Advanced Radioanalytical and Hybrid Mitigation Techniques for Detection and Control of Radioactive Contamination in Marine Environment

Radioactive contamination of marine environments poses long-term ecological and radiological risks due to the persistence and bioaccumulation of radionuclides such as cesium-137, strontium-90, and iodine-131. Accurate detection and effective mitigation of these radionuclides remain challenging because conventional monitoring approaches often lack spatial resolution, real-time capability, and scalability. In this study, an integrated framework combining advanced radioanalytical detection techniques with hybrid mitigation strategies is presented for the assessment and reduction of marine radioactive pollution. High-resolution in-situ gamma spectrometry, supported by remote sensing data and machine learning–assisted predictive modeling, was employed to enhance radionuclide detection and spatiotemporal contamination forecasting. The proposed detection framework improved identification accuracy by approximately 30% and achieved a prediction accuracy of 87.8% for radionuclide dispersion trends. For mitigation, nanomaterial-based filtration systems demonstrated a 55% reduction in strontium-90 concentration, while biologically driven remediation using Chlorella vulgaris and Deinococcus radiodurans resulted in a 20% reduction in cesium-137 and iodine-131 levels over a six-month period. The combined application of radioanalytical monitoring, data-driven modeling, and hybrid mitigation techniques provides a scalable and scientifically robust approach for managing radioactive contamination in marine systems. The proposed framework aligns with nuclear environmental safety objectives and offers a foundation for future advancements through radiochemical optimization, genetic engineering, and nanotechnology-assisted remediation.