Combined Remediation of Petroleum - Contaminated Soil by Immobilized Microbial Communities and Sorghum sudanense and Associated Soil Microbial Analysis

This project employed pot experiments to investigate the effectiveness of combined remediation of petroleum contaminated soil using immobilized microbial communities and Sorghum sudanense. The petroleum contaminated soil had an oil concentration of 5.0 g/kg, with 2.0 kg soil in each pot. Five treatment groups were set up: the control group (CK), petroleum - contaminated soil group (OP), immobilized microbial community group (IM), phytoremediation group with Sorghum sudanense (PR), and the combined group of immobilized microbial communities and Sorghum sudanense (CP). In the IM and CP groups, immobilized microbial communities were evenly mixed into the petroleum contaminated soil. In the PR and CP groups, 15 - 30 Sorghum sudanense seeds were sown at a depth of 1.5 cm, and thinned to leave 10 plants per pot after emergence. An appropriate amount of water was added every 3 days to keep the soil water content at 40%. Soil total DNA was extracted after removing impurities from each group's soil and then sent to Shanghai Majorbio Bio-Pharmaceutical Technology Co., Ltd. for high throughput sequencing. PCR amplification was carried out using barcode-tagged specific primers for the designated region. Sequencing was performed on an Illumina MiSeq 3000 high - throughput sequencer. Based on the obtained ASV representative sequences and abundance information, alpha-diversity was evaluated by calculating Chao1, Sobs, Shannon, ACE, and Simpson indices. Principal coordinate analysis (PCoA) using R language (version 3.3.1) was conducted to assess the inter - group differences in Beta-diversity. The composition of soil microbial communities was evaluated through Venn and Circos analyses. The PICRUSt2 software for prokaryotic taxonomic functional annotation was utilized to predict the functions of soil bacteria. A network diagram was obtained by calculating the correlation index (Spearman correlation coefficient) of samples to predict the key bacterial communities under different treatment conditions.