Introduction

It is well known that the radionuclides 222Rn (radon) and 220Rn (thoron), and their decay products contribute ~ 50% of natural background dose to humans (UNSEAR 2000; UNSCEAR 2010). This fraction further increases in radium/thorium handling facilities and poorly ventilated buildings (IAEA 2005; WHO 2009). Being particulates in nature, decay products deposit into the lungs resulting in an increase in inhalation radiation exposure. This may lead to DNA damage resulting in induction of cancer for prolonged exposure scenarios (ICRP 2014; UNSCEAR 2000; WHO 2009). The enhanced lung cancer risk came out to be 8% (3–16%) per 100 Bq/m3 of radon concentration (Darby et al. 2005) in case–control studies conducted in Europe. Studies estimated in cold weather region relation to socio-economic factors including fuel poverty, and the role of thermally inefficient housing such inhalation risk in terms of lung cancer incidences and/or frequency of deaths. For example, Milner et al. 2014 estimated approximately 1100 deaths per year due to the exposure of 222Rn and 220Rn in the UK. Radon was also attributed to 300 instances of lung cancer in the dwellings of Ireland every year (Dowdall et al. 2017). Several such studies conducted in different regions worldwide have established the link between the indoor 222Rn and 220Rn exposure to the detrimental effects on human health (Krewski et al. 2005; Wang and Ward 2002).

Profiling of 222Rn, 220Rn and their decay concentration via short-term and continual measurements for both residential areas and occupational buildings has been an active research domain in last few decades. This has increased the level of understanding of research community and has futuristic implications in terms of reformed building designs for the policy makers. Ambient factors such as the effect of environmental characteristics, viz. seasons, ventilation, source-term, etc. affect the levels of 222Rn, 220Rn and their decay products considerably (Agarwal et al., 2019; Joshi et al. 2011; Hu et al. 2018; Kumar et al. 2020a, b; Kumara et al. 2017; Mishra et al. 2014; Omori et al. 2020; Semwal et al. 2019). This becomes more crucial for a country like India where the diverse conditions of climate, geology and socio-economy tend to intensify the role of studies in different regions culminating towards a meaningful scientific interpretation. This led to perform several studies and efforts to form a statistically sound database useful for the future applications (Bangotra et al. 2016; Karunakara et al. 2014, 2020; Prasad et al. 2018b; Meisenberg et al. 2016; Ramola and Prasad 2020; Sahoo et al. 2011). Geological conditions (Ramola et al. 2013), dwelling types (Sharma et al. 2018; Semwal et al. 2019) and seasonal variations (Bangotra et al. 2019; Prasad et al. 2016a) have been shown to be affecting the concentration levels of 222Rn/220Rn gases and decay products. In addition to the measurements, the efforts in terms of development of various numerical and analytical models (Sahoo et al. 2011; Shetty et al. 2020; Trilochana et al. 2020; Agarwal et al. 2014, 2015, 2020) has helped to enhance the knowledge relatable to different issues/contexts.

The Indian Himalayan belt has unique features regarding the geology, climate, neo-tectonic, faults and geophysical activities (Singh et al. 2016; Semwal et al. 2018, 2019; Prasad et al. 2016b; Ramola et al. 2013, 2016). Few recent studies on the measurements of indoor 222Rn/220Rn and decay products in Indian Himalayan belt highlighted the higher levels and associated the contributing factors with the observed data (Kandari et al. 2018; Prasad et al. 2018a; Prasad et al. 2008; Semwal et al. 2018, 2019). This work discusses the experimental measurements of indoor 222Rn, 220Rn and their decay product activity levels performed in Nainital District of Uttarakhand, India. Closeness to the seismic fault layer, unique rock and soil characteristics and cold climatic conditions necessitated the formulation of this campaign. Measurements were taken in 80 houses selected from the study region on defined pre-campaign basis. The results have been presented and the estimations of yearly averaged parameters have been made in this work.

Topography of the studied area

The Nainital District selected for the present study is situated in Kumaon region of Indian Himalayan belt, located between coordinates 80° 14'–78° 80' E and 29° 00'–29° 05' N with an elevation of 424–2084 m from the sea level (shown in Fig. 1). Due to its locations and pleasant climate conditions, Nainital remains an attractive place for tourists throughout the year. The Nainital hills represent the south eastern part of a strip of enechelon basins of the Krol belt, which stretches south eastward from Solan (Himachal) to Nainital (Uttarakhand). The rock mainly comprises of garnet-ferous mica-schits, which contains layers of quartzites, lenses of graphite schists and band of gneissic rocks (Sinha 1977). The southern limit of the Krol belt is delineated by the highly tectonized rocks of the Amritpur granites (Valdiya 1980), which have been brought up along Main Boundary Thrust (MBT). Quartzites, graphite, etc. are the prime sources of Uranium and Thorium.

