Quantitative Ion Character-activity Relationships Research Articles

Using the quantitative ion character-activity relationships (QICAR) model to predict the solid-liquid release of metals in soil

Dissolved metals in the soil’s liquid phase are more easily absorbed by biological tissues, thus posing potential risks to living organisms. Therefore, studying the solid-liquid partition coefficient (Kd) of metals in the soil helps understand their environmental risks. We collected 102 Kd data of metals in soil from the literature to build a quantitative ion character-activity relationship model (s-QICAR) based on the metals’ physicochemical properties. Correlation analysis showed ten elemental physicochemical properties related to Kd, including covalent bond index, atomic mass, and first hydrolysis constant, with R2 = 0.502–0.989. Through comparative screening, 39s-QICAR models were finally established, including covalent bond index and first hydrolysis constant. The established s-QICAR model predicted the Kd values of Mo, Sb, and La in the 39 soil samples, ranging from 21 to 19978. Finally, a coupling analysis was conducted between Kd-metal and soil physicochemical properties. The s-QICAR model with covalent bond index as the independent variable was mainly affected by soil pH and cation exchange capacity, while the s-QICAR model with first hydrolysis constant was influenced primarily by clay content. Therefore, this study established a predictive model for soil Kd, providing a basis for soil environmental risk assessment and management. Environmental ImplicationDissolved metals present in the soil liquid phase are more easily absorbed by biological tissues, thus posing potential risks to living organisms. Therefore, studying the solid-liquid partition coefficient (Kd) of metals in the soil helps understand their environmental risks. Common Kd values are obtained by measuring the content of soil solid and liquid phase metals. However, this method is time-material consuming. We study the relationship between element properties and their Kd, then establish a model that does not rely on measured data to predict Kd of soil elements, providing a basis for soil environmental risk assessment and management.

Quantitative ion character-activity relationship methods for assessing the ecotoxicity of soil metal(loid)s to lettuce.

New pollution elements introduced by the rapid development of modern industry and agriculture may pose a serious threat to the soil ecosystem. To explore the ecotoxicity and risk of these elements, we systematically studied the acute toxicity of 18 metal(loid)s toward lettuce using hydroponic experiments and quantitative relationships between element toxicity and ionic characteristics using ion-grouping and ligand-binding theory methods, thereby establishing a quantitative ion character-activity relationship (QICAR) model for predicting the phytotoxicity threshold of data-poor elements. The toxicity of 18 ions to lettuce differed by more than four orders of magnitude (0.05-804.44μM). Correlation and linear regression analysis showed that the ionic characteristics significantly associated with this toxicity explained only 23.8-50.3% of the toxicity variation (R2Adj = 0.238-0.503, p < 0.05). Relationships between toxicity and ionic properties significantly improved after separating metal(loid) ions into soft and hard, with R2Adj of 0.793 and 0.784 (p < 0.05), respectively. Three ligand-binding parameters showed different predictive effects on lettuce metal(loid) toxicity. Compared with the binding constant of the biotic ligand model (log K) and the hard ligand scale (HLScale) (p > 0.05), the softness consensus scale (σCon) was significantly correlated with toxicity and provided the best prediction (R2Adj = 0.844, p < 0.001). We selected QICAR equations based on soft-hard ion classification and σCon methods to predict phytotoxicity of metal(loid)s, which can be used to derive ecotoxicity for data-poor metal(loid)s, providing preliminary assessment of their ecological risks.

