Leguminous trees have a potential for phytoremediation of oil-contaminated areas for its symbiotic association with nitrogen-fixing bacteria and arbuscular mycorrhizal fungi (AMF). This study selects leguminous tree associated with symbiotic microorganisms that have the potential to remediate petroleum-contaminated soil. Seven species of trees were tested: Acacia angustissima, Acacia auriculiformis, Acacia holosericea, Acacia mangium, Mimosa artemisiana, Mimosa caesalpiniifolia, and Samanea saman. They were inoculated with AMF mix and nitrogen-fixing bacteria mix and cultivated over five oil levels in soils, with five replicates. The decreasing of total petroleum hydrocarbons (TPH) values occurred especially with S. saman and its symbiotic microorganisms on highest oil soil contamination. Despite the large growth of A. angustissima and M. caesalpiniifolia on the highest level of oil, these species and its inoculated microorganisms did not reduce the soil TPH. Both plants were hydrocarbon tolerant but not able to remediate the polluted soil. In contrast were significative hydrocarbon decrease with M. artemisiana under high oil concentrations, but plant growth was severely affected. Results suggest that the ability of the plants to decrease the soil concentration of TPH is not directly related to its growth and adaptation to conditions of contamination, but the success of the association between plants and its symbionts that seem to play a critical role on remediation efficiency.

The adsorption of Pb(II) onto the surface of microwave-assisted activated carbon was studied through a two-layer feedforward neural network. The activated carbon was developed by microwave activation of Acacia auriculiformis scrap wood char. The prepared adsorbent was characterized by using Brunauer–Emmett–Teller (BET) surface area analyzer, scanning electron microscope (SEM), and X-ray difractometer. In the present study, the input variables for the proposed network were solution pH, contact time, initial adsorbate concentration, adsorbent dose and temperature, whereas the output variable was the percent Pb(II) removal. The network had been trained by using different algorithms and based on the lowest mean squared error (MSE) value and validation error, resilient backpropagation algorithm with 12 neurons in the hidden layer was selected for the present investigation. The tan sigmoid and purelin transfer function were used in the hidden and the output layers of the proposed network, respectively. The model predicted and experimental values of the percent Pb(II) removal were also compared and both the values were found to be in reasonable agreement with each other. The performance of the developed network was further improved by normalizing the experimental data set and it was found that after normalization, the MSE and validation error were reduced significantly. The sensitivity analysis was also performed to determine the most significant input parameter.