Abstract
Environmental pollution has been the focus of increasing concerns over potential harmful effects on human health and the environment. Amongst the available options for environmental cleanup, technologies based on biological remediation have emerged as low-cost, low-maintenance, environment-friendly, and renewable technologies for potential in situ remediation of organic and inorganic contaminants. However, both microbial and plant species used in these technologies have certain limitations, and it is desirable to know in the first instance whether a contaminant would need remedial action, and whether a biological process would be suitable to breakdown or remove it from the environment. This is where computational models based on structure-activity relationship can provide a quick assessment to support decision making. The (Q)SAR models and expert systems can help prioritise contaminants on the basis of potential toxicities, and inform on their likely behaviour and fate in the environment. This information is in turn helpful in the choice of appropriate remediation technologies, as well as in identifying the recalcitrant chemicals that can be monitored as markers for the success of remediation action. This chapter provides an overview of the rationale behind the development of structure-activity relationship models and provides an up-to-date list of the key relevant software tools that are currently available. However, the availability of a large number of software tools also requires a careful choice of appropriate models and/ or expert systems. The overview also shows that there is a need for development of more integrated systems that can cater specifically for biological remediation technologies.
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US EPA. (2009). Estimation Programs Interface Suite™ for Microsoft® Windows, v 4.00. United States Environmental Protection Agency, Washington, DC, USA.
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Price, N., Chaudhry, Q. (2011). SAR Based Computational Models as Decision Making Tools in Bioremediation. In: Schröder, P., Collins, C. (eds) Organic Xenobiotics and Plants. Plant Ecophysiology, vol 8. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9852-8_11
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