Speaker
Description
The accurate and continuous monitoring of radon concentration in indoor spaces, such as educational and work environments, is crucial for public health and safety. This study, part of a PhD thesis on statistical and computational analyses of radon levels, addresses a critical challenge in the radiation protection community: the fragmentation of data and a lack of standardized collaboration frameworks. Our research leverages data collected from both active and passive radon measurement methods in a series of institutional buildings. The objective is to analyze the spatio-temporal distribution of radon concentration and to investigate the key factors influencing these levels.
Building on these findings, we propose a conceptual framework for an open data platform designed to facilitate improved data sharing, collaborative research, and real-time monitoring within the radiation protection community. The framework emphasizes data interoperability, semantic consistency, and adherence to FAIR principles (Findable, Accessible, Interoperable, and Reusable), which are essential for creating a reliable and reusable repository of environmental data.
Our preliminary results demonstrate a significant variation in radon levels, highlighting the necessity of long-term monitoring and detailed analysis. The statistical models developed in this study can identify high-risk areas and inform more effective mitigation strategies. This research suggests that a shift towards open data frameworks can enhance data quality, accelerate research, and ultimately lead to more effective public health policies in radiation protection. The proposed framework serves as a model for future collaborative projects, ensuring that vital environmental data is accessible and actionable for a global network of researchers and practitioners.