monitoring spatiotemporal dynamics of indoor radon ...

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Fresenius Environmental Bulletin

MONITORING SPATIOTEMPORAL DYNAMICS OF INDOOR RADON CONCENTRATIONS IN THE BUILT ENVIRONMENT OF A UNIVERSITY CAMPUS Seyma Atik1, Hakan Yetis1, Haluk Denizli1, Fatih Evrendilek2 1 Department of Physics, Abant Izzet Baysal University, 14280 Bolu, Turkey Department of Environmental Engineering, Abant Izzet Baysal University, 14280 Bolu, Turkey

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annual effective dose of 1.2 mSv for indoor radon exposure [1]. The first indoor radon measurements were performed in 1950s by Hultquist for 300 buildings and four different cities of Sweden [2]. In subsequent measurements, indoor radon level was found to be highly correlated with types of dwellings, and building materials [3-7]. Therefore, a considerable amount of radon can accumulate in buildings, especially, those with poor ventilation [8], and International Agency for Research on Cancer [9] reported inhalation of radon in high concentrations to be carcinogenic. There has been a study about the mutagenetic effects of the exposure to Radon-222 and its progeny in laboratory mice by [10] which showed in mice groups exposed to more than 700 kBq/h/m3 of Radon-222, the micronuclei frequency was significantly higher than that observed before exposure. Faculty members, students, and staff in university buildings are exposed to both short-term and long-term indoor pollutants. However, there exist a few studies that focus on indoor radon concentrations in university buildings, modeling of their spatiotemporal controls in a parsimonious way, and model validation. For example, Hasan [11] reported exposure of workers in Hebron University to indoor radon concentrations. Atik et al. [12] for the first time applied artificial neural networks to account for variation in indoor radon concentration in university buildings as a function of air temperature, location, floor level, and aspect. The objective of the present study was to model spatiotemporal dynamics of indoor radon concentration in the five buildings of Abant Izzet Baysal University (AIBU) based on one-year data of air temperature, relative humidity, air pressure, and their interaction terms.

ABSTRACTS This study aims to model spatiotemporal variability of indoor radon (222Rn) concentrations measured for one year from May 2012 to May 2013 in the built environment of Abant Izzet Baysal University. There exist a few studies about datadriven modeling of spatiotemporal dynamics of indoor radon and their validation. Mean indoor radon concentration varied spatially between 14 ± 8.5 Bq/m3 and 28.5 ± 17.5 Bq/m3 and on a monthly basis between 37.3 ± 21.6 Bq/m3 in September and 13.1 ± 7.7 Bq/m3 in April, and on a seasonal basis between 23.4 ± 18.4 Bq/m3 for the summer period of June to September and 13.3 ± 7.9 Bq/m3 for the spring period of April to May. The best-fit multiple non-linear regression (MNLR) model developed in this study elucidated 57.9% (R2adj) of the spatiotemporal variability, with a cross-validationderived predictive power of 57.1% (R2CV). The twoway interactions among the temporal predictors of hour and month, air temperature, relative humidity, and location were most influential in predicting indoor radon levels. Parsimonious versus datahungry empirical non-black-box models appear to be of great practical importance to the quantification, monitoring, and mapping of shortand long-term local, regional, or global spatiotemporal dynamics of indoor and outdoor radon concentrations. KEYWORDS: indoor radon, indoor air quality, air quality monitoring, empirical modeling, spatiotemporal dynamics

INTRODUCTION Radon (222Rn) and its decay products (polonium-218, lead-214, bismuth-214, and polonium-214) are the main sources of the indoor environmental radioactivity and the main determinants of indoor environmental quality and health. Radon progeny quickly diffuses from soils and rocks to the air with the worldwide average

MATERIALS AND METHODS Sampling. Radon measurements were performed in cafeterias of five Faculty buildings of AIBU about 900 m above sea level in Bolu (Turkey) between May of 2012 and 2013. The 823

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Faculty buildings randomly selected at the campus area for radon measurements were (1) the Faculty of Arts and Science (FAS), (2) the Faculty of Economics and Administrative Sciences (FEAS), (3) the Faculty of Education (FE), (4) the School of Physical Education and Sports (SPES), and (5) the School of Foreign Languages (SFL). Cafeterias of

the buildings were chosen as the most representative places to carry out radon measurements because undergraduate students spend most of their spare times there and they all are located in the basement except for SPES whose cafeteria is on its second floor (Fig. 1).

