Journal of Environmental Radioactivity 136 (2014) 105e111
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Journal of Environmental Radioactivity journal homepage: www.elsevier.com/locate/jenvrad
Study of indoor radon distribution using measurements and CFD modeling Neetika Chauhan a, *, R.P. Chauhan a, M. Joshi b, T.K. Agarwal b, Praveen Aggarwal c, B.K. Sahoo b a b c
Department of Physics, National Institute of Technology, Kurukshetra 136119, India Radiological Physics and Advisory Division, Bhabha Atomic Research Centre, Mumbai 400 085, India Department of Civil Engineering, National Institute of Technology, Kurukshetra 136119, India
a r t i c l e i n f o
a b s t r a c t
Article history: Received 11 February 2014 Received in revised form 16 April 2014 Accepted 22 May 2014 Available online
Measurement and/or prediction of indoor radon (222Rn) concentration are important due to the impact of radon on indoor air quality and consequent inhalation hazard. In recent times, computational fluid dynamics (CFD) based modeling has become the cost effective replacement of experimental methods for the prediction and visualization of indoor pollutant distribution. The aim of this study is to implement CFD based modeling for studying indoor radon gas distribution. This study focuses on comparison of experimentally measured and CFD modeling predicted spatial distribution of radon concentration for a model test room. The key inputs for simulation viz. radon exhalation rate and ventilation rate were measured as a part of this study. Validation experiments were performed by measuring radon concentration at different locations of test room using active (continuous radon monitor) and passive (pin-hole dosimeters) techniques. Modeling predictions have been found to be reasonably matching with the measurement results. The validated model can be used to understand and study factors affecting indoor radon distribution for more realistic indoor environment. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Indoor radon Radon flux Ventilation rate Radon monitoring Computational fluid dynamics
1. Introduction Radon (222Rn) is naturally occurring radionuclide formed from alpha disintegration of radium (226Ra) in Uranium-238 decay series. Radon, thoron (220Rn) and their decay products contribute more than half of the background radiation (UNSCEAR, 2000). Being a noble gas, radon easily gets released from the source term to the pores (emanation) and subsequently from the pores to the outside environment (exhalation). In less or no ventilation (energy exchange) conditions, it gets built-up indoor and affect indoor air quality. Indoor radon exposure has been linked to lung cancer cases in several studies (WHO, 2009). Breathing for life time in a house where radon is at the action level of 200 Bq/m3 carries a 3e5% risk of fatal lung cancer (NRPB, 2000). Inhaled radon gas induces genotoxicity due to interaction of high energy alpha particles with biological tissue in the lungs leading to DNA damage (WHO, 2009). The complex mechanism of cancer arises as a result of two major genetic alterations, gene mutation and chromosomal aberrations (Land et al., 1983). Studies in mines, residential studies, animal * Corresponding author. E-mail address: [email protected] (N. Chauhan). http://dx.doi.org/10.1016/j.jenvrad.2014.05.020 0265-931X/© 2014 Elsevier Ltd. All rights reserved.
studies and radiobiology implicate indoor radon as a cause of lung cancer (BEIR, 1999). After the release from the walls and floor, radon gets distributed in indoor environment. Understanding its distribution is important to predict the spatial and temporal variations of levels which can ultimately be used for dose calculations and exposure control research. As the time spent indoor is large enough (particularly for house-wives and children); understanding, prediction and measurement of indoor radon distribution becomes quite important. Exhalation rate of radon from surfaces and room ventilation rate are two important factors affecting the indoor radon levels. An extensive experimental work has been carried out in order to investigate radon exhalation rates from different types of building materials (fired and un-fired brick, concrete, marble, granite, cement, gypsum, fly ash bricks, sand etc.) (Chen et al., 2010; Hassan et al., 2011; Keller et al., 2001; Kumar et al., 2014; Maged and Ashraf, 2005; Mahur et al., 2008; Petropoulos et al., 2001; Righi and Bruzzi, 2006). The radon exhalation rate may be different from the measured radon exhalation values when these building material are used in walls, floor and ceiling construction. This difference is due to dependence of diffusion length on the configuration (Sahoo et al., 2011). Some mathematical models are available
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in the literature for prediction of radon exhalation form the wall using radon exhalation rate of commonly used building materials (Nazaroff and Nero, 1988; Sahoo et al., 2011). A comparison of various models used for such purpose has been recently discussed in Kumar et al., 2014. However, there is too less experimental work available for direct measurement of radon exhalation from the room surfaces. In the present work, an accumulator is used for directly measuring radon wall exhalation rate. Ventilation rate has been measured in studies using radioactive krypton-85 (Samer et al., 2011), N2O or CO2 (Kittas et al., 1996), SF6 (Snell et al., 2003) as tracer gas. A few studies also reported use of radon as tracer gas that does not require any special safety measures compared to other tracer gases (Vasilyev and Zhukovsky, 2013). The present work also employs using indoor accumulated radon for estimation of ventilation rate. Traditionally, Indoor radon levels have been measured by passive method using dosimeters and active method using electronic devices for e.g. continuous radon monitor. Some mathematical models are also developed to estimate the indoor radon concentration (Jelle et al., 2011; Stoulos et al., 2003). In recent time, Computational Fluid Dynamics (CFD) has taken outstanding position for simulation of indoor radon problems. CFD solves the governing fluid equations and provides spatial and temporal field solution of variables such as pressure, temperature, energy density. It also provides velocity flow field and the dispersion pattern of indoor pollutant. Ability of commercially available CFD software to simulate a wide range of geometrical and environmental conditions is one of the appreciable features over its other existing tools. Many CFD based studies have been performed to develop the radon entry models to indoor form soil (Andersen et al., 2001; Loureiro, 1987; Wang and Ward, 1997, 2000, 2002). But limited CFD based studies have been performed to investigate indoor radon dispersion (Akbari et al., 2013; Urosevic et al., 2008; Zhuo et al., 2001). A major limitation of these studies however is lack of measurements which can help for both reduction of input parametric uncertainty and comparison of model predictions. The applicability of CFD for radon related research is still evolving and a lot needs to be done for applying this knowledge for mankind research. This work focuses on estimation of indoor radon distribution in a typical closed test room. The radon source term (radon exhalation from walls, floor and ceiling) and ventilation rate has been measured for this room. Measured radon source term and ventilation rate has been used as input in CFD code (Fluidyn MP-5.2.1) and simulations have been done taking room geometry. Pin-hole dosimeters (Sahoo et al., 2013) and scintillation radon monitor (SRM) (Gaware et al., 2011) have been used for the measurement of indoor radon in passive and active mode, respectively. These measurements were carried out at various locations so as to cover the room, spatially. This paper presents experimental measurements and simulations for the model room. The measured radon distribution (by passive and active means) in the room was compared with the simulation results.
