Soil-Bacterium Compatibility Model as a Decision-Making Tool for Soil Bioremediation.

Area of Science:

• Environmental Microbiology
• Soil Science
• Bioremediation Technologies

Background:

• Bioaugmentation offers a sustainable alternative for treating organic pollutant-contaminated soils.
• Intrinsic soil properties significantly influence the efficacy of bioaugmentation strategies.
• Predictive models are needed to optimize the selection of compatible soil-bacterium pairings.

Purpose of the Study:

• To assess the feasibility of using partial least-squares regression (PLSR) for predicting bioaugmentation success.
• To develop soil-bacterium compatibility models based on physico-chemical soil characteristics.
• To enhance the success rate of bioaugmentation in contaminated soil remediation.

Main Methods:

• Partial least-squares regression (PLSR) was employed to build predictive models.
• The survival and biodegradation activity of Novosphingobium sp. LH128 were measured across 20 different soil types.
• Soil characteristics were correlated with bacterial performance to identify key predictive variables.

Main Results:

• PLSR successfully predicted bacterial survival using 12 or fewer soil variables.
• PAH-degrading activity was predicted with 9 variables in soils where the bacteria survived.
• Compatibility models demonstrated the potential to forecast bioaugmentation outcomes.

Conclusions:

• PLSR is a viable tool for predicting bioaugmentation success based on soil properties.
• Developed soil-bacterium compatibility models can guide the selection of effective remediation strategies.
• A three-step approach using these models can improve decision-making for selecting compatible soils and microbes, increasing bioaugmentation efficacy.