}}\{\mathrm{IM}\}}{{K}_{{\rm{MBL}}}\{{{\rm{M}}}^{2+}\}+\sum {K}_{{\rm{IMBL}}}\{\mathrm{IM}\}+\sum {K}_{{\rm{XBL}}}\{{X}^{n+}\}}$$
(3)

where, K MBL, K IMBL and K XBL are constants for the binding of free Cu2+ or Co2+ (M), inorganic Cu2+ or Co2+ complexes (IM) and cation X (e.g., Mg 2+) to the BL sites, respectively, and brackets {} indicate ion activity, such as {X n+}, which is the activity of Xn+ (M). For mixtures of metals, substituting f from Eqn. 3 into Eqn. 1, transforms Eqn. 1:

$${{\rm{TU}}}_{f}=\sum {\rm{TU}}=\frac{{f}_{{\rm{Cu}}}}{{f}_{{\rm{CuBL}}}^{50 \% }}+\frac{{f}_{{\rm{Co}}}}{{f}_{{\rm{CoBL}}}^{50 \% }}$$
(4)

where \({f}_{{\rm{CuBL}}}^{50 \% }\) and \({f}_{{\rm{CoBL}}}^{50 \% }\) are the fraction of BL sites occupied by Cu or Co, respectively, at 50% RNE under single metal exposure. The TU f values can be used to establish the BLM-based TU approach. When f is replaced with free ion activity of M (Cu2+ or Co2+), the TUM values can be calculated and used to establish the FIAM-based TU model.

The wheat root elongation was correlated with TU f or TUM and was assumed to follow a log-logistic dose–response relationship according to Thakali et al.33

$$\mathrm{RNE},\,R( \% )=\frac{100}{1+{(\frac{x}{{x}_{50}})}^{{\beta }_{{\rm{M}}}}}$$
(5)

where β M is a fitting parameter determining the slope of the dose–response curve; x is the value of the toxicity index, i.e. TU f or TUM; and x 50 is the value of TU at 50% RNE. Eqn. 5 was applied to predict the mixture toxicity effects and compare the BLM-TU and FLAM-TU approaches.