Fig. 1. Room geometry with positions of dosimeters.
inlet (door 1) and other two were considered as outlet. Radon exhalation rate from the room surfaces (walls, floor and ceiling) was the only source term for the indoor radon build-up. 2.2. Radon exhalation rate measurement The surface exhalation rate of the radon from the room wall, floor and ceiling surfaces acts as a key input for the CFD simulations. An experiment has been carried out to measure exhalation rate from these surfaces using a combination unit comprising of an accumulator and SRM. The accumulator (volume 50.24 cm3) was used for accumulating radon in a perfectly sealed arrangement. Consequently, the exhalation rate was calculated from the growth curve pattern of radon measured with SRM. The experimental arrangement has demonstrated in the Fig. 2. It can be seen from this figure that accumulator was connected to an external pump sampled radon gas (at 1 liter per minute) and the accumulated radon was then measured with SRM at a frequency of 1 h. 2.3. Active monitoring of radon concentration Radon concentration inside the room was measured as a part of this work for two specific objectives: 1) Determination of ventilation rate 2) Comparison with passive measurements and CFD simulations. SRM was used for the measurement of radon concentration. Its detection principle is based on the detection of alpha particles, emitted from sampled radon and its decay products. It can be used for detecting radon concentration from 10 Bq/m3 to
2. Methods and measurements: 2.1. Description of room geometry The case study of an empty closed room of dimensions 3.01 m 3.01 m 3.0 m (Fig. 1) was carried out for the spatial and temporal distribution of indoor radon concentration under the present work. There were three doors of dimensions (0.9 m 1.99 m) having door-floor gap (0.9 m 0.02 m) in this room. All the doors were kept closed during the exposure of pinhole dosimeters. For simulations, the door opening which is directly in contact with the outer environment was considered as
Fig. 2. Experimental arrangement for direct radon exhalation measurement.
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10 MBq/m3. The details for this instrument and operating characteristics have been discussed elsewhere (Gaware et al., 2011). The indoor air was sampled into the scintillation cell (150 cm3) through a progeny filter and diffusion-time delay based thoron discriminator for eliminating radon/thoron progenies and thoron, respectively. The diffusion-time delay based thoron discriminator prevents the short lived thoron (half life 55.6 s) to enter into detector. For the purpose of present work, it was used in diffusion mode and sampling was performed at a frequency of 1 h Fig. 3 shows schematic diagram and photograph of the unit used for this work. 2.4. Passive (time integrated) measurements of radon concentration A single entry pin hole based twin cup dosimeter (Sahoo et al., 2013) which comprises of two cylindrical chambers, each of length of 4.5 cm and radius 2.5 cm was used for this purpose in the present work. The first chamber (named as radon þ thoron chamber) samples ambient air at the entry of which, the particulates are restricted by using a filter paper. The air containing radon and thoron from this chamber diffuses to the second chamber (named as radon chamber) through pin holes. However, only radon gets entered into the second chamber due to much shorter diffusion length for thoron. The tracks registered in LR-115 placed in first and second chamber are corresponding to the radon þ thoron and radon concentration in the atmosphere respectively. Schematic diagram and a photograph for pin-hole dosimeter have been shown in Fig. 4. Fifteen such units have been deployed in test room in the grid form avoiding ventilators, fans, doors (and any other obstacle) and 60 cm away from any surface of the room (walls, floor and ceiling). After exposure for 3 months, the latent tracks registered in LR115 are made optically visible by chemical etching (2.5 N NaOH, 60 C for 90 min without stirring) using automatic chemical etching instrument. Spark counter was used for track density measurements. If T is the track densities observed due to exposure to a concentration C of a given species for a time t, then
T ¼ k:C:t where k is Calibration factor (tracks cm2d1/(Bq m3)); d is the no of days of exposure; t is time in day; C is concentration in Bq m3 and T is in tracks cm2. The calibration factor used for radon was (0.017 ± 0.002 (tracks cm2 d1/(Bq m3)) (Sahoo et al., 2013). 3. Calculation of model input parameters Input parameters required for CFD simulations viz. radon exhalation rate (E), ventilation rate (lv) and inlet velocity (v) were
Fig. 4. Schematic diagram of the pin-hole based measurement device and its photograph.
calculated from the experimental measurements using standard relations. Another quantity based on radon exhalation rate i.e. Radon generation rate (Bq.m3. h1) was calculated using the following Eq. (1)
P3 G¼
i¼1 Ei *Ai Vroom
(1)
where i ¼ 1, 2, 3 corresponds to the wall, floor and ceiling of the room. Ei (Bq m2 h1) and Ai (m2) and V (m3) represents the radon exhalation rate (measured with accumulator þ SRM), surface area and volume of the room, respectively. Ventilation rate for the room was measured using Eq. (2) shown below
P3
C∞ ¼
i¼1 Ei *Ai ðl0 þ lv ÞVroom
(2)
where i ¼ 1, 2, 3 corresponding to the wall, floor and ceiling of the room. C∞ (Bq m3),Ei (Bq m2 h1) and Ai (m2) represents average steady state concentration, radon exhalation rate and surface area respectively while l0 (h1) is the radon decay constant, lv(h1) is
Fig. 3. Schematic diagram of Portable Radon Monitor (SRM) and SRM Photograph.
