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

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 (0.05-804.44 μM). 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.

Development of Ion Character Property Relationship (IC-PR) for Removal of 13-Metal Ions by Employing a Novel Green Adsorbent Aerva javanica

The novel Aerva javanica absorbent was applied for the removal of thirteen selected metal ions from a distilled water solution of each metal by the batch adsorption method. The optimization remediation parameters of the metal ions for the batch adsorption approach were developed, which were the initial concentrations (60 ppm), contact time (60 min) and pH (7). The basic properties of metal ion affected the adsorption results; therefore, 21 properties of metal ions were selected, which are called "descriptors". The most significant descriptors were selected that were vital for the adsorption results, such as covalent index, polarizability and ion charge. The developed model equation by the descriptors provided more than 80% accuracy in the predicted results. Furthermore, Freundlich and Langmuir adsorption models were also applied on the results. Constants of the Freundlich and Langmuir models were also used for model generation, and the results revealed the importance of a covalent index for the removal phenomenon of metal ions. The current study provided a suitable Ion Character Property Relationship (IC-PR) for the removal of metal ions, and future predictions can be achieved on the proposed adsorbent with significant accuracy. The ecofriendly and cost effective Aerva javanica absorbent in the batch experimental model of the current study predicted that this novel absorbent can be used for the removal of a wide spectrum of heavy metal ions from different sources of waste waters.

Critical target identification and human health risk ranking of metal ions based on mechanism-driven modeling

Huge amounts of metals have been released into environment due to various anthropogenic activities, such as smelting and processing of metals and subsequent application in construction, automobiles, batteries, optoelectronic devices, and so on, resulting in widespread detection in environmental media. However, some metal ions are considered as "Environmental health hazards", leading to serious human health concerns through affecting critical targets. Hence, it is necessary to quickly and effectively recognize the key target of metal ions in living organisms. Fortunately, the development of high-throughput analysis and in silico approaches offer a promising tool for target identification. In this study, the key oncogenic target (tumor suppressor protein, p53) was screened by network analysis based on the comparative toxicogenomics database (CTD). Some metal ions could bind to p53 core domain, impair its function and induce the development of cancer risk, but its mechanisms were still unclear. Therefore, a quantitative structure-activity relationship (QSAR) model was constructed to characterize the binding constants (K_a) between DNA binding domain of p53 (p53 DBD) and nine metal ions (Mg²⁺, Ca²⁺, Cu²⁺, Zn²⁺, Co²⁺, Ni²⁺, Mn²⁺, Fe³⁺ and Ba²⁺). It had good robustness and predictive ability, which could be used to predict the K_a values of other six metal ions (Li⁺, Ag⁺, Cs⁺, Cd²⁺, Hg²⁺ and Pb²⁺) within application domain. The results showed strong binding affinity between Cd²⁺/Hg²⁺/Pb²⁺ and p53 DBD. Subsequent mechanism analyses revealed that first hydrolysis constant (|logK_OH|) and polarization force (Z²/r) were key metal ion-characteristic parameters. The metal ions with weak hydrolysis constants and strong polarization forces could readily interact with N-containing histidine and S-containing cysteine of p53 DBD, which resulted in high K_a values. This study identified p53 as potential target for metal ions, revealed the key characteristics affecting the actions and provide a basic understanding of metal ions-p53 DBD interaction.

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 (σ_Con) 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.

Developing the relationship between metal ionic characters and ecological risk assessment screening values using QICAR

