A mathematical model for bioremediation of hydrocarbon-contaminated soils

Abstract

Bioremediation of hydrocarbons in soil is a highly complex process, involving a multiplicity of physical, chemical and biological phenomena. Therefore, it is extremely difficult to control and boost the bioremediation of these systems after an oil spill. A mathematical model was developed to assist in the prediction and decision-making regarding the in situ bioremediation of hydrocarbon-contaminated soils. The model considered the most relevant processes involved in the mass transfer and biodegradation of alkanes over time and along the depth of a flooded soil column. Aliphatic hydrocarbons were chosen since they are less water soluble than aromatics and account for 50–90% of the hydrocarbon fraction in several petroleum products. The effect of adding oxygen, nitrate, iron (III) or sulfate as electron acceptors was then simulated (bioremediation scenarios). Additionally, and to feed the model, batch assays were performed to obtain experimental data on hydrocarbon adsorption to soil particles (more than 60% of hydrocarbons tends to be adsorbed to soil particles), as well as hydrocarbon biodegradation rates in the presence of nitrate (0.114 d−1) and oxygen (0.587 d−1). The model indicates that saturated hydrocarbon removal occurs mainly with adsorption/desorption and transport processes in the upper layers of soil due to methanogenic biodegradation in deeper layers, since the other microbial processes are soon limited by the lack of electron acceptors. Simulation results show that higher initial electron acceptor concentrations led to higher hydrocarbon removal, confirming that the model is performing in accordance with the expected. Close to the surface (at 0.1 m depth), all scenarios predicted more than 83% hydrocarbon removal after two years of simulation. Soil re-aeration results in faster hydrocarbon removal (more than 20% after one year) and surfactants addition (around 15% after one year) may also accelerate soil bioremediation. With this model, the simultaneous contributions of the various physicochemical and biological processes are integrated, facilitating the simulation and comparison of different bioremediation scenarios. Therefore, it represents a useful support tool for the management of contaminated sites.