A metagenome-derived artificial intelligence modeling framework advances the predictive diagnosis and interpretation of petroleum-polluted groundwater

Groundwater (GW) quality monitoring is vital for sustainable water resource management. The present study introduced a metagenome-derived machine learning (ML) model aimed at enhancing the predictive understanding and diagnostic interpretation of GW pollution associated with petroleum. In this framework, taxonomic and metabolic profiles derived from GW metagenomes were combined for use as the input dataset. By employing strategies that optimized data integration, model selection, and parameter tuning, we achieved a significant increase in diagnostic accuracy for petroleum-polluted GW. Explanatory artificial intelligence techniques identified petroleum degradation pathways and Rhodocyclaceae as strong predictors of a pollution diagnosis. Metagenomic analysis corroborated the presence of gene operons encoding aminobenzoate and xylene biodegradation within the de novo assembled genome of Rhodocyclaceae. Our genome-centric metagenomic analysis thus clarified the ecological interactions associated with microbiomes in breaking down petroleum contaminants, validating the ML-based diagnostic results. This metagenome-derived ML framework not only enhances the predictive diagnosis of petroleum pollution but also offers interpretable insights into the interaction between microbiomes and petroleum. The proposed ML framework demonstrates great promise for use as a science-based strategy for the on-site monitoring and remediation of GW pollution Petroleum contaminants, a mixture of oil-related hydrocarbon compounds, pose a prioritized health hazard. They can exhibit toxicity, mutagenicity, and/or carcinogenicity at the levels relevant in many subsurface environments, presenting both environmental and human health risks. The present study introduces a metagenome-derived artificial intelligence (AI) modeling framework for monitoring petroleum-contaminated groundwater, significantly improving the predictive accuracy of current environmental monitoring methodologies. This research demonstrates a complementary use of advanced metagenome bioinformatics and explainable AI techniques to not only validate the AI predictions but also enhance their interpretation. This encourages the broader application of AI approaches in environmental monitoring and bioremediation practices.