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the ventilation rate and Vroom is the volume of the room. In this study, SRM was used to estimate average steady state concentration (C∞) by sampling accumulated radon in the room which was closed for around 2 days before starting the measurements. The inlet velocity corresponding to the ventilation rate and the ventilation area was calculated from Eq. (3).
lv *Vroom v¼ Avent
(3)
The occupied air volume in the empty room was considered as the room volume (Vroom), which was 27.2 m3 corresponding to the room dimensions. The ventilation area (Avent), in the closed room condition was the opening below the door and was 0.018 m2.
4. Modeling approach Simulation procedure was carried out using two steps. Steady state flow field was established in the room in the first step followed by radon dispersion simulation in steady field (second step). The steady state flow field (developed by solving mass and momentum equations in the computational domain) provides steady values of model variables e.g. pressure, velocities and temperature etc. except scalar (radon). In the second step, radon dispersion simulation was executed over the steady flow field incorporating dispersion of scalar in the room. Key input parameters for CFD simulations for test room (see Table 1) were either measured directly (room volume, total surface area and ventilation area) or estimated through indirect measurements (ventilation rate and inlet velocity). The formulation of the problem and the governing equations for simulation are described below.
4.1. Flow field simulation The indoor air flow was assumed to be incompressible. To carry out simulation, first steady state flow field was established using mass conservation equation and momentum conservation Eqs. (4) and (5) (With and Jong, 2011). The mass conservation equation used for the simulation is
rðV:ui Þ ¼ 0
(4)
where, u is the velocity vector (m s1) and i is the index to represent the three velocity components. Momentum conservation equation used in simulation can be expressed as:
vui þ V: uj: ui r ¼ V:P þ V:ðme Vui Þ þ S vt
(5)
where, P is the pressure (N m2), me is the effective viscosity (N s m2), S represents the source term, and j is the index for the three velocity components. The effective viscosity me is the sum of dynamic viscosity m and turbulent viscosity mt. The essential part for applying the appropriate model depends on the value of Reynold's number. The calculated Reynold's number for the room parameters
Table 1 Input simulation parameters.
and inlet velocity was found to be greater than the 2000 and k-ε turbulent model was used to incorporate the effect of turbulence. 4.2. Radon simulation Dispersion of radon gas inside the room was simulated using the following equation
vC ¼ S þ V: D* VC V:ðuCÞ lC vt
where, C represents radon concentration in the room volume (Bq m3), D is turbulent radon diffusion coefficient (m2 s1), u is mean air flow velocity (m s1), S is radon source term (Bq m3 s1) and l is radon decay constant (2.1 106 s1). Inlet velocity is assigned at the inlet and pressure static at the outlets. No slip wall boundary condition is assigned at the walls; floor and ceiling with appropriate value of radon exhalation rate 1.59 Bq m2 h1, 0.96 Bq m2 h1 and 0.99 Bq m2 h1 (see Table 2) respectively. No temperature non-uniformity was assumed inside the computational domain. Finite volume method (FVM) has been used to simulate the radon distribution inside the room. In this method, fluid flow governing equations are discretised at each node in the domain and solutions of the corresponding algebraic equations provide distribution of variable of interest. 5. Results and discussion: Radon exhalation rate was measured in the model room using the set-up described in Fig. 2. Table 2 shows the surface area and measured radon exhalation rate for walls, floor and ceiling which was used as input for CFD simulations. It also calculates “Radon generation rate” representing the rate of radon generation contributed by source terms. Contribution of walls towards radon exhalation and generation rate was higher compared to floor and ceiling. The radon exhalation rate measured for the plastered brick wall was 1.59 Bq m2 h1. Floor and ceiling were made up of concrete and the radon exhalation values were seen to be lower. The generation rate was calculated as 2.46 ± 0.07 (Bq m3 h1). A real-time radon monitor (SRM) based on scintillation was used for the measurement of steady state radon concentration levels. For measurement of ventilation rate, the instrument was kept on to measure radon concentration continuously (for 24 h) for measuring the steady state values by closing the room for two days before the measurement started. The average of the measured values was considered as the averaged steady state radon concentration, which was found to be 27 ± 9 Bq. m3. When the room was closed, gap between the door and floor was considered for the exchange of fresh outdoor air i.e. 0.018 m2 (0.02 m * 0.9 m). For the estimation of ventilation rate through this opening area (closed room condition) Eq. (2) was used, which gave the existing ventilation rate of about 0.08 h1. In the literature, reported ventilation rate range for the closed living room is 0.1e1 h1(Zhuo et al., 2001).
Table 2 Radon flux measured with accumulator þ SRM. S.N.
Surface
1 2 3 Total
Walls Floor Ceiling generation
Surface area covered, Ai (m2)
Radon exhalation rate, Ei, (Bq m2 h1)
Generation rate P Ei * Ai/Vroom (Bq m3 h1).
