Topic: AI in Environmental Exposure and Health

Environmental exposure assessment and evaluation of associated health outcomes are increasingly challenged by complex exposures patterns, high-dimensional data, and heterogeneous population responses. Conventional analytical approaches often struggle to capture these complexities. In this context, Artificial Intelligence (AI) has emerged as a transformative tool, offering new opportunities to enhance exposure characterization, health outcome modeling, and risk assessment. This Special Issue of Journal of Environmental Exposure Assessment (JEEA) showcases cutting-edge research integrating AI with exposure science, epidemiological analysis, and environmental health risk assessment.

The topics span a broad range of AI-driven environmental exposure and health research, encompassing machine learning-based exposure prediction (for air, water, soil, chemical, biological, and occupational exposures) and hazardous substance identification; data mining of environmental health datasets (including sensor networks, satellite remote sensing data, exposome profiles, longitudinal cohort studies, and clinical/epidemiological data); AI-optimized risk stratification for vulnerable populations - such as children, the elderly, and pregnant women - along with acute and chronic health outcome modeling; and all AI-enabled applications in environmental exposure monitoring, hazard assessment, pollution source attribution, exposure-disease causal inference, and the ethical, transparent, and responsible use of AI in environmental health research.

By bridging AI innovation with environmental health practice, this Special Issue aims to advance methodological rigor, promote translational research, and provide actionable insights for researchers, practitioners, and policymakers. We invite readers to explore these interdisciplinary contributions and to foster collaboration in addressing global environmental health challenges in the AI era.

Artificial intelligence, computational toxicology, AI models,  environmental exposure, chemicals, exposome analysis, health risk, vulnerable populations