Models Used to Predict Acute Metal Toxicity - Essay Example

To calculate the water effect ratio first of all one solution is prepared in the laboratory with known toxicity and the average range of pH, dissolved oxygen, temperature, and specific conductivity is calculated. After that, a water sample is collected from a site whose water effect ratio has to be calculated.

Metal toxicity of the site water is calculated and the values of test from the lab sample and site sample are used to calculate the ratio. The free ion activity model is based on the fact that there is always equilibrium between free metal ions in a solution. This model uses Ion characteristics to predict the relative toxicity effects of metal ions. Most ion characteristics that are useful in the predictive modeling of metal toxicity reflect the binding tendencies of metals to ligands (Christopher P. Tatara, Michael C.

Newman, John T. McCloskey, Phillip L. Williams). The concentration of metal ions in a solution depends upon various factors such as temperature and the chemistry of water. The free ion activity model predicts metal toxicity by directly measuring the metal ion concentration in water. This model is based on the assumption that organism only responds to the free-metal ion concentration in solution, regardless of the nature of the metal complexes present in water (J. Phycol, 2005). For example in the case of AgCl(s) Ag+(aq) + Cl-(aq) if the Cl- ion increase that can be consumed by Ag+ ions the concentration of Cl- will increase in water and if the concentration of Cl- ions is lesser than that of Ag+ there will be more free Ag+ cations in the water.

And if the extra Ag+ ions are not consumed by other anions available in water the Ag metal toxicity of water will increase. Biotic Ligand ModelThe Biotic Ligand Model (BLM) uses metal speciation and the protective effects of competing cations (+ve metal ions) to predict metal binding at a surface with the possibility of acute metal toxicity (e.g. gill of a fish). A legend may be defined as a chemical structure that binds with another chemical or metal. It is an effective and widely used model to determine acute metal toxicity of dissolved metals.

It relies on the mathematical integration of the interaction of trace metal or solution phase ligands with biotic ligands i.e. the living organism exposed to the toxicity. This model analyses the ligand's interaction with living beings (biotic ligands). The biotic ligand model predicts the toxicity of dissolved metal according to the quantity of metal deposited on the receptor living being. The following diagram shows the conceptual biotic ligand model. Fig: Conceptual Biotic Legend DiagramBiotic legend model of toxicity prediction clearly shows the effect of water chemistry variation on the toxicity of metal.

The biotic ligand model of toxicity prediction clearly shows the effect of water chemistry variation on the toxicity of metal. Water chemistry which is characterized by dissolved hydrogen, oxygen, and carbon-di-oxide affects the metal toxicity of the water. According to the biotic ligand model, a metal can be toxic if the concentration of that metal in the water exceeds the DOC, calcium (to out-compete for another metals binding organic legend), and dissolved organic carbon altogether.

This model predicts the concentration of a metal ion concerning the other cations and ligands present in water. The concentration of other cations and ligands in water and their binding capacity determines the toxicity of metal. In organic-rich water, a metal will be having a less toxic effect.

The biotic ligand model allows the prediction of changing toxicity of metal in changing water equilibrium due to pH, alkalinity, hardness, and presence of dissolved organic carbon (DOC). For example at a fixed free metal concentration, as hardness increases, the increased Ca2+ competes with the free metal for binding sites at the biotic ligand. A higher free metal concentration is therefore required to achieve the same toxic effect in the presence of elevated Ca2+  concentration (Meyer, 1999).