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

Brabec, Marek; Jílek, K.

doi:10.1016/j.jenvrad.2007.05.006

Cited by 17 publications

(9 citation statements)

References 11 publications

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“…with K t = 1 − γ t , whose form is similar to that of the basic equation used in computation of radon entry rate and ventilation rate with trace gas experiments, Brabec and Jílek (2007). (8) and (7) have a number of physical interpretations.…”

Section: Descriptive Modelmentioning

confidence: 99%

“…For instance, K t is the ventilation rate (VR, measured in h −1 ), whose estimation is of interest in many contexts, including civil engineering, radon safety management, etc. Often, its calculations are based on rather unrealistically strong assumptions (see Brabec and Jílek (2007) for discussion). Here, we can see that full (time-varying) estimation of VR is one of the byproducts of the model (7).…”

Section: Descriptive Modelmentioning

confidence: 99%

“…Although we were not able to prove formal significance of β 2t , we will keep it in the model as a precaution. Note that estimates obtained from (7) via local smoothing described in Section 3.2 can also be viewed as approximations to the smoother (i.e., of E(α t |Y ), E(β 1t |Y ), E(β 2t |Y ), E(γ t |Y )) obtained from a full state-space model: with normal joint distribution of η 1t , η 2t , η 3t and appropriate initial conditions, see Brabec and Jílek (2007). Figure 5 shows the fit of model (7).…”

Section: Descriptive Modelmentioning

confidence: 99%

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Dynamical model for indoor radon concentration monitoring

Brabec

Jílek

2009

Environmetrics

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SUMMARYIn this paper, we will deal with interesting example of natural risk modeling -namely of indoor radon concentration, needed for proper exposure assessment and appreciation of quality of preventive measures taken. First, we will illustrate, how the traditional view based on fixed (i.e., time-invariant) coefficient models can be misleading. Then we formulate a flexible (nonparametric) regression dynamic model offering a doable alternative. Situation is not entirely standard here, as the smoothed and weight-giving variables are different. Nevertheless, estimation for our local regression formulation is easily doable even for rather large data via extension of standard local smoothing that we describe. We illustrate on data coming from a rare intensive measurement campaign in an occupied house with simultaneous measurements taken in different rooms. Our model has nice physical interpretation of its parts. We also illustrate how even such a simple model can produce behavior that throws some light on radon experts' discussions about natural radon concentration circadial movement phase. This is because our model acts as a linear but time-varying filter that can change not only amplitude but also phase between different rooms of the same house, suggesting that there might not be a universal phase of (e.g., daily) radon variation. Instead, the phase might depend on how close a particular indoor location is to the radon sources. Finally, we present some ideas about how our model can be expanded in future to cover more complicated situations and settings.

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Section: Descriptive Modelmentioning

confidence: 99%

Section: Descriptive Modelmentioning

confidence: 99%

Section: Descriptive Modelmentioning

confidence: 99%

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Dynamical model for indoor radon concentration monitoring

Brabec

Jílek

2009

Environmetrics

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“…Recently, a method has been published that can provide continuous measurements of AERs in buildings [19] and, to our knowledge, is the first such method proposed in the literature. The method used state-space dynamic modeling techniques and Kalman filtering to estimate the radon entry rate into an unoccupied house.…”

Section: Introductionmentioning

confidence: 99%

Air exchange rates from atmospheric CO2 daily cycle

Carrilho

Mateus

Batterman

et al. 2015

Energy and Buildings

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We propose a new approach for measuring ventilation air exchange rates (AERs). The method belongs to the class of tracer gas techniques, but is formulated in the light of systems theory and signal processing. Unlike conventional CO2 based methods that assume the outdoor ambient CO2 concentration is constant, the proposed method recognizes that photosynthesis and respiration cycle of plants and processes associated with fuel combustion produce daily, quasi-periodic, variations in the ambient CO2 concentrations. These daily variations, which are within the detection range of existing monitoring equipment, are utilized for estimating ventilation rates without the need of a source of CO2 in the building. Using a naturally-ventilated residential apartment, AERs obtained using the new method compared favorably (within 10%) to those obtained using the conventional CO2 decay fitting technique. The new method has the advantages that no tracer gas injection is needed, and high time resolution results are obtained.

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“…The linear state space model postulates that an observed time series is a linear function of a state vector and the law of motion for the state vector is first-order vector autoregression (AR) (Simon and Chia 2002). In recent years, KF has become a very powerful, intelligent and computational tool widely used in signal processing applications such as noise reduction (Brailean et al 1995, Fujimoto and Ariki 2000, Evensen 2003, Brabec and Jílek 2007. The main advantage of this approach is designing an efficient filter to control noisy systems.…”

Section: Introductionmentioning

confidence: 99%

Noise Reduction in Radon Monitoring Data Using Kalman Filter and Application of Results in Earthquake Precursory Process Research

Namvaran

Negarestani

2015

Acta Geophys.

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Monitoring the concentration of radon gas is an established method for geophysical analyses and research, particularly in earthquake studies. A continuous radon monitoring station was implemented in Jooshan hotspring, Kerman province, south east Iran. The location was carefully chosen as a widely reported earthquake-prone zone. A common issue during monitoring of radon gas concentration is the possibility of noise disturbance by different environmental and instrumental parameters. A systematic mathematical analysis aiming at reducing such noises from data is reported here; for the first time, the Kalman filter (KF) has been used for radon gas concentration monitoring. The filtering is incorporated based on several seismic parameters of the area under study. A novel anomaly defined as "radon concentration spike crossing" is also introduced and successfully used in the study. Furthermore, for the first time, a mathematical pattern of a relationship between the radius of potential precursory phenomena and the distance between epicenter and the monitoring station is reported and statistically analyzed.

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

Cited by 17 publications

References 11 publications

Dynamical model for indoor radon concentration monitoring

Dynamical model for indoor radon concentration monitoring

Air exchange rates from atmospheric CO2 daily cycle

Noise Reduction in Radon Monitoring Data Using Kalman Filter and Application of Results in Earthquake Precursory Process Research

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