Predicting the thresholds of metals with limited toxicity data with invertebrates in standard soils using quantitative ion character-activity relationships (QICAR)

Terrestrial invertebrates are often used as indicator organisms in ecological risk assessments. However, determining the risk of metals to invertebrates is laborious and time-consuming due to the lengthy testing and ethical approval procedures. In this study, a review of the literature was conducted to provide toxicity data for two standard soils (OECD and LUFA 2.2). An attempt was made to establish models for predicting the toxicity of elements to invertebrates using quantitative ion character-activity relationships (QICARs). In OECD soil, the element toxicity of four groups (Enchytraeus albidus mortality and reproduction, Folsomia candida and Eisenia fetida reproduction) showed significant correlations with atomic number, atomic mass and atomic ionization potential (0.852 ≤ R2 ≤ 0.989, P < 0.05). For LUFA 2.2 soil, polarization force parameters and boiling point were most significant parameters for toxicity values of F. candida and Enchytraeus crypticus, respectively (0.866 ≤ R2 ≤ 0.962, P < 0.05). Finally, QICAR models were established, and LC50 or EC50 of elements were predicted. Then, models were verified using standard and natural soils, and showed that errors between observed and predicted logLC50/EC50 were mostly < 0.5 orders of magnitude. Thus, the developed QICAR models have potential for predicting the toxicity of elements for soils.

Using quantitative ion character–activity relationship (QICAR) method in evaluation of metal toxicity toward wheat

It is important to assess the toxic effects posed by soil pollutants toward plants. However, plant toxicology experiments normally involve a considerable amount of manpower, consumables and time. Therefore, the use of metal toxicity prediction models, independent of toxicity tests, is critical. In this study, we investigated the toxicity of different metal ions to wheat using hydroponic experiments. We employed the methods of soft–hard ion grouping, soft–hard ligand theory and K (conditional binding constant based on the biotic ligand model principle) in combination with hydroponic experiments to explore the application of quantitative ion character-activity relationships in predicting phytotoxicity. The results showed that the toxicity of the 19 metal ions tested varied significantly, with EC50 ranging from 0.27 μM to 4463.36 μM. The linear regression relationships between the toxicity of these metal ions and their physicochemical properties were poor (R2 = 0.237–0.331, p < 0.05). These relationships were improved after grouping the metals according to the soft–hard theory (R2 = 0.527–0.744 and p < 0.05 for soft ions; R2 = 0.445–0.743 and p < 0.05 for hard ions). The application of soft–hard ligand theory, based on the binding affinity of the metals to the ligands, showed poor prediction of the phytotoxicity of metals, with R2 = 0.413 (p = 0.024) for the softness consensus scale (σCon) and R2 = 0.348 (p = 0.218) for the normalized hard ligands scale (HLScale). However, the method of K provided the closest fit in predicting toxicity (R2 = 0.803, p < 0.001). Our results showed that the application of soft–hard ion grouping and log K can improve prediction of the phytotoxicity of metals relatively well, which can potentially be used for deriving the toxicity of elements with limited toxicity data.

Development of Quantitative Ion Character-Activity Relationship Models to Address the Lack of Toxicological Data for Technology-Critical Elements.

Recent industrial developments have resulted in an increase in the use of so-called technology-critical elements (TCEs), for which the potential impacts on aquatic biota remain to be evaluated. In the present study, quantitative ion character-activity relationships (QICARs) have been developed to relate intrinsic metal properties to their toxicity toward freshwater aquatic organisms. In total, 23 metal properties were tested as predictors of acute median effect concentration (EC50) values for 12 data-rich metals, for algae, daphnids, and fish (with and without species distinction). Simple and multiple linear regressions were developed using the toxicological data expressed as a function of the total dissolved metal concentrations. The best regressions were then tested by comparing the predicted EC50 values for the TCEs (germanium, indium, gold, and rhenium) and platinum group elements (iridium, platinum, palladium, rhodium, and ruthenium) with the few measured values that are available. The 8 "best" QICAR models (adjusted r2 > 0.6) used the covalent index as the predictor. For a given metal ion, this composite parameter is a measure of the importance of covalent interactions relative to ionic interactions. Toxicity was reasonably well predicted for most of the TCEs, with values falling within the 95% prediction intervals for the regressions of the measured versus predicted EC50 values. Exceptions included Au(I) (all test organisms), Au(III) (algae and fish), Pt(II) (algae, daphnids), Ru(III) (daphnids), and Rh(III) (daphnids, fish). We conclude that QICARs show potential as a screening tool to review toxicity data and flag "outliers," which might need further scrutiny, and as an interpolating or extrapolating tool to predict TCE toxicity. Environ Toxicol Chem 2021;40:1139-1148. © 2020 SETAC.