Metals are widely released and distributed in soil and may have a negative impact on terrestrial organisms. Over the past years, a series of criteria or standards for assessing the ecological risks and toxicity of metals have been published in many countries; however, few studies have investigated their metal ionic properties and toxicity. In the present study, the ecological risk assessment screening values (ERASV) recommended by the Oregon Department of Environmental Quality were selected to investigate the correlation between metal toxicity and their ionic characters based on the hard and soft acids and bases (HSAB) concept. The results showed that more ionic characters were significantly correlated with ERASV using the HSAB theory, while only one metal ionic characteristic was correlated with ERASV in organisms. For borderline metal ions, maximum complex stability constants (log βn) and the softness (δp) of borderline ions were correlated with ERASV, while log βn and electronegativity (Xm) were significantly related to ERASV for borderline plus hard ions, and the boiling point (BP) and electron density (AR/AW) (AR indicates atomic radius and AW is atomic mass) were significantly related to ERASV for borderline plus soft ions. These results indicated that different metal ion characteristics play different roles in different types of metal toxicity in organisms and the mechanisms of toxicity are different. Based on these relationships, a set of quantitative ion characteristic parameter-activity relationship (QICAR) was developed. The QICAR predicted ERASV for metals that were reasonably consistent with those recommended by the Oregon Department of Environmental Quality, with differences between them generally < 2.0 orders of magnitude. However, there were discrepancies between the recommended and predicted values, and these discrepancies may be related to terrestrial geochemical properties. These soil properties should be further considered when developing QICAR models in future studies, such as soil type, organic matter, and pH. Overall, the QICAR models were able to determine the relationships between metal ionic properties and their toxicity and will be useful for assessing toxicity data on unknown toxic metals and will provide a basis for ecological assessment.

Investigation of Competitive and Noncompetitive Adsorption of Some Heavy Metals Ions on Leucodon sciuroides (Hedw.) Schwägr

Heavy metals are an important pollutant group. Adsorption is one of the methods used to remove heavy metals from the environment. Mosses were preferred as bio-indicators because they have the capacity to accumulate many elements by their high surface-to-volume ratio. Leucodon sciuroides (Hedw.) Schwägr. (LS) are mosses that play an important part of the ecosystem and are collected from the Ida Mountain (Kazdag) region of Çanakkale (Turkey). For the purpose of determining the adsorption capacity of heavy metal ion (Pb²⁺, Cd²⁺, Co²⁺, Ni²⁺, Zn²⁺, and Cu²⁺) analysis conditions, pH, contact time, and adsorbent amounts were determined and the maximum adsorption capacity was calculated with the help of the relevant isotherms. Heavy metal concentrations were determined by inductively coupled plasma-mass spectrometry. It was determined that the optimum adsorption for mosses was 30 min at pH = 6.0 (the pH at which maximum adsorption occurs). The adsorption event shows that some divalent cations fit the Freundlich isotherm and some fit the Langmuir isotherm model. A pseudo-second-order reaction best fits the kinetic data for metal ions. Among the six metal ions studied, the highest adsorption was observed in Pb²⁺ and Cu²⁺ cations. According to the competitive adsorption results, the moss has a great advantage in determining the Pb²⁺ and Cu²⁺ cations industrially as well as other metals and in removing other metal impurities from the environment. Also, LS is exploited as a biosorbent to remove metal ions from aqueous solutions and can be used as a biomarker.

Distinct mechanisms in the heteroaggregation of silver nanoparticles with mineral and microbial colloids

Attachment to solids is an important process for determining nanomaterial transport and their fate in environments. Here we revealed distinct behaviours in the attachment of silver nanoparticles (AgNPs) to kaolin and bacterial cells. We found preferential attachment of AgNPs to the edges of kaolin. Decreasing pH or adding metal ions promoted AgNP-kaolin attachment due to the increase of positive charge on kaolin's surfaces. Multivalent cations (Mg²⁺ and Ca²⁺) induced stronger enhancement than monovalent cations (Na⁺, K⁺ and Ag⁺), which demonstrated the positive role of electrostatic interaction in AgNP-kaolin attachment. However, the presence of metal ions inhibited AgNP binding to bacterial cells. The inhibitive effect was significantly correlated with solubility product of metal ions, which implied a chemical reaction mechanism in AgNP-cell attachment. In kaolin system, humic acid (HA) can considerably inhibit AgNP attachment and diminish the enhanced effects induced by metal ions. In contrast, in bacterial cell system, HA reduced the inhibitive effect of metal ions for AgNP adsorption, although HA itself had negligible effect on AgNP-cell attachment. Taken together, our results demonstrated the contribution of electrostatic attraction versus chemical interaction to the attachment of AgNPs to kaolin or bacterial cells, providing fundamental support to understand the attachment of nanomaterials to inorganic and organic solids in the environments.

