Quantile regression and Bayesian cluster detection to identify radon prone areas.

Area of Science:

• Environmental Science
• Geology
• Public Health

Background:

• Indoor radon concentrations are influenced by both geological factors and specific dwelling characteristics.
• Understanding these factors is crucial for accurate radon risk assessment and mitigation.

Purpose of the Study:

• To develop and apply a combined approach for delineating radon prone areas.
• To investigate the impact of building covariates on indoor radon levels.
• To normalize indoor radon measures to reflect geogenic radon potential.

Main Methods:

• Stepwise analysis incorporating Bayesian spatial quantile regression to assess building covariates.
• Development of normalized radon measures by accounting for building-specific factors.
• Application of Bayesian models for spatial cluster detection to identify radon prone areas.

Main Results:

• Identified significant building-specific factors influencing indoor radon concentrations.
• Successfully normalized indoor radon measures, isolating the geogenic component.
• Delineated radon prone areas based on normalized measures and spatial cluster detection.

Conclusions:

• The combined approach effectively distinguishes between radon influenced by geology and building factors.
• Normalized radon measures provide a reliable indicator of geogenic radon potential.
• This methodology enhances the accuracy of identifying and mapping radon prone areas for targeted interventions.