Use of linear regression models to determine influence factors on the concentration levels of radon in occupied houses

Abstract

This work is part of the analysis of the effects of constructional energy-saving measures to radon concentration levels in dwellings performed on behalf of the German Federal Office for Radiation Protection. In parallel to radon measurements for five buildings, both meteorological data outside the buildings and the indoor climate factors were recorded. In order to access effects of inhabited buildings, the amount of carbon dioxide (CO 2 ) was measured. For a statistical linear regression model, the data of one object was chosen as an example. Three dummy variables were extracted Int. J. Mod. Phys. Conf. Ser. 2016.44. Downloaded from www.worldscientific.com by 13.66.222.141 on 04/29/19. Re-use and distribution is strictly not permitted, except for Open Access articles. J. Buermeyer et al. 1660223-2from the process of the CO 2 concentration to provide information on the usage and ventilation of the room. The analysis revealed a highly autoregressive model for the radon concentration with additional influence by the natural environmental factors. The autoregression implies a strong dependency on a radon source since it reflects a backward dependency in time. At this point of the investigation, it cannot be determined whether the influence by outside factors affects the source of radon or the habitant's ventilation behavior resulting in variation of the occurring concentration levels. In any case, the regression analysis might provide further information that would help to distinguish these effects. In the next step, the influence factors will be weighted according to their impact on the concentration levels. This might lead to a model that enables the prediction of radon concentration levels based on the measurement of CO 2 in combination with environmental parameters, as well as the development of advices for ventilation.