Electrochemical Pretreatment for Sludge Sulfide Control without Chemical Dosing: A Mechanistic Study

Sulfide is a toxic and corrosive odorant generated in various sludge treatment and disposal systems. We developed an electrochemical pretreatment (EPT) approach to eliminate sludge sulfide production without adding chemicals. Biochemical sulfide potential (BSP) test was used to evaluate the effectiveness of EPT on sludge sulfide production. The sulfide control was effective with EPT, and we determined the underlying mechanism of EPT. EPT which was operated at 12 V for 720 s eliminated 99% of dissolved sulfide and 100% of gaseous H₂S_(g). In comparison, the dissolved sulfide reached 104 ± 1 mg S/L in the control BSP test. A sulfur mass balance analysis in the BSP test showed that 90% of the produced sulfide was removed via metal precipitation. Metal distribution results confirmed that metals (i.e., Fe, Mn, and Ni) in the sludge became soluble after EPT and were released from their residual and organically bound fractions. EPT which was operated at 15 V solubilized around 73, 92, and 72% of Fe, Mn, and Ni, and these metals precipitated the sulfide that was produced from biological sulfate reduction. Sludge analysis revealed that EPT disintegrated sludge flocs and disrupted metal-binding functional groups. Specifically, reduction of 17% C═O functional groups in the sludge was found, which could be associated with metal release. The impact of oxidants (e.g., chlorine) generated from EPT on sulfide oxidation was minimal. The findings of this study broadened up our understanding of the electrochemical process for sulfide control during saline sludge digestion.

Rationalization and prediction of the impact of different metals and root exudates on carbon dioxide emission from soil

Quantitative structure-property relationship modeling method developed from the quantum four-element concept of electronic attributes is validated by the accurate prediction of the redox potentials, deprotonation constants, stability constants and the maximum biosorption capacity of various metal ions and verification of the toxicological endpoint of soil nematodes and mouse. This approach is an extremely promising tool for rationalizing and predicting the toxicity and mobility of different metals and the emission of carbon dioxide from soil. The results demonstrate that the high fungal toxicity and low ion-humic acid binding of cadmium (2+) and the strong complexation of root exudates, such as oxalic acid, citric acid and malic acid, are most likely to promote carbon release.

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.

Impact of metal ionic characteristics on adsorption potential of Ficus carica leaves using QSPR modeling

The present study describes Quantitative Structure Property Relationship (QSPR) modeling to relate metal ions characteristics with adsorption potential of Ficus carica leaves for 13 selected metal ions (Ca⁺², Cr⁺³, Co⁺², Cu⁺², Cd⁺², K⁺¹, Mg⁺², Mn⁺², Na⁺¹, Ni⁺², Pb⁺², Zn⁺², and Fe⁺²) to generate QSPR model. A set of 21 characteristic descriptors were selected and relationship of these metal characteristics with adsorptive behavior of metal ions was investigated. Stepwise Multiple Linear Regression (SMLR) analysis and Artificial Neural Network (ANN) were applied for descriptors selection and model generation. Langmuir and Freundlich isotherms were also applied on adsorption data to generate proper correlation for experimental findings. Model generated indicated covalent index as the most significant descriptor, which is responsible for more than 90% predictive adsorption (α = 0.05). Internal validation of model was performed by measuring [Formula: see text] (0.98). The results indicate that present model is a useful tool for prediction of adsorptive behavior of different metal ions based on their ionic characteristics.

