Investigating Biodegradation of 1,4-Dioxane by Groundwater and Soil Microbiomes: Insights into Microbial Ecology and Process Prediction

Although microorganisms play significant roles in bioremediation, their contributions to long-term site characteristics during and after active treatment need to be fully elucidated. This study described microbial ecology dynamics in 1,4-dioxane- and chlorinated solvents-contaminated groundwater in laboratory microcosms. Bioaugmented Pseudonocardia dioxanivorans CB1190 improved 1,4-dioxane removal, with increased carbohydrate and amino acid metabolism, but was eventually outcompeted by native microbes. The original microbiomes were perturbed and divergent but tended to be similar over time. Dechlorinating bacteria coexisted in the same niche, whereas CB1190 had more negative interactions in the shared niche. Multiple regression and classification machine learning models were built by using microbial taxa to predict the degradation process; the ensemble regression model provided most accurate prediction of 1,4-dioxane concentrations (R-2 = 0.81 +/- 0.17). Among the classification models, the support vector machine performed the best in differentiating the contamination levels (accuracy at 0.67 +/- 0.07, kappa at 0.56 +/- 0.10). The ensemble model predicted the 1,4-dioxane concentrations and relative duration of contamination with independent microbial datasets from a field study, and the results aligned with the geographic and hydrological information from monitoring wells. This study introduces the application of machine learning in microbiome-based diagnostics for groundwater remediation and evaluation, providing valuable methods for future research and practice.