State-space dynamic model for estimation of radon entry rate, based on Kalman filtering.

To predict the radon concentration in a house environment and to understand the role of all factors affecting its behavior, it is necessary to recognize time variation in both air exchange rate and radon entry rate into a house. This paper describes a new approach to the separation of their effects, which effectively allows continuous estimation of both radon entry rate and air exchange rate from simultaneous tracer gas (carbon monoxide) and radon gas measurement data. It is based on a state-space statistical model which permits quick and efficient calculations. Underlying computations are based on (extended) Kalman filtering, whose practical software implementation is easy. Key property is the model's flexibility, so that it can be easily adjusted to handle various artificial regimens of both radon gas and CO gas level manipulation. After introducing the statistical model formally, its performance will be demonstrated on real data from measurements conducted in our experimental, naturally ventilated and unoccupied room. To verify our method, radon entry rate calculated via proposed statistical model was compared with its known reference value. The results from several days of measurement indicated fairly good agreement (up to 5% between reference value radon entry rate and its value calculated continuously via proposed method, in average). Measured radon concentration moved around the level approximately 600 Bq m(-3), whereas the range of air exchange rate was 0.3-0.8 (h(-1)).