Application of carbon foam for heavy metal removal from industrial plating wastewater and toxicity evaluation of the adsorbent

Electroplating wastewater contains various types of toxic substances, such as heavy metals, solvents, and cleaning agents. Carbon foam was used as an adsorbent for the removal of heavy metals from real industrial plating wastewater. Its sorption capacity was compared with those of a commercial ion-exchange resin (BC258) and a heavy metal adsorbent (CupriSorb™) in a batch system. The experimental carbon foam has a considerably higher sorption capacity for Cr and Cu than commercial adsorbents for acid/alkali wastewater and cyanide wastewater. Additionally, cytotoxicity test showed that the newly developed adsorbent has low cytotoxic effects on three kinds of human cells. In a pilot plant, the carbon foam had higher sorption capacity for Cr (73.64 g kg(-1)) than for Cu (14.86 g kg(-1)) and Ni (7.74 g kg(-1)) during 350 h of operation time. Oxidation pretreatments using UV/hydrogen peroxide enhance heavy metal removal from plating wastewater containing cyanide compounds.

Bioremediation of a complex industrial effluent by biosorbents derived from freshwater macroalgae

Biosorption with macroalgae is a promising technology for the bioremediation of industrial effluents. However, the vast majority of research has been conducted on simple mock effluents with little data available on the performance of biosorbents in complex effluents. Here we evaluate the efficacy of dried biomass, biochar, and Fe-treated biomass and biochar to remediate 21 elements from a real-world industrial effluent from a coal-fired power station. The biosorbents were produced from the freshwater macroalga Oedogonium sp. (Chlorophyta) that is native to the industrial site from which the effluent was sourced, and which has been intensively cultivated to provide a feed stock for biosorbents. The effect of pH and exposure time on sorption was also assessed. These biosorbents showed specificity for different suites of elements, primarily differentiated by ionic charge. Overall, biochar and Fe-biochar were more successful biosorbents than their biomass counterparts. Fe-biochar adsorbed metalloids (As, Mo, and Se) at rates independent of effluent pH, while untreated biochar removed metals (Al, Cd, Ni and Zn) at rates dependent on pH. This study demonstrates that the biomass of Oedogonium is an effective substrate for the production of biosorbents to remediate both metals and metalloids from a complex industrial effluent.

Insight into the roles of microbial extracellular polymer substances in metal biosorption

Biosorption presents a potent technology to remediate metal-contaminated aqueous environment or even to recover precious metals. Extracellular polymeric substances (EPS) are believed to play an important role in metal biosorption by microorganisms, but the reported results have been rather contradictory and the underlying mechanisms remain largely unclear so far. This review aims to clarify why large discrepancies existed for different EPS-metal systems through systematically exploring into the adsorption mechanisms and influential factors, and to offer some implications for advancing the implementation of metal biosorption technologies. The state-of-the-art methodologies for characterizing metal-EPS binding are summarized; several interaction mechanisms, including ion exchange, complexation and surface precipitation, are analyzed; the major influential factors such as EPS composition, metal species, solution chemistry and operating conditions are discussed; and lastly future research needs to advance the investigations and implementations of such biosorption processes are proposed.

Prediction of mono-, bi-, and trivalent metal cation relative toxicity to the seaweed Gracilaria domingensis (Gracilariales, Rhodophyta) in synthetic seawater

The present study reports a 48-h aquatic metal-toxicity assay based on daily growth rates of the red seaweed Gracilaria domingensis (Gracilariales, Rhodophyta) in synthetic seawater. The median inhibitory concentration (IC50) for each metal cation was experimentally determined, and the ratios of free ions (aqueous complex) were calculated by software minimization of the total equilibrium activity (MINTEQA2) to determine the free median inhibitory concentration (IC50F). A model for predicting the toxicity of 14 metal cations was developed using the generic function approximation algorithm (GFA) with log IC50F values as the dependent variables and the following properties as independent variables: ionic radius (r), atomic number (AN), electronegativity (Xm ), covalent index (Xm (2) r), first hydrolysis constant (|log KOH |), softness index (σp ), ion charge (Z), ionization potential (ΔIP), electrochemical potential (ΔEo ), atomic number divided by ionization potential (AN/ΔIP), and the cation polarizing power for Z(2) /r and Z/AR. The 3-term independent variables were predicted as the best-fit model (log IC50F: -23.64 + 5.59 Z/AR + 0.99 |log KOH | + 37.05 σp ; adjusted r(2) : 0.88; predicted r(2) : 0.68; Friedman lack-of-fit score: 1.6). This mathematical expression can be used to predict metal-biomolecule interactions, as well as the toxicity of mono-, bi-, and trivalent metal cations, which have not been experimentally tested in seaweed to date. Quantitative ion-character relationships allowed the authors to infer that the mechanism of toxicity might involve an interaction between metals and functional groups of biological species containing sulfur or oxygen.

