Logistic regression model for detecting radon prone areas in Ireland.

Area of Science:

• Environmental Science
• Geology
• Public Health

Background:

• Radon is a naturally occurring radioactive gas that poses a significant health risk.
• Accurate radon risk assessment is crucial for public health protection.
• Previous radon risk maps lacked high spatial resolution.

Purpose of the Study:

• To develop a high spatial resolution radon risk map for the Republic of Ireland.
• To identify areas with a high probability of indoor radon concentrations exceeding the national reference level.
• To provide a tool for targeted public health interventions and radon awareness campaigns.

Main Methods:

• Utilized a dataset of 31,910 indoor radon measurements.
• Integrated geological data including Bedrock Geology, Quaternary Geology, soil permeability, and aquifer type.
• Employed logistic regression to predict the probability of indoor radon levels exceeding 200 Bqm⁻³.

Main Results:

• Identified three main radon risk categories: High (HR), Medium (MR), and Low (LR) for the Republic of Ireland.
• Predicted probabilities of exceeding 200 Bqm⁻³ were 19% (HR), 8% (MR), and 3% (LR).
• Estimated that approximately 460,000 people (10% of the population) are in areas with radon concentrations above 200 Bqm⁻³.

Conclusions:

• The developed radon risk map provides a high spatial resolution utility for Ireland.
• Results enable customized radon awareness information to be targeted at specific geographic areas.
• The map serves as a valuable tool for public health authorities to mitigate radon-related risks.