Nano-quantitative ion character–activity relationships of adsorption of rare earth ions on multi-walled carbon nanotubes

ABSTRACTIn this study, the maximum adsorption capacities (qmax) of multi-walled carbon nanotubes (MWCNTs) for 16 rare earth ions were estimated from the experimental data using Langmuir isotherm model. The Langmuir model fitted the experimental data very well, with R2 > 0.98. To explore the impact of ion properties of their adsorption onto MWCNTs, Quantitative Ion Character–Activity Relationships (QICAR) method was applied. The experimental values of qmax were used as dependent variable and the rare earth ions properties were considered as independent variables. Based on covalent index, a new variable was defined which is highly correlated to qmax (correlation coefficient of 0.776). Different variable selection techniques and wrapper models were applied to choose the best subset of variable. Among all developed linear and non-linear models constructed using support vector machine regression (SVR), neural networks, random forest and multiple linear regression, the SVR model presents the best statistical performance. The result of the Wrapper-SVR developed model, with R2c = 0.993 and R2p = 0.794, was excellent than those of other models. The importance of some thermodynamic parameters, Gibbs free energy (ΔG0f) and enthalpy of formation (ΔH0f) of the trivalent lanthanide ions (M+3) at 25°C, indicated the role of thermodynamic aspects on adsorption mechanism. Atomic radius, ionisation energy and electronegativity of rare earth ions should also be considered.

Predicting metal toxicity to aquatic life:a first step towards integrating a QICAR approach with the BLM framework

Abstract The biotic ligand model (BLM) is a tool that quantifies the bioavailability of metals, accounting for both metal speciation and hydrogeochemical properties. The BLM assumes that metal ions bind to biologically active sites, the so-called biotic ligands (BLs), and that metal toxicity is caused by the binding of metal ions to BLs. The binding affinity of a given metal ion for the BLs is represented by a conditional binding constant (K). This parameter is typically obtained via metal toxicity testing. However, metal toxicity testing is time consuming, labor intensive, and expensive. Thus, there is an urgent need for a system to predict BLMs for data-poor metals. Herein, relationships between the log K values of known metals (derived from the literature) and metal ionic characters were investigated to construct quantitative ion character-activity relationship (QICAR) equations. The results showed that log K was significantly correlated with the softness index (σp) in rainbow trout (adjusted R2 = 0.901; F = 65.0; p

Quantifying correlations of metal ionic characters with ecological soil screening levels (Eco-SSLs) of metals using QICAR models

Soil pollution by heavy metals is a major challenge for soil ecosystems; therefore, many countries have published thresholds or standards for protecting soil organisms based on toxicity testing. However, there have been few studies on the mechanism of metal toxicity on organisms in soils, especially relationships between metal's ionic properties and its toxicity. Herein, we selected environmental soil screening levels (Eco-SSLs), which are internationally recognized ecotoxicity values recommended by the United States Environmental Protection Agency (USEPA), and investigated relationships between Eco-SSLs and metal ionic characteristics. The results showed that several ionic characteristics were significantly correlated with Eco-SSL using a classification of metal ions according to hard and soft acids and bases. Electrochemical potential, atomic ionization potential, the first hydrolysis constant, the maximum complex stability constant, a polarization force parameter and covalent radius showed significant correlations with Eco-SSLs for borderline plus hard ions, while the soft index exhibited significant fitting for borderline plus soft ions, suggesting that ionic bonding and covalent bonding played important roles in metal toxicity on borderline plus hard ions and soft ions, respectively. Then, we chose characteristics that had the strongest correlations with Eco-SSLs, and developed quantitative ion character–activity relationship (QICAR) for soil organisms. The QICARs predicted Eco-SSLs for metals that were reasonably consistent with those recommended by USEPA, with differences between them generally <0.5 orders of magnitude. Overall, QICAR provide a basis for ecological risk assessment and could be useful to interpret relationships between metal's ionic properties and its toxicity.