30.9 09.1 09.1 rate
1.59 ± 0.10 0.96 ± 0.07 0.99 ± 0.19
(49.13 ± 3.1)/27.2 (08.74 ± 0.6)/27.2 (09.01 ± 1.7)/27.2 2.46 ± 0.07
Dwelling related parameters and simulation related input parameters Room volume
Total surface area
Ventilation rate
Ventilation area
Inlet velocity
27.2 m3
49.1 m2
0.08 h1
0.018 m2
0.03 m s1
(6)
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The velocity of air at the inlet corresponding to the ventilation rate and vent area was calculated using Eq. (4), which came out to be 0.03 m s1 and used as inlet boundary condition for CFD simulation. Using the above measured radon source term and boundary conditions; the CFD model was setup for indoor radon dispersion study. The simulation time was set large enough for getting steady state radon concentration (predicted from growth equation analytically). In order to investigate the horizontal spatial and temporal distribution profile of radon concentration using CFD model, contours of radon concentration at a position z ¼ 1.22 m from the floor at various time interval has been plotted which are shown in Fig. 5. The height 1.22 m above the floor is considered here approximately as a height of breathing zone at working place and at houses. The contour plots indicate that initial radon concentration (high near the walls) found to be dispersed into the room environment with passage of time. However, the non-uniformity observed in the in simulations can be viewed as an effect of difference in radon generation rates from surfaces and the ventilation profile. The CFD simulation predicted average steady state radon concentration of 22.6 Bq. m3 for the test room. Fig. 6 shows the radon distribution at different planes (z ¼ 1.22 m, 1.83 m, 2.44 m) indicating the in-homogeneity (corresponding to simulation time) in the distribution profile at various
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heights. Such differences could be linked to differences in velocity distribution pattern as shown in Fig. 7. Simulation results were validated using results of active and passive measurements carried out in same room (as described earlier). Radon concentrations were measured continuously for 24 h (at the interval of 1 h) at the 5 positions in a horizontal plane at z ¼ 1.22 m. The room was closed for two days before the measurement to achieve the steady state. The results of active measurement (SRM based) of radon concentration are shown in Table 3. The average radon concentration at 1.22 m height in the test room from the active measurement was found to be 23.6 ± 3.7 Bq m3. Passive measurements (using pin-hole dosimeters) were carried out at three planes (1.22 m, 1.83 m, 2.44 m) selecting five locations per plane. The results of time integrated indoor radon measurements for all locations are shown in Table 4. The average radon concentration in the test room was estimated to be 23.3 ± 15.4 Bq m3 (passive measurement). Also, it was clearly observed that radon concentration was not uniform even under closed room conditions. Table 5 shows the comparisons of CFD predictions with experimentally measured values for z ¼ 1.22 m plane of test room. Relatively higher values of deviations observed for passive measurements are expected due to considerably long exposure
Fig. 5. Horizontal distribution of the indoor radon concentration.
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Fig. 6. Steady state Rn222 distribution at different planes.
Fig. 7. Velocity distribution pattern at different planes.
Table 3 Active measurement results of Radon concentration. Distance from 24 h averaged radon concentration at 5 locations (Bq. m3) the floor (m) Corner 1 Corner 2 Corner 3 Corner 4 Center 1.22
27 ± 11
24 ± 8
27 ± 8
18 ± 6
22 ± 7
average steady state indoor radon concentration for the test room (taking average of all locations of selected 3 planes) was found to be 22.6 Bq m3, 23.6 ± 3.8 Bq m3 and 23.3 ± 15.4 Bq m3 from simulation, SRM measurements and pin hole measurements, respectively. As can be seen, simulation results were found to be quite close to active and passive measurement results for test room conditions. Also, a larger uncertainty for passive measurement can
Table 4 Passive measurement results of Radon concentration. Distance from the floor (m)
Radon concentration at 15 locations (Bq m3) Corner 1
Corner 2
Corner 3
Corner 4
Center
1.22 1.83 2.44
30 4 18
30 48 27
30 9 27
8 7 7
42 13 50
time during which room conditions are prone to change. However, average values for the entire plane predicted from CFD were seen to be closer to experimental values (deviation of 13.5% and 34.6% from active and passive measurements, respectively). On the other hand,
Table 5 Comparison of simulation results with experimental measurements for z ¼ 1.22 m. Location
Corner 1 Corner 2 Corner 3 Corner 4 Center Average (Bq/m3)
Radon concentration (Bq m3) CFD
Active measurement (Relative deviation*)
Passive measurement (Relative deviation*)
20 21 27 13 23 20.8
27 24 27 18 22 23.6
30 30 30 8 42 28
(35.0%) (14.3%) (0.0%) (38.5%) (4.3%) (13.5%)
(50.0%) (42.9%) (11.1%) (38.5%) (82.6%) (34.6%)
*Relative deviation ¼ (jMeasurement-CFD predictionj)/CFD prediction.
N. Chauhan et al. / Journal of Environmental Radioactivity 136 (2014) 105e111
again be attributed to the change in room properties during exposure period. The close agreement between simulation and experiments suggests that CFD modeling technique is capable of predicting the indoor radon distribution for real room conditions. The prediction of the radon (for present case) levels (and distribution pattern) by this technique is less time consuming, cost effective and more versatile. Measurement or accurate prediction of input parameters (required for CFD simulation) are crucial and can affect the simulation predictions. Measurement of input parameters and extensive validation experiments are the highlight of this work. The technique can be implemented for more complex indoor conditions in future. 6. Conclusions A carefully planned study was performed in a closed test room to study indoor radon distribution due to emissions from the walls, floor and ceiling. The radon flux and ventilation rate for the room were measured experimentally. Averaged flux was measured using accumulator and radon monitor combination while ventilation rate was calculated using the decay of accumulated radon in the room (measured with radon monitor). The measured values of radon flux i.e. 1.59 ± 0.10 (Bq m2 h1) for wall, 0.96 ± 0.07 (Bq m2 h1) for floor, 0.99 ± 0.19 (Bq m2 h1) for ceiling and room ventilation rate of 0.08 h1 were used as input for CFD model for simulation study. Using CFD modeling, spatial distribution of radon in the room was simulated for the test room conditions. It was observed that, radon distribution was not uniform due to the difference in the flux from different sources in non-mixing conditions. For test room at 1.22 m height, CFD simulation provided an averaged steady state concentration of 22.6 Bq m3. The averaged indoor radon concentration and spatial values of CFD simulation were compared with active and passive monitoring carried out for the same room. Measurements made with scintillation radon monitor and pin-hole dosimeters have shown values quite close to the CFD predicted results. Another striking feature of the study pertains regarding considerable difference in radon concentration in passive monitoring performed at different spatial locations. This hints at deployment of at least few dosimeters to make an average estimate for less ventilated conditions. Acknowledgment The authors are thankful to Board of Research in Nuclear Science, Department of Atomic Energy, Mumbai, India, for providing the financial support in term of major research project No. 2011/36/ 25-BRNS and instruments to carry out this work. References Akbari, K., Mahmoudi, J., Ghanbari, M., 2013. Influence of indoor air conditions on radon concentration in a detached house. J. Environ. Radioact. 116, 166e173. Andersen, C.E., 2001. Numerical modeling of radon-222 entry into houses: an outline of techniques and results. Sci. Total Environ. 272, 33e42. BEIR VI (Report on the committee on biological effects of ionizing radiation), 1999. Health Effects of Exposure to Radon. National Research Council. National Academy Press, Washington DC.
