Metal and proton toxicity to lake zooplankton: a chemical speciation based modelling approach

A modelling framework to assess multiple metals impacts on marine food webs: Relevance for assessing the ecological implications of deep-sea mining based on a systematic review

Industrial deep-sea mining will release plumes containing metals that may disperse over long distances; however, there is no general understanding of metal effects on marine ecosystems. Thus, we conducted a systematic review in search of models of metal effects on aquatic biota with the future perspective to support Environmental Risk Assessment (ERA) of deep-sea mining. According to results, the use of models to study metal effects is strongly biased towards freshwater species (83% freshwater versus 14% marine); Cu, Hg, Al, Ni, Pb, Cd and Zn are the best-studied metals, and most studies target few species rather than entire food webs. We argue that these limitations restrain ERA on marine ecosystems. To overcome this gap of knowledge, we suggest future research directions and propose a modelling framework to predict the effects of metals on marine food webs, which in our view is relevant for ERA of deep-sea mining.

Responses of marine zooplankton indicators after five years of a dam rupture in the Doce River, Southeastern Brazil

Since November of 2015, when ore tailings from a dam rupture reached the Atlantic Ocean, researchers are trying to assess the degree of impact across the Doce River and adjacent coastal area. This study aims to use the zooplankton dynamics as a tool to evaluate the environmental impact in the coastal region, five years after the rupture, during periods of low and high river flow. Doce River flow varied from 49 to 5179 m³/s and structured the zooplankton community between periods of low and high river flow, but salinity and chlorophyll-a had stronger correlation with depth (r = 0.40 and - 0.40 respectively) than with the Doce River discharge variation along the sampling period (r < 0.2). On the other hand, inorganic particles in the water and total metal concentration (dissolved + particulate), used as tracers of the iron enriched tailing (Al, Cd, Cr, Cu, Fe, V), were correlated with fluvial discharge and showed to be the main factor driving the zooplankton community dynamics. For assessing the degree of environmental impact, we tested the ecological indexes for the zooplankton community. Margalef Richness, Pielou Evenness and Shannon-Wiener Diversity varied from 2.52, 0.40 and 1.39 (all registered during high river flow period) to 9.02, 0.85 and 3.44 (all registered during low river flow period), respectively. Along with those community indicators, we evaluated the response of representative taxonomical genera such as Paracalanus, Oikopleura and Temora, regarding the Doce River flow, and found population patterns that established a baseline for future monitoring in the region. Our results showed that the zooplankton community is more fragile when the river discharge is stronger, and this pattern is confirmed by all indicators tested.

The use of WHAM-FTOX, parameterized with laboratory data, to simulate zooplankton species richness in acid- and metal- contaminated lakes

The WHAM-F_TOX model quantifies cation toxicity towards freshwater organisms, assuming an additive toxic response to the amounts of protons and metals accumulated by an organism. We combined a parameterization of the model, using data from multi-species laboratory toxicity tests, with a fitted field species sensitivity distribution, to simulate the species richness (n_sp) of crustacean zooplankton in acid- and metal-contaminated lakes near Sudbury, Ontario over several decades, and also in reference (uncontaminated) lakes. A good description of variation in toxic response among the zooplankton species was achieved with a log-normal distribution of a new parameter, β, which characterizes an organism's intrinsic sensitivity towards toxic cations; the greater is β, the more sensitive is the species. The use of β assumes that while species vary in their sensitivity, the relative toxicities of different metals are the same for each species (common relative sensitivity). Unbiased agreements between simulated and observed n_sp were obtained with a high correlation (r² = 0.81, p < 0.0001, n = 217). Variations in zooplankton species richness in the Sudbury lakes are calculated to be dominated by toxic responses to H, Al, Cu and Ni, with a small contribution from Zn, and negligible effects of Cd, Hg and Pb. According to the model, some of the Sudbury lakes were affected predominantly by acidification (H and Al), while others were most influenced by toxic heavy metals (Ni, Cu, Zn); for lakes in the latter category, the relative importance of heavy metals, compared to H and Al, has increased over time. The results suggest that, if common relative sensitivity operates, n_sp can be modelled on the basis of a single set of parameters characterizing the average toxic effects of different cations, together with a species sensitivity distribution.