Development of a coupled model of quantitative ion character-activity relationships-biotic ligand model (QICARs-BLM) for predicting toxicity for data poor metals.

The biotic ligand model (BLM) is proposed as a tool to quantitatively evaluate biological toxicity of metals considering both metal speciation and the influence of environmental conditions. The model assumes that biological sites bind to metals as biotic ligands (BLs) and obtains a series of BLM parameters including conditional binding constants (K). However, developing a BLM for each metal and biology takes a lot of experimentation. In the present study, relationships between metal ionic characters and BLM parameter K were respectively investigated for three terrestrial organisms. The results showed that ionization potential was the most strongly related to log K for barley (R2 = 0.845, p < 0.01) and earthworm (R2 = 0.881, p < 0.01), and electronegativity index most significantly related to log K for lettuce (R2 = 0.835, p < 0.01). Based on these relationships, a set of quantitative ion character-activity relationships (QICARs) were developed for predicting log K of metals. Then the QICAR were coupled with BLM and a novel QICAR-BLM was constructed. Finally, the QICAR-BLM was applied to predict EC50 of other unknown-toxicity metals for selected species, and compensate for the lack of toxicity data for a large number of metals in soil.

An exploratory study using QICAR models for prediction of adsorption capacity of multi-walled carbon nanotubes for heavy metal ions

ABSTRACTThe Quantitative Ion Character–Activity Relationship (QICAR) method was used for correlating metal ionic characteristics with the maximum adsorption capacity (qmax) of multi-walled carbon for heavy metals. The experimental values of qmax for 25 heavy metal ions, estimated by the Langmuir isotherm model, were used to construct a QICAR model. The genetic algorithm, enhanced replacement method and successive projection algorithm procedures were applied as variable selection algorithms to choose the optimal subsets of descriptors. The selected variables were correlated with qmax values by using partial least squares (PLS) regression. Orthogonal signal correction was applied as a pre-processing technique. Among of different variable selection methods, the enhanced replacement method displayed noticeable statistical parameters of the final model. The results of the enhancement replacement method–orthogonal correction signal–PLS model, with RMSEC = 0.733, r2c = 0.999 and r2p = 0.946, were excellent and dramatically better than those of other models. The developed QICAR model satisfied the internal and external validation criteria. The importance of electronegativity, ionic radius and atomic number of the heavy metal ions indicated the impact of the tendency to accept electrons and the size of ions in adsorption on carbon nanotubes.

Derivation of marine water quality criteria for metals based on a novel QICAR-SSD model.

Establishment of water quality criteria (WQC) is one procedure for protection of marine organisms and their ecosystems. This study, which integrated two separate approaches, quantitative ion character-activity relationships (QICARs) and species sensitivity distributions (SSDs), developed a novel QICAR-SSD model. The QICARs predict relative potencies of individual elements while SSDs integrate relative sensitivities among organisms. The QICAR-SSD approach was applied to derive saltwater WQC for 34 metals or metalloids. Relationships between physicochemical properties of metal ions and their corresponding potencies for acute toxicity to eight selected marine species were determined. The softness index (σp) exhibited the strongest correlation with the acute toxicity of metals (r (2) > 0.66, F > 5.88, P < 0.94 × 10(-2)). Predictive criteria maximum concentrations for the eight metals, derived by applying the SSD approach to values predicted by use of QICARs, were within the same order of magnitude as values recommended by the US EPA (2009). In general, the results support that the QICAR-SSD approach is a rapid method to estimate WQC for metals for which little or no information is available for marine organisms.