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Study of indoor radon distribution using measurements and CFD modeling Neetika Chauhan a, *, R.P. Chauhan a, M. Joshi b, T.K. Agarwal b, Praveen Aggarwal c, B.K. Sahoo b a b c
Department of Physics, National Institute of Technology, Kurukshetra 136119, India Radiological Physics and Advisory Division, Bhabha Atomic Research Centre, Mumbai 400 085, India Department of Civil Engineering, National Institute of Technology, Kurukshetra 136119, India
a r t i c l e i n f o
a b s t r a c t
Article history: Received 11 February 2014 Received in revised form 16 April 2014 Accepted 22 May 2014 Available online
Measurement and/or prediction of indoor radon (222Rn) concentration are important due to the impact of radon on indoor air quality and consequent inhalation hazard. In recent times, computational fluid dynamics (CFD) based modeling has become the cost effective replacement of experimental methods for the prediction and visualization of indoor pollutant distribution. The aim of this study is to implement CFD based modeling for studying indoor radon gas distribution. This study focuses on comparison of experimentally measured and CFD modeling predicted spatial distribution of radon concentration for a model test room. The key inputs for simulation viz. radon exhalation rate and ventilation rate were measured as a part of this study. Validation experiments were performed by measuring radon concentration at different locations of test room using active (continuous radon monitor) and passive (pin-hole dosimeters) techniques. Modeling predictions have been found to be reasonably matching with the measurement results. The validated model can be used to understand and study factors affecting indoor radon distribution for more realistic indoor environment. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Indoor radon Radon flux Ventilation rate Radon monitoring Computational fluid dynamics
1. Introduction Radon (222Rn) is naturally occurring radionuclide formed from alpha disintegration of radium (226Ra) in Uranium-238 decay series. Radon, thoron (220Rn) and their decay products contribute more than half of the background radiation (UNSCEAR, 2000). Being a noble gas, radon easily gets released from the source term to the pores (emanation) and subsequently from the pores to the outside environment (exhalation). In less or no ventilation (energy exchange) conditions, it gets built-up indoor and affect indoor air quality. Indoor radon exposure has been linked to lung cancer cases in several studies (WHO, 2009). Breathing for life time in a house where radon is at the action level of 200 Bq/m3 carries a 3e5% risk of fatal lung cancer (NRPB, 2000). Inhaled radon gas induces genotoxicity due to interaction of high energy alpha particles with biological tissue in the lungs leading to DNA damage (WHO, 2009). The complex mechanism of cancer arises as a result of two major genetic alterations, gene mutation and chromosomal aberrations (Land et al., 1983). Studies in mines, residential studies, animal * Corresponding author. E-mail address: [email protected] (N. Chauhan). http://dx.doi.org/10.1016/j.jenvrad.2014.05.020 0265-931X/© 2014 Elsevier Ltd. All rights reserved.
studies and radiobiology implicate indoor radon as a cause of lung cancer (BEIR, 1999). After the release from the walls and floor, radon gets distributed in indoor environment. Understanding its distribution is important to predict the spatial and temporal variations of levels which can ultimately be used for dose calculations and exposure control research. As the time spent indoor is large enough (particularly for house-wives and children); understanding, prediction and measurement of indoor radon distribution becomes quite important. Exhalation rate of radon from surfaces and room ventilation rate are two important factors affecting the indoor radon levels. An extensive experimental work has been carried out in order to investigate radon exhalation rates from different types of building materials (fired and un-fired brick, concrete, marble, granite, cement, gypsum, fly ash bricks, sand etc.) (Chen et al., 2010; Hassan et al., 2011; Keller et al., 2001; Kumar et al., 2014; Maged and Ashraf, 2005; Mahur et al., 2008; Petropoulos et al., 2001; Righi and Bruzzi, 2006). The radon exhalation rate may be different from the measured radon exhalation values when these building material are used in walls, floor and ceiling construction. This difference is due to dependence of diffusion length on the configuration (Sahoo et al., 2011). Some mathematical models are available
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in the literature for prediction of radon exhalation form the wall using radon exhalation rate of commonly used building materials (Nazaroff and Nero, 1988; Sahoo et al., 2011). A comparison of various models used for such purpose has been recently discussed in Kumar et al., 2014. However, there is too less experimental work available for direct measurement of radon exhalation from the room surfaces. In the present work, an accumulator is used for directly measuring radon wall exhalation rate. Ventilation rate has been measured in studies using radioactive krypton-85 (Samer et al., 2011), N2O or CO2 (Kittas et al., 1996), SF6 (Snell et al., 2003) as tracer gas. A few studies also reported use of radon as tracer gas that does not require any special safety measures compared to other tracer gases (Vasilyev and Zhukovsky, 2013). The present work also employs using indoor accumulated radon for estimation of ventilation rate. Traditionally, Indoor radon levels have been measured by passive method using dosimeters and active method using electronic devices for e.g. continuous radon monitor. Some mathematical models are also developed to estimate the indoor radon concentration (Jelle et al., 2011; Stoulos et al., 2003). In recent time, Computational Fluid Dynamics (CFD) has taken outstanding position for simulation of indoor radon problems. CFD solves the governing fluid equations and provides spatial and temporal field solution of variables such as pressure, temperature, energy density. It also provides velocity flow field and the dispersion pattern of indoor pollutant. Ability of commercially available CFD software to simulate a wide range of geometrical and environmental conditions is one of the appreciable features over its other existing tools. Many CFD based studies have been performed to develop the radon entry models to indoor form soil (Andersen et al., 2001; Loureiro, 1987; Wang and Ward, 1997, 2000, 2002). But limited CFD based studies have been performed to investigate indoor radon dispersion (Akbari et al., 2013; Urosevic et al., 2008; Zhuo et al., 2001). A major limitation of these studies however is lack of measurements which can help for both reduction of input parametric uncertainty and comparison of model predictions. The applicability of CFD for radon related research is still evolving and a lot needs to be done for applying this knowledge for mankind research. This work focuses on estimation of indoor radon distribution in a typical closed test room. The radon source term (radon exhalation from walls, floor and ceiling) and ventilation rate has been measured for this room. Measured radon source term and ventilation rate has been used as input in CFD code (Fluidyn MP-5.2.1) and simulations have been done taking room geometry. Pin-hole dosimeters (Sahoo et al., 2013) and scintillation radon monitor (SRM) (Gaware et al., 2011) have been used for the measurement of indoor radon in passive and active mode, respectively. These measurements were carried out at various locations so as to cover the room, spatially. This paper presents experimental measurements and simulations for the model room. The measured radon distribution (by passive and active means) in the room was compared with the simulation results.
