Modelling assisted phytoremediation of soils contaminated with heavy metals - Main opportunities, limitations, decision making and future prospects

The heavy metals (HMs) soils contamination is a growing concern since HMs are not biodegradable and can accumulate in all living organisms causing a threat to plants and animals, including humans. Phytoremediation is a cost-efficient technology that uses plants to remove, transform or detoxify contaminants. In recent years, phytoremediation is entering the stage of large-scale modelling via various mathematical models. Such models can be useful tools to further our understanding and predicting of the processes that influence the efficiency of phytoremediation and to precisely plan such actions on a large-scale. When dealing with extremely complicated and challenging variables like the interactions between the climate, soil and plants, modelling before starting an operation can significantly reduce the time and cost of such process by granting us an accurate prediction of possible outcomes. Research on the applicability of different modelling approaches is ongoing and presented work compares and discusses available models in order to point out their specific strengths and weaknesses in given scenarios. The main aim of this paper is to critically evaluate the main advantages and limitations of available models for large-scale phytoremediation including, among others, the Decision Support System (DSS), Response Surface Methodology (RSM), BALANS, PLANTIX and various regression models. Study compares their applicability and highlight existing gaps in current knowledge with a special reference to improving the efficiency of large-scale phytoremediation of sites contaminated with heavy-metals. The presented work can serve as a useful tool when choosing the most suitable model for the phytoremediation of contaminated sites.