Biosorption of Toxic Heavy Metals on Sawdust

The present investigation deals with the biosorption of Zn2+, Cr3+, and Pb2+ in the liquid phase on the surface of zinc sequestering bacterium VMSDCM (accession no. HQ108109) cells immobilized on pineapple peel powder particles (the term sawdust has been used for immobilized microorganisms). In batch studies, the biosorption data design and regression model for predicting the relationship between the metal ion and their biosorption capacity used were Minimum Run Res V Design and quantitative ion character activity relationship (QICAR), respectively. The values of qmax calculated by the Langmuir isotherm were found to be in increasing order of Cr3+ &lt; Zn2+ &lt; Pb2+. The results of the present investigation showed that the pH and contact time played an important role in the removal of Zn2+ from the liquid phase. In case of Cr3+, the significant independent parameters associated with metal ion removal were the initial concentration of the metal ion and the pH. The important independent parameters for Pb2+ removal were the temperature (°C) and pH, respectively. In QICAR modeling, among all six physico‐chemical characteristics only the ionization potential (eV) played a negative role in the biosorption of metal ions. The rationale behind the inverse relationship between the removal of metal ions and eV was the variation of the corresponding covalent index. The rest of the remaining five physico‐chemical properties showed that regression models fitted quite well and were characterized by high values of the linear regression coefficient (R2), adjusted R2, and low values of the error function (χ2). In the present work, it was concluded that the immobilized cells of sawdust can be a suitable alternative for the removal of heavy metal ions from the liquid phase. The initial concentration of all the metals was kept at 50 mg L−1 each in the liquid phase. The residual concentrations of Zn2+, Cr3+, and Pb2+ after the biosorption were 0.4, 1.2, and 0.2 mg L−1, respectively.

Predicting criteria continuous concentrations of 34 metals or metalloids by use of quantitative ion character-activity relationships–species sensitivity distributions (QICAR–SSD) model

Criteria continuous concentrations (CCCs) are useful for describing chronic exposure to pollutants and setting water quality standards to protect aquatic life. However, because of financial, practical, or ethical restrictions on toxicity testing, few data are available to derive CCCs. In this study, CCCs for 34 metals or metalloids were derived using quantitative ion character-activity relationships–species sensitivity distributions (QICAR–SSD) and the final acute-chronic ratio (FACR) method. The results showed that chronic toxic potencies were correlated with several physico-chemical properties among eight species chosen, where the softness index was the most predictive characteristic. Predicted CCCs for most of the metals, except for Lead and Iron, were within a range of 10-fold of values recommended by the U.S. EPA. The QICAR–SSD model was superior to the FACR method for prediction of data-poor metals. This would have significance for predicting toxic potencies and criteria thresholds of more metals or metalloids.

Cardiovascular Outcomes and the Physical and Chemical Properties of Metal Ions Found in Particulate Matter Air Pollution: A QICAR Study

Background: This paper presents an application of quantitative ion character–activity relationships (QICAR) to estimate associations of human cardiovascular (CV) diseases (CVDs) with a set of metal ion properties commonly observed in ambient air pollutants. QICAR has previously been used to predict ecotoxicity of inorganic metal ions based on ion properties.Objectives: The objective of this work was to examine potential associations of biological end points with a set of physical and chemical properties describing inorganic metal ions present in exposures using QICAR.Methods: Chemical and physical properties of 17 metal ions were obtained from peer-reviewed publications. Associations of cardiac arrhythmia, myocardial ischemia, myocardial infarction, stroke, and thrombosis with exposures to metal ions (measured as inference scores) were obtained from the Comparative Toxicogenomics Database (CTD). Robust regressions were applied to estimate the associations of CVDs with ion properties.Results: CVD was statistically significantly associated (Bonferroni-adjusted significance level of 0.003) with many ion properties reflecting ion size, solubility, oxidation potential, and abilities to form covalent and ionic bonds. The properties are relevant for reactive oxygen species (ROS) generation, which has been identified as a possible mechanism leading to CVDs.Conclusion: QICAR has the potential to complement existing epidemiologic methods for estimating associations between CVDs and air pollutant exposures by providing clues about the underlying mechanisms that may explain these associations.