FIGURE 1 Five Faculty buildings randomly selected in the campus area of Abant Izzet Baysal University (Bolu, Turkey): FAS: the Faculty of Arts and Science; FEAS: the Faculty of Economics and Administrative Sciences; FE: the Faculty of Education; SPES: the School of Physical Education and Sports; and SFL: the School of Foreign Languages.

A calibrated AlphaGUARD P30 ionization detector as a portable radon monitor (Saphymo GmbH, Germany) was used for active measurements at a 10-min time interval during daytime for each month of the study period. In addition, AlphaGUARD registers such ancillary variables as ambient temperature (Ta), relative humidity (RH), atmospheric pressure (Pa), and local time. The sampling strategy employed included monitoring over a year-long period, with each sample representing a 10-minute average. A total of 1996 samples distributed among the five sites randomly selected (1% of the year's time monitored at each location, with average of 66 h when compared to 8760 h per year) were taken so as to represent average exposure conditions during occupancy. Such considerations for the detector as standing position, height from the floor, and distance from ventilation of the measurement area were held constant during the measurements.

detect significant linear relationships between indoor radon concentrations and ancillary data. Tukey’s multiple comparisons following one-way analysis of variance (ANOVA) were used to capture significant mean differences in Ta, RH, Pa, and radon concentration spatially (among the five locations) and temporally (among the 12 months). Best-fit multiple non-linear regression (MNLR) models were built using a stepwise procedure with alpha-to-enter and -to-remove values set to 0.5. The categorical predictor of location was incorporated in the MNLR models as a dummy variable which was coded as follows: 1 = FAS (as the reference level); 2 = FEAS; 3 = FE; 4 = SPES; and 5 = SFL. Autocorrelation and multicollinearity in the MNLR models were measured using Durbin-Watson statistics and variance inflation factors (VIF), respectively. Goodness-of-fit and cross-validationderived predictive power of the best-fit MNLR models were measured using adjusted coefficient of determination (R2adj) and R2CV, respectively.

Data Analysis. Statistical analyses were performed using Minitab 17.1 (Minitab Inc. State College, PA, USA). Anderson-Darling (AD) statistic was used to capture the optimal statistical distribution of the measured variables, with the smallest AD value being the closest fit to the data. Pearson’s correlation matrix was employed to

RESULTS AND DISCUSSIONS Consistent with the fact that the distribution of indoor radon concentration follows a lognormal

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maximum of 85 Bq/m3 in FAS in December (Table 1). Mean indoor radon concentration ranged from 14 ± 8.5 Bq/m3 in SFL to 28.5 ± 17.5 Bq/m3 in FAS (Table 2). Maximum indoor radon concentrations were 60.0 Bq/m3 in FEAS, 54.0 Bq/m3 in FE, 61.0 Bq/m3 in SPES and 52.0 Bq/m3 in SFL.

distribution [13], 3-parameter lognormal distribution best fitted our indoor radon data (Table 1). Indoor radon concentration across the five Faculty buildings varied from a minimum of 0 Bq/m3 in FE from June to August, SPES in July and September, and SFL in April, July and October to a

TABLE 1 Descriptive statistics of radon concentration ( 222Rn) and ancillary measurements (n = 1996). 222

Rn (Bq/m3) 19.9 13.5 67.5 0 17 85 9.0 (n = 177) 1.53 3.15 3-parameter lognormal (AD1 = 2.9)

Variable Mean SD CV Minimum Median Maximum Mode Skewness Kurtosis Distribution

Location 3.11 Scale 0.49 Threshold -5.52 Shape 1 AD: Anderson-Darling statistic

Ta (oC) 22.9 2.1 9.1 12.6 23.1 28.4 25.1 (n = 66) -0.34 0.05 3-parameter Weibull (AD = 2.0) 12.96 10.88 6.63