Fig. 1. Room geometry with positions of dosimeters.
inlet (door 1) and other two were considered as outlet. Radon exhalation rate from the room surfaces (walls, floor and ceiling) was the only source term for the indoor radon build-up. 2.2. Radon exhalation rate measurement The surface exhalation rate of the radon from the room wall, floor and ceiling surfaces acts as a key input for the CFD simulations. An experiment has been carried out to measure exhalation rate from these surfaces using a combination unit comprising of an accumulator and SRM. The accumulator (volume 50.24 cm3) was used for accumulating radon in a perfectly sealed arrangement. Consequently, the exhalation rate was calculated from the growth curve pattern of radon measured with SRM. The experimental arrangement has demonstrated in the Fig. 2. It can be seen from this figure that accumulator was connected to an external pump sampled radon gas (at 1 liter per minute) and the accumulated radon was then measured with SRM at a frequency of 1 h. 2.3. Active monitoring of radon concentration Radon concentration inside the room was measured as a part of this work for two specific objectives: 1) Determination of ventilation rate 2) Comparison with passive measurements and CFD simulations. SRM was used for the measurement of radon concentration. Its detection principle is based on the detection of alpha particles, emitted from sampled radon and its decay products. It can be used for detecting radon concentration from 10 Bq/m3 to
2. Methods and measurements: 2.1. Description of room geometry The case study of an empty closed room of dimensions 3.01 m 3.01 m 3.0 m (Fig. 1) was carried out for the spatial and temporal distribution of indoor radon concentration under the present work. There were three doors of dimensions (0.9 m 1.99 m) having door-floor gap (0.9 m 0.02 m) in this room. All the doors were kept closed during the exposure of pinhole dosimeters. For simulations, the door opening which is directly in contact with the outer environment was considered as
Fig. 2. Experimental arrangement for direct radon exhalation measurement.
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10 MBq/m3. The details for this instrument and operating characteristics have been discussed elsewhere (Gaware et al., 2011). The indoor air was sampled into the scintillation cell (150 cm3) through a progeny filter and diffusion-time delay based thoron discriminator for eliminating radon/thoron progenies and thoron, respectively. The diffusion-time delay based thoron discriminator prevents the short lived thoron (half life 55.6 s) to enter into detector. For the purpose of present work, it was used in diffusion mode and sampling was performed at a frequency of 1 h Fig. 3 shows schematic diagram and photograph of the unit used for this work. 2.4. Passive (time integrated) measurements of radon concentration A single entry pin hole based twin cup dosimeter (Sahoo et al., 2013) which comprises of two cylindrical chambers, each of length of 4.5 cm and radius 2.5 cm was used for this purpose in the present work. The first chamber (named as radon þ thoron chamber) samples ambient air at the entry of which, the particulates are restricted by using a filter paper. The air containing radon and thoron from this chamber diffuses to the second chamber (named as radon chamber) through pin holes. However, only radon gets entered into the second chamber due to much shorter diffusion length for thoron. The tracks registered in LR-115 placed in first and second chamber are corresponding to the radon þ thoron and radon concentration in the atmosphere respectively. Schematic diagram and a photograph for pin-hole dosimeter have been shown in Fig. 4. Fifteen such units have been deployed in test room in the grid form avoiding ventilators, fans, doors (and any other obstacle) and 60 cm away from any surface of the room (walls, floor and ceiling). After exposure for 3 months, the latent tracks registered in LR115 are made optically visible by chemical etching (2.5 N NaOH, 60 C for 90 min without stirring) using automatic chemical etching instrument. Spark counter was used for track density measurements. If T is the track densities observed due to exposure to a concentration C of a given species for a time t, then
T ¼ k:C:t where k is Calibration factor (tracks cm2d1/(Bq m3)); d is the no of days of exposure; t is time in day; C is concentration in Bq m3 and T is in tracks cm2. The calibration factor used for radon was (0.017 ± 0.002 (tracks cm2 d1/(Bq m3)) (Sahoo et al., 2013). 3. Calculation of model input parameters Input parameters required for CFD simulations viz. radon exhalation rate (E), ventilation rate (lv) and inlet velocity (v) were
Fig. 4. Schematic diagram of the pin-hole based measurement device and its photograph.
calculated from the experimental measurements using standard relations. Another quantity based on radon exhalation rate i.e. Radon generation rate (Bq.m3. h1) was calculated using the following Eq. (1)
P3 G¼
i¼1 Ei *Ai Vroom
(1)
where i ¼ 1, 2, 3 corresponds to the wall, floor and ceiling of the room. Ei (Bq m2 h1) and Ai (m2) and V (m3) represents the radon exhalation rate (measured with accumulator þ SRM), surface area and volume of the room, respectively. Ventilation rate for the room was measured using Eq. (2) shown below
P3
C∞ ¼
i¼1 Ei *Ai ðl0 þ lv ÞVroom
(2)
where i ¼ 1, 2, 3 corresponding to the wall, floor and ceiling of the room. C∞ (Bq m3),Ei (Bq m2 h1) and Ai (m2) represents average steady state concentration, radon exhalation rate and surface area respectively while l0 (h1) is the radon decay constant, lv(h1) is
Fig. 3. Schematic diagram of Portable Radon Monitor (SRM) and SRM Photograph.