Predicting Water Quality Criteria for Protecting Aquatic Life from Physicochemical Properties of Metals or Metalloids

Metals are widely distributed pollutants in water and can have detrimental effects on some aquatic life and humans. Over the past few decades, the United States Environmental Protection Agency (U.S. EPA) has published a series of criteria guidelines, which contain specific criteria maximum concentrations (CMCs) for 10 metals. However, CMCs for other metals are still lacking because of financial, practical, or ethical restrictions on toxicity testing. Herein, a quantitative structure activity relationship (QSAR) method was used to develop a set of predictive relationships, based on physical and chemical characteristics of metals, and predict acute toxicities of each species for five phyla and eight families of organisms for 25 metals or metalloids. In addition, species sensitivity distributions (SSDs) were developed as independent methods for determining predictive CMCs. The quantitative ion character-activity relationships (QICAR) analysis showed that the softness index (σp), maximum complex stability constants (log -β(n)), electrochemical potential (ΔE(0)), and covalent index (X(m)(2)r) were the minimum set of structure parameters required to predict toxicity of metals to eight families of representative organisms. Predicted CMCs for 10 metals are in reasonable agreement with those recommended previously by U.S. EPA within a difference of 1.5 orders of magnitude. CMCs were significantly related to σp (r(2) = 0.76, P = 7.02 × 10(-9)) and log -β(n) (r(2) = 0.73, P = 3.88 × 10(-8)). The novel QICAR-SSD model reported here is a rapid, cost-effective, and reasonably accurate method, which can provide a beneficial supplement to existing methodologies for developing preliminarily screen level toxicities or criteria for metals, for which little or no relevant information on the toxicity to particular classes of aquatic organisms exists.

Using the QICAR Model to Correlate Metal Ion Characteristics with Toxicity Order Numbers

ABSTRACT The quantitative ion character–activity relationship (QICAR) was used to correlate nine ion characteristics with ion toxicity order numbers (TON) in 19 metals. A multi-parameter regression model was used to simulate the metals toxicity order numbers after minimization of the multicollinearity among the ion characteristics using principal component analysis (PCA). The toxicity order numbers of the metals increased with the positively correlated variables AN, Xm 2r, AN/ΔIP, AW, and Xm , and decreased with the negatively correlated variables ΔE 0, |logK OH|, AR/AW, and σ P . The regression model provided high prediction ability, with Nash-Suttcliffe simulation efficiency coefficients (NSC) of 0.93 for the modeling phase and 0.87 for the testing phases. The model may be successfully employed to predict the stability constants and metal toxicity and used as a first step in the further risk assessment modeling.

Prediction of metal cation toxicity to the bioluminescent fungus Gerronema viridilucens

A correlation between the physicochemical properties of mono- [Li(I), K(I), Na(I)] and divalent [Cd(II), Cu(II), Mn(II), Ni(II), Co(II), Zn(II), Mg(II), Ca(II)] metal cations and their toxicity (evaluated by the free ion median effective concentration, EC50(F)) to the naturally bioluminescent fungus Gerronema viridilucens has been studied using the quantitative ion character-activity relationship (QICAR) approach. Among the 11 ionic parameters used in the current study, a univariate model based on the covalent index (X(2) (m)r) proved to be the most adequate for prediction of fungal metal toxicity evaluated by the logarithm of free ion median effective concentration (log EC50(F)): log EC50(F) = 4.243 (± 0.243) -1.268 (± 0.125)· X(2) (m)r (adj-R(2) = 0.9113, Alkaike information criterion [AIC] = -60.42). Additional two- and three-variable models were also tested and proved less suitable to fit the experimental data. These results indicate that covalent bonding is a good indicator of metal inherent toxicity to bioluminescent fungi. Furthermore, the toxicity of additional metal ions [Ag(I), Cs(I), Sr(II), Ba(II), Fe(II), Hg(II), and Pb(II)] to G. viridilucens was predicted, and Pb was found to be the most toxic metal to this bioluminescent fungus (EC50(F)): Pb(II) > Ag(I) > Hg(I) > Cd(II) > Cu(II) > Co(II) ≈ Ni(II) > Mn(II) > Fe(II) ≈ Zn(II) > Mg(II) ≈ Ba(II) ≈ Cs(I) > Li(I) > K(I) ≈ Na(I) ≈ Sr(II)> Ca(II).

