Atmospheric Radionuclides: Physical Behavior, Exposure Assessment, Spectroscopic Surveillance, and Emerging AI-Driven Monitoring Approaches

Abstract

Atmospheric radionuclides are transported through the environment as gases or particle-associated contaminants, making their behavior closely dependent on aerosol properties and atmospheric conditions. Understanding their movement, deposition, persistence, and potential exposure pathways therefore requires integration of aerosol physics with reliable environmental surveillance. This review discusses the major physical principles governing atmospheric radionuclide dynamics, with emphasis on particle size, transport, deposition, resuspension, and interactions with radiation and light. Current exposure-assessment approaches based on environmental sampling and laboratory measurements are examined alongside emerging spectroscopic and remote sensing (SRS) technologies for atmospheric and terrestrial monitoring. Particular attention is given to the growing role of artificial intelligence and machine learning in interpreting complex environmental datasets and supporting short-term forecasting. Recent PM₁₀ research demonstrates that XGBoost, CatBoost, artificial neural networks, LSTM, Bi-LSTM, and ensemble models can integrate air-quality and meteorological data to provide location-specific predictions. Although these approaches were developed for particulate air pollution rather than radionuclides, their data-driven framework provides a useful direction for future radionuclide surveillance. Integrating conventional measurements with SRS, sensor networks, and AI-assisted prediction could improve spatial coverage, early detection, exposure assessment, and environmental decision-making.

Keywords: Atmospheric radionuclides; Aerosols; Exposure assessment; Spectroscopic remote sensing; Machine learning; Environmental surveillance

How to cite

Punna SK, Pusuluru S, Subburao S, et al. (2026). Atmospheric Radionuclides: Physical Behavior, Exposure Assessment, Spectroscopic Surveillance, and Emerging AI-Driven Monitoring Approaches. American Journal of Sustainable Science and Technology, 1(1), Article 2. https://aasrtresearch.org/articles/ajsst-2026-002.html

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