AI-Driven Spatiotemporal Modeling of Environmental Radiation Exposure and Respiratory Health Risk in Vulnerable Populations
Keywords:
spatiotemporal modeling, artificial intelligence, environmental radiation, respiratory health, vulnerable populations, health equity, system architecture, federated learning, fairness, governanceAbstract
Environmental radiation originating from both natural and anthropogenic sources constitutes a persistent and inequitably distributed health threat. Vulnerable populations, including children, the elderly, and those with pre-existing respiratory conditions, often experience disproportionately high exposure due to residential proximity to contamination, substandard housing, or limited access to protective infrastructures. The complex interplay between spatiotemporal radiation dispersion, meteorological factors, and population mobility requires computational frameworks that surpass conventional exposure assessment methods. This paper presents an interdisciplinary system-level analysis of artificial intelligence-driven spatiotemporal modeling for environmental radiation exposure and respiratory health risk, with a particular emphasis on structural architecture, data integration, and deployment governance. We examine how deep learning architectures, including convolutional long short-term memory networks, graph neural networks, and spatiotemporal transformers, can be orchestrated within a hybrid cloud-edge infrastructure to produce high-resolution exposure maps and health risk projections. The discussion extends to the challenges posed by multisource data heterogeneity, privacy constraints associated with health records, and the necessity of fairness-aware modeling to prevent algorithmic magnification of existing disparities. We articulate trade-offs between centralized and federated learning paradigms, real-time inference capabilities, and model interpretability in public health contexts. Robustness against environmental data drift, missing sensor readings, and adversarial reporting conditions is analyzed alongside sustainability considerations encompassing institutional capacity, funding models, and open-source ecosystems. Policy implications are evaluated through the lens of environmental justice frameworks, highlighting how AI-driven exposure surveillance can inform regulatory interventions, early warning systems, and urban planning efforts tailored to the protection of at-risk communities. By synthesizing architectural, computational, and ethical dimensions, the paper offers a comprehensive roadmap for the responsible design and deployment of spatiotemporal AI systems in environmental public health.
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