Correlating metal ionic characteristics with biosorption capacity of Staphylococcus saprophyticus BMSZ711 using QICAR model

Quantitative Ion Character–Activity Relationship (QICAR) was used for correlating metal ionic properties with maximum biosorption capacity ( q max). Heat inactivated biomass of Staphylococcus saprophyticus BMSZ711 was studied for biosorption of nine metal ions. Influence of contact time and initial pH was checked. q max was determined by Langmuir isotherm and followed a descending sequence (in mmol/g): Pb 2+ > Cd 2+ > Cr 3+ > Zn 2+ > Hg 2+ > Cu 2+ > Co 2+ > Ni 2+ > K +. q max values was modeled with 20 metal ionic characteristics, among these covalent index ( X m 2 r) was best fitted with q max for all metal ions tested, in the following model: q max = 0.09 + 0.11( X m 2 r) ( R 2 = 0.73, AIC = −4.14). Classification of metal ions according to valence or soft/hard improved QICARs modeling and more characteristics significantly correlated with q max which revealed that covalent bonding played major role in biosorption of soft metal ions and ionic bonding for borderline and hard ions. Biosorption capacity was most effectively predicted (R 2 = 0.99, AIC = −8.04) with a two variable model containing electro-negativity ( X m) and softness index ( o ρ ′ ) .

Advances in Quantitative Ion Character‐Activity Relationships (QICARs): Using Metal‐Ligand Binding Characteristics to Predict Metal Toxicity

AbstractEnvironmental toxicologists readily adopted QSARs from pharmacology to predict organic contaminant toxicity. In contrast, models relating metal ion characteristics to their bioactivity remain poorly explored and underutilized. Quantitative Ion Character‐Activity Relationships (QICARs) have recently been developed to predict metal toxicity. The QICAR approach, based on metal‐ligand binding tendencies, has been applied successfully to a wide range of effects, species, and media on a single metal basis. In previous single metal studies, a softness parameter and the |log KOH| were among the ion qualities with the highest predictive value for toxicity.

Using metal-ligand binding characteristics to predict metal toxicity: quantitative ion character-activity relationships (QICARs).

Ecological risk assessment can be enhanced with predictive models for metal toxicity. Modelings of published data were done under the simplifying assumption that intermetal trends in toxicity reflect relative metal-ligand complex stabilities. This idea has been invoked successfully since 1904 but has yet to be applied widely in quantitative ecotoxicology. Intermetal trends in toxicity were successfully modeled with ion characteristics reflecting metal binding to ligands for a wide range of effects. Most models were useful for predictive purposes based on an F-ratio criterion and cross-validation, but anomalous predictions did occur if speciation was ignored. In general, models for metals with the same valence (i.e., divalent metals) were better than those combining mono-, di-, and trivalent metals. The softness parameter (sigma p) and the absolute value of the log of the first hydrolysis constant ([symbol: see text] log KOH [symbol: see text]) were especially useful in model construction. Also, delta E0 contributed substantially to several of the two-variable models. In contrast, quantitative attempts to predict metal interactions in binary mixtures based on metal-ligand complex stabilities were not successful.