Bioavailability and phytotoxicity of rare earth metals to Triticum aestivum under various exposure scenarios

It is a daunting challenge to predict toxicity and accumulation of rare earth metals (REMs) in different exposure scenarios (e.g., varying water chemistry and metal combinations). Herein, we investigated the toxicity and uptake of La and Ce in the presence of various levels of Ca, Mg, Na, K, and at different pH values, as well as the combined effects of La and Ce in wheat Triticum aestivum. Major cations (Ca²⁺ and Mg²⁺) significantly mitigated the toxicity and accumulation of La³⁺/Ce³⁺. Toxicity and uptake of La, Ce, and La-Ce mixtures could be well quantified by the multi-metal biotic ligand model (BLM) and by the Langmuir-type uptake model with the consideration of the competitive effects of Ca²⁺ and Mg²⁺, with more than 85.1% of variations explained. The derived binding constants of Ca, Mg, La, and Ce to wheat root were respectively 3.87, 3.59, 6.97, and 6.48 on the basis of toxicity data, and 3.23, 2.84, 6.07, and 5.27 on the basis of uptake data. The use of the alternative WHAM-F_tox approach, requiring fewer model parameters than the BLM but with similar Akaike information criterion (AIC) values, successfully predicted the toxicity and accumulation of La/Ce as well as toxicity of La-Ce mixtures, with at least 76.4% of variations explained. However, caution should be taken when using this approach to explain the uptake of La-Ce mixtures. Our results provided promising tools for delineating REMs toxicity/uptake in the presence of other toxicity-modifying factors or in mixture scenarios.

Higher than … or lower than ….? Evidence for the validity of the extrapolation of laboratory toxicity test results to predict the effects of chemicals and ionising radiation in the field

Single species laboratory tests and associated species sensitivity distributions (SSDs) that utilise the resulting data can make a key contribution to efforts to prospective hazard assessments for pesticides, biocides, metals and ionising radiation for research and regulatory risk assessment. An assumption that underlies the single species based toxicity testing approach when combined in SSD models is that the assessments of sensitivities to chemical and ionising radiation measured across a range of species in the laboratory can inform on the likely effects on communities present in the field. Potential issues with the validity of this assumption were already recognised by Van Straalen and Denneman (1989) in their landmark paper on the SSD methodology. In this work, they identified eight major factors that could potentially compromise the extrapolation of laboratory toxicity data to the field. Factors covered a range of issues related to differences in chemistry (e.g. bioavailability, mixtures); environmental conditions (optimal, variable), ecological (compensatory, time-scale) and population genetic structure (adaptation, meta-population dynamics). This paper outlines the evidence pertaining to the influence of these different factors on toxicity in the laboratory as compared to the field focussing especially on terrestrial ecosystems. Through radiological and ecotoxicological research, evidence of the influence of each factor on the translation of observed toxicity from the laboratory to field is available in all cases. The importance of some factors, such as differences in chemical bioavailability between laboratory tests and the field and the ubiquity of exposure to mixtures is clearly established and has some relevance to radiological protection. However, other factors such as the differences in test conditions (optimal vs sub-optimal) and the development of tolerance may be relevant on a case by case basis. When SSDs generated from laboratory tests have been used to predict chemical and ionising radiation effects in the field, results have indicated that they may often seem to under-predict impacts, although this may also be due to other factors such as the effects of other non-chemical stressors also affecting communities at polluted sites. A better understanding of the main factors affecting this extrapolation can help to reduce uncertainty during risk assessment.

Systematic analysis of freshwater metal toxicity with WHAM-FTOX

We used the WHAM chemical speciation model and the WHAM-F_TOX toxicity model to analyse the published results of laboratory toxicity experiments covering 52 different freshwater biological test species and 24 different metals, a total of 2037 determinations of EC₅₀ with accompanying data on solution composition. The key extracted parameter was α_M, the parameter in WHAM-F_TOX that characterises the toxic potency of a metal on the basis of its estimated metabolically active body burden. For 16 data sets applying to metal-test species pairs with appreciable variations in solution composition, values of EC₅₀ back-calculated from averaged values of α_M showed significantly (p < 0.001) less deviation from the measured EC₅₀ values than did the simple average EC₅₀, confirming that the modelling calculations could account for some of the dependence of toxicity on chemical speciation. Data for different exposure times permitted a simple parameterisation of temporal effects, enabling values of α_M,max (values at infinite exposure time) to be obtained, and the effects of different exposure times to be factored out for further analysis. Comparison of averaged values of α_M,max for different metals showed little difference among major taxa (invertebrates, plants, and vertebrates). For Cd, Cu, Ni and Zn (the four metals with most data) there were significant differences among α_M,max values for different species, but within-species variabilities were greater. Reasonably similar species sensitivity distributions of standardised α_M,max applied to Cd, Cu, Ni and Zn. The average values, over all species, of α_M,max increased in the order Al < lanthanides < Zn ∼ UO₂ < Ni ∼ Cu < Pb < Cd < Ag. Considering all the α_M,max values, there was a strong dependence (r² = 0.56, p < 0.001) on Pearson's hardness-softness categories, and a slightly stronger relationship (r² = 0.59) if ionic radius was included in the statistical model, indicating that softer, larger cations are the most effective toxicants.

