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Electrostatic Method to Remove Particulate Organic Matter from Soil
Published on: February 10, 2021
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Modelling and Optimizing Pyrene Removal from the Soil by Phytoremediation using Response Surface Methodology,
Farzaneh Mohammadi1, Mohammad Reza Samaei2, Abooalfazl Azhdarpoor2
1Department of Environmental Health Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
Chemosphere
|August 10, 2019
Summary
This study optimized pyrene removal from soil using Sorghum bicolor plants, indole acetic acid (IAA), and Pseudomonas aeruginosa bacteria. The Artificial Neural Network (ANN) model demonstrated superior prediction accuracy over Response Surface Methodology (RSM).
Area of Science:
- Environmental Science
- Bioremediation
- Soil Science
Background:
- Soil contamination by polycyclic aromatic hydrocarbons (PAHs) like pyrene poses environmental risks.
- Phytoremediation offers a sustainable approach for removing soil contaminants.
- Optimizing phytoremediation requires understanding plant-bacterial interactions and environmental factors.
Purpose of the Study:
- To model and optimize pyrene removal from contaminated soil using phytoremediation.
- To evaluate the efficacy of indole acetic acid (IAA) and Pseudomonas aeruginosa bacteria in enhancing pyrene removal by Sorghum bicolor.
- To compare the predictive performance of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) models.
Main Methods:
- Box-Behnken Design (BBD) for experimental design.
- Response Surface Methodology (RSM) for modeling and optimization.
- Artificial Neural Network (ANN) with a Feed-Forward Back-Propagation Neural Network (FFBPNN) architecture and Levenberg-Marquardt (LM) algorithm.
- Genetic Algorithm (GA) for determining optimal conditions.
Main Results:
- A non-linear second-order RSM model showed good agreement with experimental data.
- The ANN model, with eight hidden neurons, achieved high prediction accuracy (high R, low MSE, low MAE).
- Indole acetic acid (IAA) and Pseudomonas aeruginosa significantly enhanced pyrene removal efficiency by Sorghum bicolor.
Conclusions:
- The combined application of IAA and Pseudomonas aeruginosa with Sorghum bicolor is effective for pyrene phytoremediation.
- ANN models provide superior predictive capabilities for phytoremediation optimization compared to RSM.
- Optimized phytoremediation strategies can effectively mitigate pyrene soil contamination.
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