Prediction of biosorption of total chromium by Bacillus sp. using artificial neural network

An artificial neural network (ANN) model was developed to predict the biosorption efficiency of Bacillus sp. for the removal of total chromium from aqueous solution based on 360 data sets obtained in a laboratory batch study. Experimental parameters affecting the biosorption process such as pH, contact time and initial concentration of chromium were studied. A contact time of 2 h was generally sufficient to achieve equilibrium. At optimal conditions, metal ion uptake increased with increasing initial metal ion concentration. The Freundlich model was applied to describe the biosorption isotherm. Chromium biosorption was most significantly influenced by pH, followed by the initial metal concentration of the solution. The findings indicated that the ANN model provided reasonable predictive performance (R(2) = 0.971) of chromium biosorption.

Identification and characterization of Leucobacter sp. N-4 for Ni (II) biosorption by response surface methodology

In the present study, batch experiments were carried out to characterize and optimize the removal process of Ni (II) by a nickel tolerant strain Leucobacter sp. N-4, which was isolated from the soil samples. The effects of operating parameters with respect to initial solution pH (3.0-6.5), initial nickel concentration (50-100mg/L) and biomass dosage (1-10 g/L) on Ni (II) biosorption were investigated by response surface methodology (RSM). The maximal Ni (II) removal efficiency (nearly 99%) was achieved under the following conditions: pH 4.75, biomass dosage 5.38 g/L and initial Ni (II) concentration 53.6 mg/L. The adsorption-equilibrium data fitted well with both Langmuir and Freundlich isotherms. The monolayer adsorption capacity of biomass obtained from Langmuir isotherm was about 19.6 mg/g. Infrared spectrometer (IR) results showed that chemical functional groups (e.g. -NH(2), -OH and COO-M) of the biomass should be the active binding sites for Ni (II) biosorption from aqueous solutions.

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).

Modelling the relative toxicity of metals on respiration of nitrifiers using ion characteristics

The effects of eight transition metals were studied in a nitrifying system to investigate the relationship between the ionic characteristics of metals and their toxicity to nitrifiers. The cumulative oxygen consumption and the cumulative carbon dioxide production were monitored throughout each respirometric batch run to determine the toxicity of metals to nitrifiers. Several quantitative cationic-activity relationship (QCAR) models were developed on the basis of these different toxicity endpoints using quantum chemical descriptors. Descriptors were calculated with density functional theory (DFT) at the B3LYP/LANL2DZ level using the Gaussian 03W software. Additionally, the same descriptors were recalculated using another basis set, B3LYP/SDD, to test the impact of the basis set on prediction of toxicity. Of the calculated descriptors, mainly the gaseous phase descriptors explained significant variances in both toxicity endpoints. However, the energy of the polarized solute-solvent (E(PSS)) was the most important common descriptors in modelling labile toxicity. A combination of the aqueous phase energy of the highest occupied molecular orbital (E(HOMO(aq))) and the maximum value for the energy of the lowest unoccupied molecular orbital of the most important metal species (E(LUMO)C(max)) produced the best two-descriptor model for both pTO(2) and pTCO(2). The electron donor/acceptor ability of metals and the electron acceptor ability of metal species (E(LUMO)C(max)) seemed to be important in explaining toxicity in aqueous media regardless of the measured endpoints for nitrifiers.