In his doctoral work Ali Hosseini emphasises the importance of using fit-for-purpose models to improve decision-making during nuclear and radiological emergencies.
When a nuclear accident happens, it's crucial for authorities to make quick and effective decisions to protect people and the environment. To do this, they rely on so-called Decision Support Systems (DSSs), which use models to predict the spread of radioactive materials and their impact. However, these models need to be accurate and reliable to ensure the best possible outcomes.
“Models are essential tools that help predict how radioactive materials will move through the environment and how they might affect human health,” PhD candidate Ali Hosseini explains.
“In the case of a nuclear emergency, it is crucial for authorities to understand where the consequences might occur.”
Improving the underlying models
In his doctoral work, Hosseini has worked on improving one of the key foundations of decision support systems: the underlying models.
The FDMT (Food Chain and Dose Module for Terrestrial Pathways) model is a tool used in nuclear emergency management to predict how radioactive materials move through the environment and enter the food chain. It helps estimate the levels of radionuclides in food products and assess the potential radiation doses to humans following a nuclear incident.
“In my project , I focused on addressing the limitations of FDMT and enhancing its adaptability to various regions and scenarios, with a particular emphasis on Norwegian conditions,” Hosseini explains.
The aim was to help decision-makers use reliable data and models to make confident and effective choices during emergencies.
Tailored to Norwegian conditions
Hosseini adopted a dual approach, combining field experiments with in-depth model development.
“I conducted a gap analysis to identify key limitations in the FDMT model.”
Based on this he created a redesign that improved the model’s flexibility, usability, and alignment with specific regional needs.
“The FDMT is tailored to Norwegian conditions by incorporating locally relevant parameters and pathways, such as farming practices and climatological factors.”
Dynamics in boreal ecosystems
Hosseini conducted tracer experiments at two Norwegian research stations to study radionuclide dynamics in boreal ecosystems. These experiments provided insights into how site-specific and climatological factors affect the foliar uptake of radioactive iodine by grass and its subsequent loss.
“There was significant variability in radionuclide interception by grass and differences in weathering rates between coastal and inland sites,” he says.
New model framework
The new FDMT framework offers a flexible environment for developing, testing, and refining models.
“It facilitates sensitivity and uncertainty analyses, enabling authorities to better characterize risks and allocate resources efficiently,” Hosseini says.
By improving the precision and reliability of predictions, his research equips emergency response organizations with tools that enhance transparency and defensibility in decision-making.
The refined models allow for more accurate assessments of radiological risks to human health and aid in the design of mitigation strategies.
AI and machine learning
“Future research should leverage advancements in artificial intelligence and machine learning to enhance data management and modelling capabilities,” Hosseini says.
Integrating physical laws into deep learning models, known as physics-informed machine learning, can improve predictions.
“Additionally, developing computational structures to handle large volumes of heterogeneous data in real-time will be crucial for further improving decision-support systems,” he concludes.
Ali Hosseini will defend his PhD thesis "Modelling radionuclide transfer along terrestrial food-chains: Conceptual enhancements and region-specific adaptations” Friday the 13th of December 2024.
Trial lecture and public defense are open to all. Read more about those here.