Design and Development of a New Methodology Based on Expert Systems Applied to the Prevention of Indoor Radon Gas Exposition Risks

Jorge Cerqueiro-Pequeño, Alberto Comesaña-Campos, Manuel Casal-Guisande and José-Benito Bouza-Rodríguez
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Jorge Cerqueiro-Pequeño: Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain
Alberto Comesaña-Campos: Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain
Manuel Casal-Guisande: Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain
José-Benito Bouza-Rodríguez: Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain

IJERPH, 2020, vol. 18, issue 1, 1-32

Abstract: Exposure to high concentration levels of radon gas constitutes a major health hazard, being nowadays the second-leading cause of lung cancer after smoking. Facing this situation, the last years have seen a clear trend towards the search for methodologies that allow an efficient prevention of the potential risks derived from the presence of harmful radon gas concentration levels in buildings. With that, it is intended to establish preventive and corrective actions that might help to reduce the impact of radon exposure on people, especially in places where workers and external users must stay for long periods of time, as it may be the case of healthcare buildings. In this paper, a new methodology is developed and applied to the prevention of the risks derived from the exposure to radon gas in indoor spaces. Such methodology is grounded in the concurrent use of expert systems and regression trees that allows producing a diagram with recommendations associated to the exposure risk. The presented methodology has been implemented by means of a software application that supports the definition of the expert systems and the regression algorithm. Finally, after proving its applicability with a case study and discussing its contributions, it may be claimed that the benefits of the new methodology might lead on to an innovation in this field of study.

Keywords: radon; expert systems; decision support systems; regression tree; risk; design science research (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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