Fig. 1
figure 1

Nainital District Map for measurements

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Details of the selected dwellings

A pre-survey for outdoor gamma radiation level (using ATOMTEX Gamma radiation survey meter) was conducted in the Nainital District. As a result, the studied region was segregated into two different zones as shown in Table 1. There were prominently three types of construction of the houses in the studied region. Type A represents the mud houses (mud and wood), type B stands for the stone houses (stone with cement plaster) and type C is cement houses (bricks and coated with cement). In this way, total 80 houses are selected for carrying out elaborative indoor measurements. The respective distribution of number of houses is shown in Table 1. A satellite-based GARMIN GPS; ETREX-10 monitor was used to determine the topography (latitude, longitude and altitude) of the selected houses.

Table 1 Distribution of houses with respect to the type of construction and gamma zone
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Materials and methods

Measurement of gas concentration

The 222Rn and 220Rn concentration was measured using “single entry face Pin-hole dosimeter” aboriginal designed by Bhabha Atomic Research Centre (BARC), Mumbai (Sahoo et al. 2013). Figure 2 shows a schematic illustration of the dosimeter used for the measurement. Two chambers of the dosimeter are detached by a central pinhole disc, which segregates 220Rn from 222Rn. Both cylindrical chamber measures 4.1 cm in length and 3.1 cm in radius. The foremost chamber determines 222Rn + 220Rn while the second chamber determines the 222Rn only. The gas accesses 222Rn + 220Rn chamber through a filter paper made of glass fiber (to cut-off the entry of their decay products), which is placed at the entry face and subsequently diffuses to 222Rn chamber. The LR-115 Type-II detector films of 3 cm × 3 cm are pre-fixed in these chambers to register the alpha tracks generated by 222Rn, 220Rn and their decay products. The unbiased electric field is maintained inside the chamber by metal powder coating for facilitating the uniform deposition of decay products. The comprehensive study of the measurement technique and its determination and validation of pin-hole dosimeter are reported by Sahoo et al. (2013).

Fig. 2
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Schematic diagram of single entry pin-hole dosimeter

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Determination of the concentration of decay products

The decay product of 222Rn and 220Rn was measured using Direct 222Rn Progeny Sensor (DRPS) and Direct 220Rn Progeny Sensor (DTPS), aboriginal made by BARC, Mumbai (Mishra et al. 2009a; b). The schematic diagram of the direct progeny sensing system is shown in Fig. 3. DRPS and DTPS are composed of LR115 detector foil and aluminized polyester film, arranged in a cassette. Selected polyester film thickness of 37 microns and 50 microns is used to record tracks in DRPS and DTPS due to the alpha particles emission from 214Po (α energy 7.69 MeV) and 212Po (α energy 8.78 MeV) for the detection of 222Rn decay products. Whereas the tracks generated by the 212Po (α energy 8.78 MeV) are registered to detect the 222Rn decay products. The recorded tracks are used to estimate the equilibrium equivalent concentration of the 222Rn and 220Rn decay products present in the surrounding air. A detailed description of the measurement technique, calibration and validation is found elsewhere (Mishra et al. 2010, 2009a, b; Mishra and Mayya 2008a, b). The dosimeter and DTPS/DRPS are commonly used in the numerous studies at various locations worldwide. (Bajwa et al. 2016; Singh et al. 2016; Mishra et al. 2014; Ramola et al. 2016).

Fig. 3
figure 3

The schematic diagram of direct 222Rn/220Rn progeny sensors (DRPS/DTPS)

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Evaluation of equilibrium factors

Equilibrium equivalent concentration (EEC) of 222Rn/220Rn is equal to that quantity of 222Rn/220Rn which is in secular equilibrium with its decay products giving equivalent potential alpha energy concentration (PAEC) for the decay products present in the environment. Thus, the equilibrium factor is defined as the ratio of EERC/EETC to the 222Rn/220Rn concentration in the atmosphere. The equilibrium factors for 222Rn (FRn) and 220Rn (FTn) can be calculated using Eqs. (1 and 2).

$$F_{R} = \frac{{{\text{EERC}}}}{{C_{R} }}$$
(1)
$$F_{T} = \frac{{{\text{EETC}}}}{{C_{T} }}$$
(2)

where CR and CT are the concentrations of 222Rn and 220Rn, respectively.

Assessment of total annual inhalation and annual effective dose

The total annual inhalation dose (D) and the annual effective dose (AED) due to exposure of indoor 222Rn, 220Rn and their decay products have been calculated using Eqs. (3–5) as given by UNSCEAR (2000).

$$D({\text{mSv}}/y) =\, \{ (0.17 \, + \, 9 \times F_{R} )xC_{R} + (0.11 + 40 \times F_{T} ) \times C_{T} \} \times 8760 \times 0.8 \times 10^{ - 6}$$
(3)
$${\text{AEDR}}({\text{mSv}}/y) =\, {\text{EERC}}({\text{Bq}}/m^{3} ) \times 8760h \times 0.8 \times 9{\text{nSv}}({\text{Bq}} \cdot h/m^{3} )^{ - 1} \times 10^{ - 6}$$
(4)
$${\text{AEDT}}({\text{mSv}}/y) =\, {\text{EETC}}({\text{Bq}}/m^{3} ) \times 8760h \times 0.8 \times 40{\text{nSv}}({\text{Bq}} \cdot h/m^{3} )^{ - 1} \times 10^{ - 6}$$
(5)

The dose conversion coefficients for the concentration of 222Rn and its decay products are 0.17 and 9, respectively; whereas 0.11 and 40 are the dose conversion factors for 220Rn and its decay products concentrations (UNSCEAR 2000). 0.8 is the indoor occupancy factor for 1 year exposure period (UNSCEAR 1993). 10–6 is the multiplication factor for unit conversion from nSv to mSv.

