project

Computer models for water quality assessment

Around the world, the number of different contaminants that are found in water is increasing. With new, emerging substances and substances that may be produced in the environment or during water treatment (transformation products), it is often the case that hardly any information is available about their toxicological properties and potentially adverse effects on human health and the environment. Computer models, also known as in silico models, for predicting the toxicity of contaminants provide a possible solution here.

In silico toxicology

The chemical structure of a contaminant and data about similar substances can provide insight into the toxicological properties and possible adverse effects on human health and the environment of contaminants for which toxicological data are not available. This helps with the prioritisation of follow-up research, hazard and risk assessment of contaminants, and the targeted deployment of measures and decision-making procedures to mitigate potential risks.

In silico models in practice

We use in silico models for different purposes, such as prioritising substances for studies of treatment efficiency, acquiring insights into the possible health effects of unknown contaminants found in sources of water and drinking water, and assessing the risks for humans and the environment (sometimes in combination with bioassays and/or measurements of substances of very high concern) or non-target screening. In the case of contaminants for which almost no toxicological information is available, an in silico approach is faster and more cost-effective than conducting experiments in a laboratory.

Predicting contaminant behaviour in water treatment and in the environment

In silico models are not limited to predicting the toxicity of contaminants; they also make it possible to predict the properties of a contaminant and how it behaves in the environment and during water treatment. For this purpose, KWR experts have developed the Aqua Priori tool to predict the rate of removal of organic micropollutants in treatment on the basis of the chemical structure of a contaminant.