pyDOSEIA

pyDOSEIA performs radiological impact assessment using the Gaussian Plume Model and following IAEA and AERB guidelines, enabling estimation of radiation doses from multiple exposure pathways with age-specific, distance-specific, and radionuclide-specific computations.

Key Features:

• Gaussian Plume Model: Employs the Gaussian Plume Model for atmospheric dispersion modeling in radiological scenarios.
• Multiple Exposure Pathways: Estimates radiation doses from inhalation, ingestion, groundshine, submersion, and plumeshine exposure pathways.
• Age-Specific Dose Calculations: Enables age-specific radiation dose computations for different population groups.
• Distance-Specific Dose Calculations: Enables distance-specific radiation dose computations for spatial assessment.
• Radionuclide-Specific Dose Calculations: Enables radionuclide-specific radiation dose computations for individual radionuclides.
• Exposure Duration Coverage: Provides calculations for both short-term and long-term exposures.
• Dose Conversion Factors: Leverages up-to-date dose conversion factors for dose estimation.
• Parallel Processing: Features parallel processing capabilities for rapid analysis of large datasets.
• Machine Learning Integration: Facilitates applications in machine learning and deep learning research.
• Meteorological Data Processing: Processes meteorological data for radiological impact assessment in diverse scenarios.

Scientific Applications:

• Nuclear Accident Assessment: Assesses radiological impacts during nuclear accidents with atmospheric releases.
• Radiological Accident Assessment: Assesses radiological impacts during radiological accidents with atmospheric releases.
• Long-term Release Assessment: Assesses radiological impacts during long-term atmospheric releases.
• Radiation Risk Assessment: Supports radiation risk assessment for emergency preparedness and decision making.

Methodology:

pyDOSEIA employs the Gaussian Plume Model, follows IAEA and AERB guidelines, and utilizes up-to-date dose conversion factors with parallel processing capabilities.