Phytotoxicity of individual and binary mixtures of rare earth elements (Y, La, and Ce) in relation to bioavailability

Rare earth elements (REEs) are typically present as mixtures in the environment, but a quantitative understanding of mixture toxicity and interactions of REEs is still lacking. Here, we examined the toxicity to wheat (Triticum aestivum L.) of Y, La, and Ce when applied individually and in combination. Both concentration addition (CA) and independent action (IA) reference models were used for mixture toxicity analysis because the toxicity mechanisms of REEs remain obscure. Upon single exposure, the EC50s of Y, La, and Ce, expressed as dissolved concentrations, were 1.73 ± 0.24 μM, 2.59 ± 0.23 μM, and 1.50 ± 0.22 μM, respectively. The toxicity measured with relative root elongation followed La < Y ≈ Ce, irrespective of the dose descriptors. The use of CA and IA provided similar estimates of REE mixture interactions and toxicity. When expressed as dissolved metal concentrations, nearly additive effects were observed in Y-La and La-Ce mixtures, while antagonistic interactions were seen in Y-Ce mixtures. When expressed as free metal activities, antagonistic interactions were found for all three binary mixtures. This can be explained by a competitive effect of REEs ions for binding to the active sites of plant roots. The application of a more elaborate MIXTOX model in conjunction with the free ion activities, which incorporates the non-additive interactions and bioavailability-modifying factors, well predicted the mixture toxicity (with >92% of toxicity variations explained). Our results highlighted the importance of considering mixture interactions and subsequent bioavailability in assessing the joint toxicity of REEs.

Evidence-based logic chains demonstrate multiple impacts of trace metals on ecosystem services

Trace metals can have far-reaching ecosystem impacts. In this study, we develop consistent and evidence-based logic chains to demonstrate the wider effects of trace metal contamination on a suite of ecosystem services. They demonstrate knock-on effects from an initial receptor that is sensitive to metal toxicity, along a cascade of impact, to final ecosystem services via alterations to multiple ecosystem processes. We developed logic chains to highlight two aspects of metal toxicity: for impacts of copper pollution in soil ecosystems, and for impacts of mercury in freshwaters. Each link of the chains is supported by published evidence, with an indication of the strength of the supporting science. Copper pollution to soils (134 unique chains) showed a complex network of pathways originating from direct effects on a range of invertebrate and microbial taxa and plants. In contrast, mercury pollution on freshwaters (63 unique chains) shows pathways that broadly follow the food web of this habitat, reflecting the potential for mercury bioaccumulation. Despite different pathways, there is considerable overlap in the final ecosystem services impacted by both of these metals and in both ecosystems. These included reduced human-use impacts (food, fishing), reduced human non-use impacts (amenity value) and positive or negative alterations to climate regulation (impacts on carbon sequestration). Other final ecosystem goods impacted include reduced crop production, animal production, flood regulation, drinking water quality and soil purification. Taking an ecosystem services approach demonstrates that consideration of only the direct effects of metal contamination of soils and water will considerably underestimate the total impacts of these pollutants. Construction of logic chains, evidenced by published literature, allows a robust assessment of potential impacts indicating primary, secondary and tertiary effects.

Evaluating water quality and ecotoxicology assessment techniques using data from a lead and zinc effected upland limestone catchment

Point and diffuse sources associated with historical metal ore mining are major causes of metal pollution. The understanding of metal behaviour and fate has been improved by the integration of water chemistry, metal availability and toxicity. Efforts have been devoted to the development of efficient methods of assessing and managing the risk posed by metals to aquatic life and meeting national water quality standards. This study focuses on the evaluation of current water quality and ecotoxicology techniques for the metal assessment of an upland limestone catchment located within a historical metal (lead ore) mining area in northern England. Within this catchment, metal toxicity occurs at circumneutral pH (6.2-7.5). Environmental Quality Standards (EQSs) based on a simple single concentration approach like hardness based EQS (EQS-H) are more overprotective, and from sixteen sites monitored in this study more than twelve sites (>75%) failed the EQSs for Zn and Pb. By increasing the complexity of assessment tools (e.g. bioavailability-based (EQS-B) and WHAM-F_TOX), less conservative limits were provided, decreasing the number of sites with predicted ecological risk to seven (44%). Thus, this research supports the use of bioavailability-based approaches and their applicability for future metal risk assessments.

