pyDOSEIA: A Python Package for Radiological Impact Assessment during Long-term or Accidental Atmospheric Releases.

Area of Science:

• Environmental Science
• Nuclear Science
• Computational Science

Background:

• Accurate radiological impact assessment is crucial for nuclear and radiological accident preparedness.
• Existing tools may lack comprehensive features for diverse exposure pathways and specific demographic groups.

Purpose of the Study:

• To introduce pyDOSEIA, a Python package for meteorological data processing and radiological dose assessment.
• To provide a user-friendly, open-source tool for estimating radiation doses from various exposure scenarios.

Main Methods:

• Utilizes the Gaussian Plume Model and adheres to International Atomic Energy Agency (IAEA) and Atomic Energy Regulatory Board (AERB) guidelines.
• Incorporates age-specific, distance-specific, and radionuclide-specific dose computations.
• Features parallel processing for efficient analysis of large datasets and up-to-date dose conversion factors.

Main Results:

• pyDOSEIA enables comprehensive radiation dose estimation across multiple exposure pathways (inhalation, ingestion, groundshine, submersion, plumeshine).
• The package supports both short-term and long-term exposure assessments.
• Facilitates applications in machine learning and deep learning for radiation risk analysis.

Conclusions:

• pyDOSEIA offers a robust and accessible solution for radiological risk assessment.
• Empowers researchers, practitioners, and policymakers in decision-making and emergency preparedness.
• The open-source nature promotes collaboration and further development in the field.