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the ventilation rate and Vroom is the volume of the room. In this study, SRM was used to estimate average steady state concentration (C∞) by sampling accumulated radon in the room which was closed for around 2 days before starting the measurements. The inlet velocity corresponding to the ventilation rate and the ventilation area was calculated from Eq. (3).
lv *Vroom v¼ Avent
(3)
The occupied air volume in the empty room was considered as the room volume (Vroom), which was 27.2 m3 corresponding to the room dimensions. The ventilation area (Avent), in the closed room condition was the opening below the door and was 0.018 m2.
4. Modeling approach Simulation procedure was carried out using two steps. Steady state flow field was established in the room in the first step followed by radon dispersion simulation in steady field (second step). The steady state flow field (developed by solving mass and momentum equations in the computational domain) provides steady values of model variables e.g. pressure, velocities and temperature etc. except scalar (radon). In the second step, radon dispersion simulation was executed over the steady flow field incorporating dispersion of scalar in the room. Key input parameters for CFD simulations for test room (see Table 1) were either measured directly (room volume, total surface area and ventilation area) or estimated through indirect measurements (ventilation rate and inlet velocity). The formulation of the problem and the governing equations for simulation are described below.
4.1. Flow field simulation The indoor air flow was assumed to be incompressible. To carry out simulation, first steady state flow field was established using mass conservation equation and momentum conservation Eqs. (4) and (5) (With and Jong, 2011). The mass conservation equation used for the simulation is
rðV:ui Þ ¼ 0
(4)
where, u is the velocity vector (m s1) and i is the index to represent the three velocity components. Momentum conservation equation used in simulation can be expressed as:
vui þ V: uj: ui r ¼ V:P þ V:ðme Vui Þ þ S vt
(5)
where, P is the pressure (N m2), me is the effective viscosity (N s m2), S represents the source term, and j is the index for the three velocity components. The effective viscosity me is the sum of dynamic viscosity m and turbulent viscosity mt. The essential part for applying the appropriate model depends on the value of Reynold's number. The calculated Reynold's number for the room parameters
Table 1 Input simulation parameters.
and inlet velocity was found to be greater than the 2000 and k-ε turbulent model was used to incorporate the effect of turbulence. 4.2. Radon simulation Dispersion of radon gas inside the room was simulated using the following equation
vC ¼ S þ V: D* VC V:ðuCÞ lC vt
where, C represents radon concentration in the room volume (Bq m3), D is turbulent radon diffusion coefficient (m2 s1), u is mean air flow velocity (m s1), S is radon source term (Bq m3 s1) and l is radon decay constant (2.1 106 s1). Inlet velocity is assigned at the inlet and pressure static at the outlets. No slip wall boundary condition is assigned at the walls; floor and ceiling with appropriate value of radon exhalation rate 1.59 Bq m2 h1, 0.96 Bq m2 h1 and 0.99 Bq m2 h1 (see Table 2) respectively. No temperature non-uniformity was assumed inside the computational domain. Finite volume method (FVM) has been used to simulate the radon distribution inside the room. In this method, fluid flow governing equations are discretised at each node in the domain and solutions of the corresponding algebraic equations provide distribution of variable of interest. 5. Results and discussion: Radon exhalation rate was measured in the model room using the set-up described in Fig. 2. Table 2 shows the surface area and measured radon exhalation rate for walls, floor and ceiling which was used as input for CFD simulations. It also calculates “Radon generation rate” representing the rate of radon generation contributed by source terms. Contribution of walls towards radon exhalation and generation rate was higher compared to floor and ceiling. The radon exhalation rate measured for the plastered brick wall was 1.59 Bq m2 h1. Floor and ceiling were made up of concrete and the radon exhalation values were seen to be lower. The generation rate was calculated as 2.46 ± 0.07 (Bq m3 h1). A real-time radon monitor (SRM) based on scintillation was used for the measurement of steady state radon concentration levels. For measurement of ventilation rate, the instrument was kept on to measure radon concentration continuously (for 24 h) for measuring the steady state values by closing the room for two days before the measurement started. The average of the measured values was considered as the averaged steady state radon concentration, which was found to be 27 ± 9 Bq. m3. When the room was closed, gap between the door and floor was considered for the exchange of fresh outdoor air i.e. 0.018 m2 (0.02 m * 0.9 m). For the estimation of ventilation rate through this opening area (closed room condition) Eq. (2) was used, which gave the existing ventilation rate of about 0.08 h1. In the literature, reported ventilation rate range for the closed living room is 0.1e1 h1(Zhuo et al., 2001).
Table 2 Radon flux measured with accumulator þ SRM. S.N.
Surface
1 2 3 Total
Walls Floor Ceiling generation
Surface area covered, Ai (m2)
Radon exhalation rate, Ei, (Bq m2 h1)
Generation rate P Ei * Ai/Vroom (Bq m3 h1).