Interactions and Toxicity of Cu-Zn mixtures to Hordeum vulgare in Different Soils Can Be Rationalized with Bioavailability-Based Prediction Models

Soil contamination with copper (Cu) is often associated with zinc (Zn), and the biological response to such mixed contamination is complex. Here, we investigated Cu and Zn mixture toxicity to Hordeum vulgare in three different soils, the premise being that the observed interactions are mainly due to effects on bioavailability. The toxic effect of Cu and Zn mixtures on seedling root elongation was more than additive (i.e., synergism) in soils with high and medium cation-exchange capacity (CEC) but less than additive (antagonism) in a low-CEC soil. This was found when we expressed the dose as the conventional total soil concentration. In contrast, antagonism was found in all soils when we expressed the dose as free-ion activities in soil solution, indicating that there is metal-ion competition for binding to the plant roots. Neither a concentration addition nor an independent action model explained mixture effects, irrespective of the dose expressions. In contrast, a multimetal BLM model and a WHAM-Ftox model successfully explained the mixture effects across all soils and showed that bioavailability factors mainly explain the interactions in soils. The WHAM-Ftox model is a promising tool for the risk assessment of mixed-metal contamination in soils.

Incorporating bioavailability into toxicity assessment of Cu-Ni, Cu-Cd, and Ni-Cd mixtures with the extended biotic ligand model and the WHAM-F(tox) approach

There are only a limited number of studies that have developed appropriate models which incorporate bioavailability to estimate mixture toxicity. Here, we explored the applicability of the extended biotic ligand model (BLM) and the WHAM-F(tox) approach for predicting and interpreting mixture toxicity, with the assumption that interactions between metal ions obey the BLM theory. Seedlings of lettuce Lactuca sativa were exposed to metal mixtures (Cu-Ni, Cu-Cd, and Ni-Cd) contained in hydroponic solutions for 4 days. Inhibition to root elongation was the endpoint used to quantify the toxic response. Assuming that metal ions compete with each other for binding at a single biotic ligand, the extended BLM succeeded in predicting toxicity of three mixtures to lettuce, with more than 82% of toxicity variation explained. There were no significant differences in the values of f(mix50) (i.e., the overall amounts of metal ions bound to the biotic ligand inducing 50% effect) for the three mixture combinations, showing the possibility of extrapolating these values to other binary metal combinations. The WHAM-F(tox) approach showed a similar level of precision in estimating mixture toxicity while requiring fewer parameters than the BLM-f(mix) model. External validation of the WHAM-F(tox) approach using literature data showed its applicability for other species and other mixtures. The WHAM-F(tox) model is suitable for delineating mixture effects where the extended BLM also applies. Therefore, in case of lower data availability, we recommend the lower parameterized WHAM-F(tox) as an effective approach to incorporate bioavailability in quantifying mixture toxicity.

Expanding metal mixture toxicity models to natural stream and lake invertebrate communities

A modeling approach that was used to predict the toxicity of dissolved single and multiple metals to trout is extended to stream benthic macroinvertebrates, freshwater zooplankton, and Daphnia magna. The approach predicts the accumulation of toxicants (H, Al, Cd, Cu, Ni, Pb, and Zn) in organisms using 3 equilibrium accumulation models that define interactions between dissolved cations and biological receptors (biotic ligands). These models differ in the structure of the receptors and include a 2-site biotic ligand model, a bidentate biotic ligand or 2-pKa model, and a humic acid model. The predicted accumulation of toxicants is weighted using toxicant-specific coefficients and incorporated into a toxicity function called Tox, which is then related to observed mortality or invertebrate community richness using a logistic equation. All accumulation models provide reasonable fits to metal concentrations in tissue samples of stream invertebrates. Despite the good fits, distinct differences in the magnitude of toxicant accumulation and biotic ligand speciation exist among the models for a given solution composition. However, predicted biological responses are similar among the models because there are interdependencies among model parameters in the accumulation-Tox models. To illustrate potential applications of the approaches, the 3 accumulation-Tox models for natural stream invertebrates are used in Monte Carlo simulations to predict the probability of adverse impacts in catchments of differing geology in central Colorado (USA); to link geology, water chemistry, and biological response; and to demonstrate how this approach can be used to screen for potential risks associated with resource development.