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Toxicity of trace metals to plants at mixed contamination in soil: experimental analysis, modelling and implementation in risk-assessment

Book - Dissertation

The widespread contamination of soils with trace metals such as zinc (Zn), cadmium (Cd), nickel (Ni) or copper (Cu) can have an adverse ecological effect. The evaluation of the toxic effects of metals in soils has almost exclusively been estimated from toxicity studies with single metal dosing. As a rule, metals occur as mixtures in a contaminated environment, e.g. Cd is usually enriched where higher Zn concentrations are found. The premise in the current assessment is that risks can be excluded provided that all metal concentrations are below their corresponding limits. This assumption is questioned and has been the starting point of this work. A mixture of different metals can have a larger effect than each metal individually, e.g. because the effects of each metal add up or because metals can even interact and act synergistically. There is a need to understand how metals act together in producing combination effects, especially at low effect concentrations and at a large number of metals in the mixture. It is predictable that metals do interact since they compete for sorption, uptake in biota, translocation and detoxification. The general objective of this thesis was to analyze low level metal mixture effects in soil grown plants. The scientific questions were (i) to identify which of two reference mixture models to predict the toxic effect of a mixture applies. These models are the concentration addition (CA) or independent action (IA) models, respectively assuming that there is an equal or an independent mode of action of the different metals; (ii) to identify if deviations from additivity (‘interactions’) can be explained based on soil metal bioavailability. The environmental question was to identify if metal mixture effects occur and if they are relevant. More specifically, a relevant mixture effect occurs when metals dosed in isolation are not affecting plant growth, but become toxic when dosed as a mixture at equal concentrations. First, a study was set up to investigate the toxicity of multiple metal mixtures of Cu, Ni, Cd and Zn to plants at metal doses individually causing low level effects. Barley (Hordeum vulgare L.) root elongation toxicity tests were performed in resin buffered nutrient solutions to control metal speciation. Mixtures of different metals at free ion concentrations each causing <10% inhibition, yielded significant mixture effects when dosed in combination at equal concentrations, with inhibition reaching up to 50%. The IA model predicted mixture toxicity statistically better than the CA model, but some synergisms relative to the independent action model were observed. These synergisms relative to IA were most pronounced in quaternary mixtures and when the dose response curves had steep slopes. Generally, antagonistic interactions relative to the CA model were observed. Increasing solution Zn concentrations shifted metal interactions (CA based) from additive or slightly synergistic at background to antagonistic at higher supply, suggesting a protective effect of Zn. Overall, this study showed that the CA model can be used as a conservative model to predict metal mixture toxicity to barley. Second, interactions at biochemical level in plant roots were investigated. Net toxic effects and interactions of mixtures on plant growth may be better explained by biochemical parameters than by exposure information, and therefore effects of mixtures of Zn, Cd and Cu on barley plants were analyzed using antioxidant and oxidative stress parameters and root K+-efflux. Root elongation in Cu+Cd mixtures was well predicted from solution concentrations, using CA or IA reference models. In contrast, Zn acted antagonistically when combined with Cu and/or Cd, relative to both CA and IA. This protective effect of Zn correlated with the biomarkers, i.e. oxidative stress (indicated by e.g. MDA and H2O2 levels) decreased upon addition of Zn. However, external solution metal concentrations, i.e. the exposure, explained mixture effects better than any of the 16 antioxidant and oxidative stress biomarkers, i.e. the biochemical effects. It was concluded that the biomarkers are no robust indicators for metal mixture toxicity, potentially because different metals have different parallel modes of action on growth that are insufficiently indexed by the biomarkers. Next, interactions at the exposure and uptake level were investigated by incorporating bioavailability in the interpretation of metal interactions. The Biotic Ligand Model (BLM) and the WHAM-Ftox model that assume that toxicity depends on the concentration of metal bound to a biological binding site (the biotic ligand), were used. That concentration bound to the biotic ligand, is in turn calculated from the speciation of the metals in (soil) solution and the concentrations of ions competing with metal binding. First, Cu2+ and Zn2+ mixture toxicity was tested in resin buffered solutions at three different Ca2+ concentrations. Antagonistic interactions between Cu2+ and Zn2+ were found at low Ca2+ concentrations, but became smaller or insignificant at higher Ca2+, illustrating that mutual competition is eliminated at high concentration of a third competing ion (Ca2+). These effects obeyed the BLM combined with the IA reference mixture toxicity model. In a second test in nutrient solution, the complexity was increased by adding the metal chelator NTA (nitrilotriacetic acid) in solution. Metals compete for binding to NTA and this model molecule represents general complexation of metals in the environment. Mixture toxicity of Cu and Zn was investigated at contrasting NTA supply (-NTA and +NTA). In the +NTA solutions, Cu and Zn acted synergistically (IA based) when evaluation of toxicity was based on total metal concentration in solution. This interaction shifted to antagonism when the toxicity evaluation was based on the solution free ion activities of the metal, thus by accounting for competition effects on the NTA ligand and, hence, by accounting for bioavailability. In a final test, mixture toxicity and interactions of Cu and Zn were investigated in three different soil samples. The toxic effects of Cu and Zn mixtures on barley root elongation were synergistic in soils with high and medium cation exchange capacity (CEC), but antagonistic in a low CEC soil. This was found when expressing the dose as the conventional total soil concentration. In contrast, antagonism was found in all soils when expressing the dose as free ion activities in soil solution, indicating, again, that there is metal ion competition for binding to the plant roots. Neither a CA, nor an IA model fully explained mixture effects, irrespective of the dose expressions. In contrast, a multi-metal BLM model and a WHAM-Ftox model successfully explained the mixture effects from pore water composition across all soils and showed that bioavailability factors mainly explain the interactions in soils. Concluding, metal mixture effects can be predicted from the effects of each metal separately and the CA reference model is more conservative (protective) than the IA model, however the latter was statistically more accurate. This suggests that different metals, in general, act independently for toxicity. Metals can act synergistically in solution-plant or soil-plant systems when considering the total metal concentration, however, that almost always reverted to antagonism when considering the bioavailability, i.e. metal speciation in the exposure medium and competition for uptake. Biomarkers were no robust indicators for physiological effects and offer little opportunities to address mixture effects in the environment. A multi-metal BLM model and a WHAM-Ftox model successfully explained mixture effects in different soil samples. From risk-assessment point of view, this work showed that mixture effects are relevant and that, for metals, something can happen from nothing, especially when individual dose-response curves have steep slopes. Validated chronic mixture toxicity models accounting for bioavailability can be included in tiered risk assessment approaches.
Publication year:2016
Accessibility:Closed