Remediation of Phenanthrene Contaminated Soil by Persulfate Coupled with Pseudomonas Aeruginosa Gz7 Based on Oxidation Prediction Model

Abstract

Chemical oxidation coupled with microbial remediation has attracted widespread attention for its high efficiency and environmental friendliness in removing PAHs. Among them, precise evaluation of the feasible oxidants concentration of PAHs contaminated soil is the key to achieving the goal of soil functional ecological remediation. ln this study, the phenanthrene (PHE) was used as the target pollutant, and Fe2+ activated persulfate (PS) was used to repair four typical soils. Linear regression analysis identified important influencing factors in remediation: PS dosage and soil PHE content for PHE degradation; Fe2+ dosage, Hydrolyzed Nitrogen (HN) and Available Phosphorus (AP) for PS decomposition. A comprehensive model of "soil characteristics - oxidation conditions - remediation effect" with great prediction function was constructed. On the basis of model identification, Pseudomonas aeruginosa GZ7 with efficient degradation ability was further applied to the coupled remediation of PHE-contaminated soil. The results showed that the optimal PS dose of coupled remediation was 0.75%; the response relationship between soil physical, chemical and biological indicators at the intermediate interface to oxidation conditions were analyzed. Through the microbial diversity sequencing method, the coupled remediation effect was clarified. pH, Electrical Conductivity (EC), Total Nitrogen (TN), HN, and Oxidation Reduction Potential (ORP) have great impacts on the structural composition of the phytoedaphon. Oxidation greatly destroyed the expression of related functional genes such as microbial metabolism, growth and oxidoreductase. Introduction of Pseudomonas aeruginosa GZ7 stimulating the relative abundance of Cohnella, Enterobacter, Paenibacillus and Bacillus, which can promote material metabolism and energy transformation in the remediation process.

Keywords: Persulfate, PAHs, Soil properties, Multiple linear regression model, Chemical oxidation coupled bioremediation