Approaches for Emerging Contaminants: Toward Intelligent Omics-Enabled Monitoring and Mitigation

Emerging contaminants exhibit complex environmental behavior influenced by physicochemical conditions, microbial interactions, and transformation processes, often contributing to the evolution and dissemination of antibacterial, antimicrobial, and antiviral resistance, with implications for environmental and human health. Traditional monitoring approaches based on discrete sampling and laboratory analysis are insufficient to capture these dynamic interactions and the resulting patterns of environmental exposure. Advances in nanotechnology and biosensing have enabled highly sensitive and selective detection systems, while artificial intelligence and machine learning facilitate advanced data interpretation and predictive modelling. Physics-informed modelling integrates mechanistic transport and reaction processes with data-driven insights, enhancing predictive reliability. Concurrently, omics-based approaches, including genomics, proteomics, metabolomics, and microbiomics, provide in-depth understanding of microbial adaptation, resistance evolution, biological responses, and ecological impacts. However, these developments are often fragmented, necessitating integrated frameworks for comprehensive environmental assessment, exposome characterization, and public health relevance.

Emerging contaminants, including pharmaceuticals, personal care products, endocrine disruptors, and industrial chemicals, pose significant risks to environmental and human health due to their persistence, bioaccumulation, and complex transformation pathways. These challenges are further intensified by the growing prevalence of antibacterial resistance, antimicrobial resistance, and antiviral resistance, which compromise treatment efficacy and public health outcomes. Conventional monitoring and mitigation strategies remain inadequate to address low concentration dynamics, mixture toxicity, spatiotemporal variability, and cumulative exposure patterns. This Research Topic aims to develop integrated and intelligent frameworks that combine advanced sensing technologies, data-driven modelling, physics-informed approaches, and omics-based analyses to improve detection, exposure assessment, prediction, and mitigation of emerging contaminants and resistance drivers. By bridging mechanistic understanding with computational intelligence, the goal is to enable adaptive, real-time, and scalable environmental monitoring systems that support exposome research, sustainable risk management, and protection of human health.

This Research Topic invites interdisciplinary contributions addressing emerging contaminants and resistance mechanisms through integrated sensing, modelling, and biological analysis, with a focus on environmental health and exposome research. Topics include:

- Data-driven and machine learning models for contaminant occurrence, exposure patterns, and resistance prediction.

- Physics-informed models for transport, fate, transformation processes, and exposure assessment.

- Omics-based investigations of microbial dynamics, resistance evolution, biological responses, and host-environment interactions.

- Hybrid systems integrating monitoring with treatment and mitigation strategies to reduce exposure and health risks.

Particular emphasis is placed on studies linking contaminant exposure to antibacterial, antimicrobial, and antiviral resistance propagation, as well as to environmental and human health outcomes. Contributions focusing on digital monitoring, exposome-informed assessment, intelligent systems, and adaptive environmental health management are encouraged. We welcome original research articles, reviews, perspectives, and case studies that advance scalable, sustainable, and system-level solutions for monitoring and mitigating emerging contaminants and resistance in environmental systems.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy Brief
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Emerging contaminants, Environmental sensing, Data-driven modelling, Omics-based analysis, Physics-informed modelling, Exposome, Environmental health, Antimicrobial resistance

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.