30.9 09.1 09.1 rate
1.59 ± 0.10 0.96 ± 0.07 0.99 ± 0.19
(49.13 ± 3.1)/27.2 (08.74 ± 0.6)/27.2 (09.01 ± 1.7)/27.2 2.46 ± 0.07
Dwelling related parameters and simulation related input parameters Room volume
Total surface area
Ventilation rate
Ventilation area
Inlet velocity
27.2 m3
49.1 m2
0.08 h1
0.018 m2
0.03 m s1
(6)
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The velocity of air at the inlet corresponding to the ventilation rate and vent area was calculated using Eq. (4), which came out to be 0.03 m s1 and used as inlet boundary condition for CFD simulation. Using the above measured radon source term and boundary conditions; the CFD model was setup for indoor radon dispersion study. The simulation time was set large enough for getting steady state radon concentration (predicted from growth equation analytically). In order to investigate the horizontal spatial and temporal distribution profile of radon concentration using CFD model, contours of radon concentration at a position z ¼ 1.22 m from the floor at various time interval has been plotted which are shown in Fig. 5. The height 1.22 m above the floor is considered here approximately as a height of breathing zone at working place and at houses. The contour plots indicate that initial radon concentration (high near the walls) found to be dispersed into the room environment with passage of time. However, the non-uniformity observed in the in simulations can be viewed as an effect of difference in radon generation rates from surfaces and the ventilation profile. The CFD simulation predicted average steady state radon concentration of 22.6 Bq. m3 for the test room. Fig. 6 shows the radon distribution at different planes (z ¼ 1.22 m, 1.83 m, 2.44 m) indicating the in-homogeneity (corresponding to simulation time) in the distribution profile at various
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heights. Such differences could be linked to differences in velocity distribution pattern as shown in Fig. 7. Simulation results were validated using results of active and passive measurements carried out in same room (as described earlier). Radon concentrations were measured continuously for 24 h (at the interval of 1 h) at the 5 positions in a horizontal plane at z ¼ 1.22 m. The room was closed for two days before the measurement to achieve the steady state. The results of active measurement (SRM based) of radon concentration are shown in Table 3. The average radon concentration at 1.22 m height in the test room from the active measurement was found to be 23.6 ± 3.7 Bq m3. Passive measurements (using pin-hole dosimeters) were carried out at three planes (1.22 m, 1.83 m, 2.44 m) selecting five locations per plane. The results of time integrated indoor radon measurements for all locations are shown in Table 4. The average radon concentration in the test room was estimated to be 23.3 ± 15.4 Bq m3 (passive measurement). Also, it was clearly observed that radon concentration was not uniform even under closed room conditions. Table 5 shows the comparisons of CFD predictions with experimentally measured values for z ¼ 1.22 m plane of test room. Relatively higher values of deviations observed for passive measurements are expected due to considerably long exposure
Fig. 5. Horizontal distribution of the indoor radon concentration.
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Fig. 6. Steady state Rn222 distribution at different planes.
Fig. 7. Velocity distribution pattern at different planes.
Table 3 Active measurement results of Radon concentration. Distance from 24 h averaged radon concentration at 5 locations (Bq. m3) the floor (m) Corner 1 Corner 2 Corner 3 Corner 4 Center 1.22
27 ± 11
24 ± 8
27 ± 8
18 ± 6
22 ± 7
average steady state indoor radon concentration for the test room (taking average of all locations of selected 3 planes) was found to be 22.6 Bq m3, 23.6 ± 3.8 Bq m3 and 23.3 ± 15.4 Bq m3 from simulation, SRM measurements and pin hole measurements, respectively. As can be seen, simulation results were found to be quite close to active and passive measurement results for test room conditions. Also, a larger uncertainty for passive measurement can
Table 4 Passive measurement results of Radon concentration. Distance from the floor (m)
Radon concentration at 15 locations (Bq m3) Corner 1
Corner 2
Corner 3
Corner 4
Center
1.22 1.83 2.44
30 4 18
30 48 27
30 9 27
8 7 7
42 13 50
time during which room conditions are prone to change. However, average values for the entire plane predicted from CFD were seen to be closer to experimental values (deviation of 13.5% and 34.6% from active and passive measurements, respectively). On the other hand,
Table 5 Comparison of simulation results with experimental measurements for z ¼ 1.22 m. Location
Corner 1 Corner 2 Corner 3 Corner 4 Center Average (Bq/m3)
Radon concentration (Bq m3) CFD
Active measurement (Relative deviation*)
Passive measurement (Relative deviation*)
20 21 27 13 23 20.8
27 24 27 18 22 23.6
30 30 30 8 42 28
(35.0%) (14.3%) (0.0%) (38.5%) (4.3%) (13.5%)
(50.0%) (42.9%) (11.1%) (38.5%) (82.6%) (34.6%)
*Relative deviation ¼ (jMeasurement-CFD predictionj)/CFD prediction.
N. Chauhan et al. / Journal of Environmental Radioactivity 136 (2014) 105e111
again be attributed to the change in room properties during exposure period. The close agreement between simulation and experiments suggests that CFD modeling technique is capable of predicting the indoor radon distribution for real room conditions. The prediction of the radon (for present case) levels (and distribution pattern) by this technique is less time consuming, cost effective and more versatile. Measurement or accurate prediction of input parameters (required for CFD simulation) are crucial and can affect the simulation predictions. Measurement of input parameters and extensive validation experiments are the highlight of this work. The technique can be implemented for more complex indoor conditions in future. 6. Conclusions A carefully planned study was performed in a closed test room to study indoor radon distribution due to emissions from the walls, floor and ceiling. The radon flux and ventilation rate for the room were measured experimentally. Averaged flux was measured using accumulator and radon monitor combination while ventilation rate was calculated using the decay of accumulated radon in the room (measured with radon monitor). The measured values of radon flux i.e. 1.59 ± 0.10 (Bq m2 h1) for wall, 0.96 ± 0.07 (Bq m2 h1) for floor, 0.99 ± 0.19 (Bq m2 h1) for ceiling and room ventilation rate of 0.08 h1 were used as input for CFD model for simulation study. Using CFD modeling, spatial distribution of radon in the room was simulated for the test room conditions. It was observed that, radon distribution was not uniform due to the difference in the flux from different sources in non-mixing conditions. For test room at 1.22 m height, CFD simulation provided an averaged steady state concentration of 22.6 Bq m3. The averaged indoor radon concentration and spatial values of CFD simulation were compared with active and passive monitoring carried out for the same room. Measurements made with scintillation radon monitor and pin-hole dosimeters have shown values quite close to the CFD predicted results. Another striking feature of the study pertains regarding considerable difference in radon concentration in passive monitoring performed at different spatial locations. This hints at deployment of at least few dosimeters to make an average estimate for less ventilated conditions. Acknowledgment The authors are thankful to Board of Research in Nuclear Science, Department of Atomic Energy, Mumbai, India, for providing the financial support in term of major research project No. 2011/36/ 25-BRNS and instruments to carry out this work. References Akbari, K., Mahmoudi, J., Ghanbari, M., 2013. Influence of indoor air conditions on radon concentration in a detached house. J. Environ. Radioact. 116, 166e173. Andersen, C.E., 2001. Numerical modeling of radon-222 entry into houses: an outline of techniques and results. Sci. Total Environ. 272, 33e42. BEIR VI (Report on the committee on biological effects of ionizing radiation), 1999. Health Effects of Exposure to Radon. National Research Council. National Academy Press, Washington DC.
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