Pa (mbar) 918.2 4.6 0.5 903.6 917.8 928.9 914.4 (n = 36) -0.13 0.36 Loglogistic (AD = 11.3)

RH (%) 34.9 11.6 33.2 12.7 36.1 60.5 43.0 (n = 40) -0.12 -0.98 Weibull (AD = 14.5)

6.82 0.002

38.93 3.42

TABLE 2 Tukey’s multiple comparisons test for mean indoor radon concentration and ancillary data in response to changes in location (P ≤ 0.001).

Building FAS FEAS FE SPES SFL

n 508 379 373 372 364

RH (%) 37.1+7.9ab 35.5+10.0b 38.6+8.6a 30.2+13.7c 32.1+15.2c

Pa (mbar) 917.9+3.7b 916.8+5.3c 920.2+5.4a 915.5+2.0d 920.9+3.8a

The mean indoor radon concentration of 19.9 + 13.5 Bq/m3 for the five buildings was below the worldwide geometric mean values of 37.0 + 2.2 Bq/m3 (unweighted) and 30.0 + 2.3 Bq/m3 (population weighted) reported for indoor radon concentrations by UNSCEAR [14]. The maximum indoor radon concentrations measured in the built environment of AIBU were below the reference level of 100 Bq/m3 set by WHO [15] to minimize health risks due to indoor radon exposure. By measuring indoor radon concentrations in 7293

Ta (oC) 24.2+1.2a 22.4+2.5c 22.1+1.9cd 23.8+1.7b 21.9+2.1d

222

Rn (Bq/m3) 28.5+17.5a 21.2+11.1b 16.9+9.8c 16.0+10.1cd 14.0+8.5d

dwellings in 153 residential units of the total 81 provinces of Turkey, Celebi et al. [8] estimated the geometric mean to be 57 + 2.3 Bq/m3 with the range of 1 to 1400 Bq/m3. However, there exists considerable variability both within and between countries. For example, Canoba et al. [16] reported mean indoor radon concentrations for the following six Latin countries of Argentina (37.0 + 9.4 Bq/m3; n = 2034), Brazil (81.3 + 4.5 Bq/m3; n = 320), Ecuador (94.3 + 17.2 Bq/m3; n = 61), Mexico (67.9 + 34.6 Bq/m3; n = 4630), Peru (32.3 + 2.4 Bq/m3; n 825

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= 168), and Venezuela (52.5 + 24.7 Bq/m3; n = 143). UNSCEAR [17] reported nominal geometric mean indoor radon levels to range from < 10 Bq/m3 in Egypt and Cuba to > 100 Bq/m3 in a number of European countries and even to > 600 Bq/m3 in some parts of Iran. Mean indoor radon concentration of the university buildings reached the maximum (37.3 + 21.6 Bq/m3) in September (n = 196) and the minimum (13.1 + 7.7 Bq/m3) in April (n = 190). Mean indoor radon concentration was highest (23.4 + 18.4 Bq/m3) for the summer period of June to September (n = 576) and lowest (13.3 + 7.9 Bq/m3) for the spring period of April to May (n = 332). Median indoor radon concentration was highest

(20.0 Bq/m3) for the autumn period of October to November (n = 423) and lowest (11.0 Bq/m3) for the spring period. On average, the FAS building had the maximum indoor radon concentration and temperature, whereas the SFL building had the minimum indoor radon concentration, temperature and RH and the maximum Pa (Table 2) (P ≤ 0.001). On average, the winter period showed the minimum RH, Pa and Ta, while the minimum indoor radon concentration was observed in the spring period. Unlike the winter and spring periods, the maximum values of indoor RH, Ta and radon concentration were observed in the summer period (Table 3) (P ≤ 0.001).

TABLE 3 Tukey’s multiple comparisons test for mean indoor radon concentration and ancillary data in response to changes in month (P ≤ 0.001).

