Sustainable Management of Toxic Organic Pollutants: Application of AI/ML and Omics Based Approaches for Monitoring, Risk Assessment, and Biotransformation

Environmental microbiology and microbial data science are increasingly important for understanding the effects of persistent pollutants on ecosystems, agriculture, and human health. Pesticides, heavy metals, polycyclic aromatic hydrocarbons (PAHs), and microplastics can persist, interact, and accumulate in environmental matrices, creating complex and poorly understood risks. Their heterogeneous distribution, transformation into potentially toxic metabolites, and interactions with microbial communities challenge conventional approaches to detection, monitoring, risk assessment, and remediation. Although recent advances in artificial intelligence (AI), machine learning (ML), metagenomics, transcriptomics, proteomics, and metabolomics have improved the identification of pollutant-responsive microorganisms, genes, pathways, and enzymes, important gaps remain in model interpretability, data integration, cross-environment validation, and translation from laboratory studies to field applications. A stronger integration of computational methods with microbial ecology and environmental biotechnology is needed to enable reliable prediction of pollutant fate, toxicity, biodegradation potential, and ecosystem recovery.This Research Topic aims to advance data-driven approaches for investigating microbial responses to environmental pollutants and developing sustainable strategies for their monitoring, risk assessment, and removal. It will explore how AI and ML can support early detection, pollutant fate modelling, toxicity prediction, ecological forecasting, and environmental decision-making, while integrated omics can reveal the molecular mechanisms underlying microbial adaptation, transformation, and degradation. The Research Topic will also examine how computationally guided discovery can inform the design of microbial consortia, enzyme-based treatments, and bioremediation systems. Contributions should help clarify how microbial data can be made more reproducible, interpretable, scalable, and applicable across diverse environmental settings.To gather further insights into the use of microbial data science for understanding and mitigating pollutant contamination in soil, water, sediments, and associated ecosystems, we welcome articles addressing, but not limited to, the following themes:o AI and ML applications in pollutant detection, classification, monitoring, and fate predictiono Microbiome and multi-omics analyses of pollutant-exposed environmentso Discovery of pollutant-degrading microorganisms, genes, enzymes, and metabolic pathwayso Computational reconstruction and modelling of microbial degradation networkso Prediction of pollutant toxicity, bioavailability, transformation products, and ecological risko Microbial responses to pesticides, heavy metals, PAHs, microplastics, and pollutant mixtureso Data-driven design and optimization of microbial consortia for bioremediationo Digital tools for environmental surveillance, biosensing, and decision-support systemso Integration, standardization, visualization, and interpretation of environmental microbiology datasetso Field validation, scalability, and real-world implementation of AI–omics-based remediation strategieso Effects of climate change, land-use change, and environmental stressors on microbial pollutant transformationo Reproducible and explainable computational methods for environmental microbiology Topic editors: Ashwani Kumar, Muneer Ahmad Malla Source: https://www.frontiersin.org/research-topics/84532/sustainable-management-of-toxic-organic-pollutants-application-of-aiml-and-omics-based-approaches-for-monitoring-risk-assessment-and-biotransformation