The explosive data growth in the current information age requires consistent new methodologies harmonized with the new IoT era for data analysis in a space-time context. Moreover, intuitive data visualization is a central feature in exploring, interpreting, and extracting specific insights for subsequent numerical data representation. This integrated process is normally based on the definition of relevant metrics and specific performance indicators, both computed upon continuous real-time data, considering the specificities of a particular application case for data validation. This article presents an IoT-oriented evaluation tool for Radon Risk Management (RRM), based on the design of a simple and intuitive Indoor Radon Risk Exposure Indicator (IRREI), specifically tailored to be used as a decision-making aid tool for building owners, building designers, and buildings managers, or simply as an alert flag for the problem awareness of ordinary citizens. The proposed methodology was designed for graphic representation aligned with the requirements of the current IoT age, i.e., the methodology is robust enough for continuous data collection with specific Spatio-temporal attributes and, therefore, a set of adequate Radon risk-related metrics can be extracted and proposed. Metrics are summarized considering the application case, taken as a case study for data validation, by including relevant variables to frame the study, such as the regulatory International Commission on Radiological Protection (ICRP) dosimetric limits, building occupancy (spatial dimension), and occupants' exposure periods (temporal dimension). This work has the following main contributions: (1) providing a historical perspective regarding RRM indicator evolution along time; (2) outlining both the formulation and the validation of the proposed IRREI indicator; (3) implementing an IoT-oriented methodology for an RRM indicator; and (4) a discussion on Radon risk public perception, undertaken based on the results obtained after assessment of the IRREI indicator by applying a screening questionnaire with a total of 873 valid answers.
Keywords: IoT; radon gas; risk analysis; risk communication; risk management; risk mitigation; risk public perception; visual analytics.