Month January February March April May June July August September October November December

n 154 101 245 190 142 130 131 119 196 262 161 165

RH (%) 21.3+7.1h 30.2+4.6e 25.4+8.4fg 26.6+6.4f 42.6+4.6d 45.2+6.6bc 48.1+5.9a 41.9+5.9d 43.0+2.7cd 46.7+5.7ab 24.0+3.8g 26.0+6.4fg

Pa (mbar) 919.5+6.0b 916.6+1.5de 912.8+4.6f 920.9+5.7a 917.9+1.7cd 916.3+1.6e 916.5+1.8de 916.3+4.6e 919.6+2.4b 921.1+3.6a 918.3+2.7bc 921.5+3.3a

Indoor radon concentrations showed significant positive correlations with local time (r = 0.54), month (r = 0.22), RH (r = 0.15), and Pa (r = 0.09) and a significant negative correlation with Ta (r = 0.54) (P < 0.05). However, the stepwise procedure based on the significance level of 0.05 for the removal and addition of predictors, and their interaction and polynomial terms through a specified order of 3 pointed out to a MNLR model as the best-fit one in accounting for and predicting variation in spatiotemporal dynamics of indoor radon concentration. Thus, three- and two-way interaction terms, and quadratic and cubic terms of the predictors were found to elucidate spatiotemporal dynamics of indoor radon concentration better than linear relationships as measured by Pearson’s correlation matrix. The best-fit MNLR model found in this study explained and predicted 57.9% (R2adj) and 57.1% (R2CV) of variation in spatiotemporal dynamics of indoor

Ta (oC) 22.0+2.5g 21.9+1.6g 22.4+1.9fg 22.8+2.4ef 22.9+2.0def 23.8+1.2bc 25.1+1.5a 23.7+1.6bcd 23.1+0.8cde 22.1+2.4g 24.2+1.8b 22.8+2.1ef

222

Rn (Bq/m3) 19.3+9.0bc 21.7+10.6b 16.2+8.8cd 13.2+7.7d 13.7+8.2d 14.9+9.9cd 14.9+11.2cd 19.3+11.7bc 37.4+21.6a 21.6+12.0b 20.3+11.0b 23.0+10.8b

radon concentration, respectively (Table 4). Although extensive surveys have been reported to characterize indoor radon concentrations in a variety of settings, there are only a few highly predictive empirical models reported using the parsimonious factors that mechanistically govern indoor air concentrations. For example, Bochicchio et al. [18] elucidated 26% of the total variation in indoor radon concentration measured in 334 primary schools of 13 municipalities of three districts in Southern Serbia using the best-fit multiple linear regression (MLR) model as a function of municipality (three categories), village/town (two categories), floor (four categories), school size, and intended use of the room. Using Kernel regression, Kropat et al. [19] accounted for 28% of the variations of indoor radon concentration as a function of building type, foundation type, year of construction, detector type, geographical coordinates, altitude, temperature, and 826

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lithology based on 238,769 measurements in 148,458 houses in Switzerland. Andersen et al. [20] reported a better predictive MLR model for indoor radon in Danish houses than MLR models specified for other countries. The best-fit MLR model developed by Andersen et al. [20] using nine explanatory variables had an R2adj of 40% and an R2 of 45% based on a comparison between predicted values and an independent dataset of measured indoor radon (n = 788). Two-way interaction terms make up 74% of the 19 predictors used in the best-fit MNLR model, with the remaining being three-way interaction (16%) and cubic (10%) terms. The three predictors that exerted the most positive influence on the rate of change in indoor radon concentration were twoway interactions between hour and month, hour and RH, and hour2 and Ta, respectively. Likewise, the three predictors that caused the highest rate of negative change in indoor radon concentration were two-way interactions between hour2 and month, Ta and month, and hour and month2. The two-way interactions of location were significant with the temporal predictors of hour and month, Ta, and RH.

from that of SPES. The interaction of the temporal predictors (hour*location) showed that indoor radon concentration of FEAS was the highest, that of FAS differed most (0.8%) from that of SFL and least (0.2%) from that of FE.

