| description abstract | In recent years, phytoremediation, i.e., the use of plants to clean up soils contaminated with organics, has become a promising new area of research, particularly for in-situ cleanup of large volumes of slightly contaminated soils. A model that can be used as a predictive tool in phytoremediation operations was developed to simulate the transport and fate of a residual hydrocarbon contaminant interacting with plant roots in a partially saturated soil. Time-specific distribution of root quantity through soil, as well as root uptake of soil water and hydrocarbon, was incorporated into the model. In addition, the microbial activity in the soil rhizosphere was modeled with a biofilm theory. A sandy loam, which is dominant in soils of agricultural importance, was selected for simulations. Cotton, which has well-documented plant properties, was used as the model plant. Model parameters involving root growth and root distribution were obtained from the actual field data reported in the literature and ranges of reported literature values were used to obtain a realistic simulation of a phytoremediation operation. Following the verification of the root growth model with published experimental data, it has been demonstrated that plant characteristics such as the root radius are more dominant than contaminant properties in the overall rate of phytoremediation operation. The simulation results showed enhanced biodegradation of a hydrocarbon contaminant mostly because of increased biofilm metabolism of organic contaminants in a growing root system of cotton. Simulations also show that a high mean daily root-water uptake rate increases the contaminant retardation factors because of the resulting low water content. The ability to simulate the fate of a hydrocarbon contaminant is essential in designing technically efficient and cost-effective, plant-aided remedial strategies and in evaluating the effectiveness of a proposed phytoremediation scheme. The model presented can provide an insight into the selection and optimization of a specific strategy. | |
