Correlation of physico-chemical parameters with toxicity of metal ions to plants

Enache, Monica-Adela; Palit, Piali; Dearden, John C.; Lepp, Nicholas W.

doi:10.1002/1526-4998(200009)56:9<821::aid-ps203>3.0.co;2-r

Cited by 5 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Apart from the study of Kahlenberg and True [43], Grushkin [69] and Serrano and Barceló [111], there have been very few QSAR investigations of metal toxicity to higher plants or plant‐based systems. Recently, Enache et al [112–114] developed (Q)CAR models based on toxicity data from the sunflower ( Helianthus annuus F.1. Sunspot) and found a large number of correlations with relevant physical and chemical descriptors (Table 3).…”

Section: The Covalent Indexmentioning

confidence: 99%

Quantitative cationic‐activity relationships for predicting toxicity of metals

Walker

Enache

Dearden

2003

Enviro Toxic and Chemistry

Self Cite

View full text Add to dashboard Cite

Developing and validating quantitative cationic-activity relationships or (Q)CARs to predict the toxicity metals is challenging because of issues associated with metal speciation, complexation and interactions within biological systems and the media used to study these interactions. However, a number of simplifying assumptions can be used to develop and validate (Q)CARs to predict the toxicity of metals: The ionic form is the most active form of a metal; the bioactivity of a dissolved metal is correlated with its free ion concentration or activity; most metals exist in biological systems as cations, and differences in metal toxicity result from differences in metal ion binding to biological molecules (ligand-binding). In summary, it appears that certain useful correlations can be made between several physical and chemical properties of ions (mostly cations) and toxicity of metals. This review provides a historical perspective of studies that have reported correlations between physical and chemical properties of cations and toxicity to mammalian and nonmammalian species using in vitro and in vivo assays. To prepare this review, approximately 100 contributions dating from 1839 to 2003 were evaluated and the relationships between about 20 physical and chemical properties of cations and their potential to produce toxic effects were examined.

show abstract

Section: The Covalent Indexmentioning

confidence: 99%

Quantitative cationic‐activity relationships for predicting toxicity of metals

Walker

Enache

Dearden

2003

Enviro Toxic and Chemistry

Self Cite

View full text Add to dashboard Cite

show abstract

“…In a similar study, Enache et al . 24 noticed that increased inherent toxicity of metals was generally accompanied with increasing AN, CR and r of cabbage plants ( Brassica oleracea L var capitata cv Soshu).…”

Section: Resultsmentioning

confidence: 99%

“…Based on the results of several previous studies 12 28 51 52 53 54 , 26 structural parameters characterizing various physical and chemical properties of the metal ions were investigated. They include AN 12 , AW 28 52 , AR 28 52 53 55 , CR 51 52 53 , r 12 28 52 53 , melting point ( MP ) 52 , density ( D ) 52 , enthalpy (heat) of vaporization ( Eh ) 52 , boiling point ( BP ) 52 , difference in ionization potentials between the ion oxidation numbers OX and OX −1 (Δ IP(eV) ) 12 28 54 , electrochemical potential (∆ E 0 (V) ) 12 28 52 54 , log of first hydrolysis constant ( |logK OH | ) 12 , covalent index ( X m 2 r ) 12 24 , polarization force parameters ( Z/r,Z/r 2 and Z 2 /r ) 12 24 , σp 12 , ionization potential ( IP ) 12 23 24 26 , electronegativity ( X m ) 12 23 24 , AN/∆IP 12 23 24 26 , AR/AW 23 24 , electronegativity index ( x ) 12 23 , relative softness ( Z/rx ) (x is a electronegativity value index) 12 23 24 , similar polarization force parameters ( Z/AR and Z/AR 2 ) …”

Section: Methodsmentioning

confidence: 99%

Directly Predicting Water Quality Criteria from Physicochemical Properties of Transition Metals

Wang

et al. 2016

Sci Rep

View full text Add to dashboard Cite

Transition metals are a group of elements widespread in aquatic environments that can be hazardous when concentrations exceeding threshold values. Due to insufficient data, criteria maximum concentrations (CMCs) of only seven transition metals for protecting aquatic life have been recommended by the USEPA. Hence, it is deemed necessary to develop empirical models for predicting the threshold values of water quality criteria (WQC) for other transition metals for which insufficient information on toxic potency is available. The present study established quantitative relationships between recommended CMCs and physicochemical parameters of seven transition metals, then used the developed relationships to predict CMCs for other transition metals. Seven of 26 physicochemical parameters examined were significantly correlated with the recommended CMCs. Based on this, five of the seven parameters were selected to construct a linear free energy model for predicting CMCs. The most relevant parameters were identified through principle component analysis, and the one with the best correlation with the recommended CMCs was a combination of covalent radius, ionic radius and electron density. Predicted values were largely consistent with their toxic potency values. The present study provides an alternative approach to develop screening threshold level for metals which have insufficient information to use traditional methods.

show abstract

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

Ownby¹,

Newman²

2003

QSAR Comb. Sci.

View full text Add to dashboard Cite

Environmental 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 j log K OH j were among the ion qualities with the highest predictive value for toxicity. Here, QICAR modeling is extended to predict toxicity using data from the US EPA ECOTOX database and for binary metal mixtures. Using the US EPA ECOTOX database, predictive single metal models were produced for four fish species (bluegill, carp, fathead minnow, and mummichog). Using the Microtox bioassay, the interactions of binary mixtures of metals (Co, Cu, Mn, Ni, and Zn) were quantified using a linear model with an interaction term. A predictive relationship was developed for metal interaction between metal pairs and the difference in softness. This study supports the hypothesis that general prediction of metal toxicity and interactions from ion characteristics is feasible. It is important that additional work with metals of different valences and sizes be done to further enhance the general accuracy of metal interaction predictions.

show abstract

Correlation of physico-chemical parameters with toxicity of metal ions to plants

Cited by 5 publications

References 17 publications

Quantitative cationic‐activity relationships for predicting toxicity of metals

Quantitative cationic‐activity relationships for predicting toxicity of metals

Directly Predicting Water Quality Criteria from Physicochemical Properties of Transition Metals

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

Contact Info

Product

Resources

About