CONCLUSIONS One-year indoor radon measurements were taken in the five buildings of the university campus. Spatiotemporal dynamics of indoor radon concentrations were modeled and validated using the best-fit MNLR model. Non-linear relationships among the temporal predictors, RH, Ta, and location, in particular, their two-way interactions emulate spatiotemporal dynamics of indoor radon concentration in the built environment of the university campus. All the mean indoor radon concentrations across the AIBU buildings were found below the global geometric mean indoor radon concentration of 30 Bq/m3 (population weighted), and the maximum indoor radon concentrations were below the reference level of 100 Bq/m3 determined to minimize health risks due to indoor radon exposure. The modeling approach illustrated in this study can be extended to the quantifications of short- and long-term local, regional, or global spatiotemporal dynamics of indoor and outdoor radon concentrations. To better understand controls over and improve indoor air quality for a particular built environment, a stochastic component such as Markov Chain and Monte Carlo algorithms should be coupled to nonblock-box, data-driven and deterministic models such as MLR and MNLR models.

According to the two-way interaction of location by hour, indoor radon concentration of FAS was the highest and differed most (82%) from that of FEAS and least (38.5%) from that of FE. According to the interaction between location and RH, indoor radon concentration of FAS was the highest again; however, differed most (0.4%) from that of FE and least (0.09%) from that of FEAS. Coefficients associated with the location by Ta interaction indicated that indoor radon concentration of FAS was the lowest and differed most (1.6%) from that of FEAS and least (0.9%)

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TABLE 4 Multiple non-linear regression (MNLR) model of spatiotemporal dynamics of indoor radon concentration (Bq/m3) with the FAS building as the reference level (R2adj = 57.95%; R2CV = 57.13%; D-W = 1.5; VIF > 10; SE = 10.6 Bq/m3; n = 1996; P ≤ 0.001). Term Intercept hour*RH hour*month Ta*RH Ta*month hour*location FEAS FE SPES SFL Ta*location FEAS FE SPES SFL RH*location FEAS FE SPES SFL month*location FEAS FE SPES SFL hour3 month3 hour2*Ta hour2*RH hour2*month hour*Ta*RH hour*Ta*month hour*Pa*RH hour*month2 Ta2*month Ta*month2

Coefficient

SE

T-value

P

13.3 20.3 27.1 0.053 -0.751

2 3 4 0.01 0.09

5.8 7.6 6.5 7.2 -8.0

0.001 0.001 0.001 0.001 0.001

-82.0 -38.5 -44.0 -60.0

8 8 8 8

-10.3 -5.0 -5.6 -7.8

0.001 0.001 0.001 0.001

1.674 1.361 0.918 1.391

0.26 0.26 0.18 0.19

6.5 5.3 5.0 7.3

0.001 0.001 0.001 0.001

-0.094 -0.446 -0.137 -0.141

0.09 0.09 0.07 0.07

-1.1 -4.9 -2.0 -2.0

> 0.05 0.001 0.04 0.04

0.643 -0.241 -0.300 -0.871 -0.490 -0.023 1.492 0.263 -4.171 -0.143 -0.655 -0.021 -0.691 0.011 0.049

0.23 0.24 0.2 0.21 0.07 0.01 0.12 0.04 0.43 0.02 0.12 0.003 0.26 0.002 0.01

2.8 -1.0 -1.5 -4.2 -6.6 -3.4 12.2 6.0 -9.7 -7.0 -5.5 -7.3 -2.6 4.9 6.7

0.006 > 0.05 > 0.05 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.008 0.001 0.001

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ACKNOWLEDGMENT This work was financially supported by Abant Izzet Baysal University (BAP Project No: 2011.03.02.425).

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Received: Accepted:

09.06.2015 13.01.2016

CORRESPONDING AUTHOR Haluk Denizli Abant Izzet Baysal University Department of Physics 14280 Golkoy Campus, Bolu – TURKEY e-mail: [email protected]

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