Results and discussion

Effect of seasonal variation on dose estimation

To observe the seasonal effects, dosimeters were deployed in the dwellings for a block period of four months (i.e. November–February, March–June and July–October), representing the three successive seasons (winter, summer, rainy) in a year. Seasonal classification has been done based on environmental parameters (temperature and relative humidity) and houses features (e.g. ventilation rate) which vary with the seasons.

The Q–Q plots (probability plots) are used to show the statistical distribution of data around their mean. If the data in the Q–Q plot lie on a straight diagonal line with minimal deviations, it indicates the normal distribution. Figure 4 gives Q–Q plots of 222Rn and 220Rn concentration during winter, summer and rainy seasons. In statistics, skewness is a measure of symmetry, or more precisely, degree of asymmetry. A distribution/data set is symmetric(mean = mode = median) if it looks the same to the left and right of the center point and Kurtosis is a measure of the combined weight of a distribution's tails relative to the center of the distribution. Positively Skewed Distribution is a type of distribution where the mean, median, and mode of the distribution are positive rather than negative or zero. As can be observed, data for 222Rn and 220Rn is showing a rightly skewed pattern for all seasons, e.g. winter (Sk = 0.52 and 0.86), summer (Sk = 0.35, Sk = 0.92) and rainy (Sk = 0.86; Sk = 0.48) season; indicating a log-normal distribution with right tail. EERC and EETC also followed the same pattern in all three seasons as observed for their parent gases. As 222Rn, 220Rn and their decay products concentration pattern follow a log-normal distribution, it is therefore recommended to representing the data with a geometric mean (GM, GSD) rather than arithmetic mean (AM ± SD). It is to avoid the biasing or inclination of the results towards a larger or smaller data value in the distribution.

Fig. 4
figure 4

Q–Q probability plots for 222Rn, 220Rn

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The statistical parameters such as range, geometric mean (GM), geometric standard deviation (GSD), relative standard deviation (RSD), skewness (Sk) and kurtosis (K) explaining the effect of seasonal variation on 222Rn, 220Rn and EERC-EETC concentration are tabulated in Table 2. GM for 222Rn were found to be varying in order of winter > summer > rainy. While the dispersion parameters (RSD and GSD) are seen to be comparable for different seasons. A similar distribution pattern can be noticed for EERC also. On the other hand, 220Rn and EETC profiles are quite dissimilar to each other as expected. This is because of 220Rn concentration profile is not observed to be affected by the existing ventilation rate in the region due to its short half-life. However, seasonal effect can be clearly seen on EETC due to the longer half-life of decay products. It is also evident from Table 2 that mean values in winter season for 222Rn, 220Rn and EERC-EETC concentration can be observed marginally higher than that of summer and rainy season. This indicates ‘offset ventilation effect’, i.e. smaller difference for winter and summer season because of ventilation patterns of cold regions, doors and windows, are opened during daytime in winters to allow warm sunlight inside dwellings and makes ventilation rates for summers (where air mixing is higher) and winters comparatively similar, which is a common occurrence in most Indian studies conducted in cold climates (Semwal et al. 2019). As water fills the pores of the soil, the exhalation potential decreases, influencing the emission from building materials in damp conditions; resulting lower concentration of 222Rn, 220Rn and their respective decay products in the rainy season than compared to winter and summer (Bangotra et al. 2019; Kaur et al. 2018; Semwal et al. 2019).

Table 2 Statistical data of seasonal variation for indoor 222Rn, 220Rn of Nainital district
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To further investigate the seasonal effect, pie charts for 222Rn and 220Rn are presented in Fig. 5. Figure 5 indicates that ~ 68% of the houses in the winter season showed the indoor 222Rn concentration higher than the global average (40 Bq/m3). While, this fraction reduces to ~ 41% and 17.5% in summer and rainy season, respectively, as expected. It can also be noticed that 222Rn concentration reach greater than 100 Bq/m3 (reference value, WHO) in 8.75% of the houses in winter season (only). For EERC, 63%, 36% and 20% the houses in the winter, summer and rainy season, respectively, were reported to have a concentration higher than its yearly average of 21 Bq/m3 (discussed in Implication from yearly data section).

Fig. 5
figure 5

The seasonal variation Pie chart of 222Rn and 220Rn

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On the other hand, 220Rn and EETC concentrations were found to be significantly higher than their global average of 10 Bq/m3 and 0.02 Bq/m3, respectively, in the studied region. For the case of 220Rn, 56% and 56% and 47% of the houses in winter, summer and rainy season, respectively, showed 220Rn concentration higher than its annual average of 34 Bq/m3 (discussed in Implication from yearly data section). While 2.5%, 1.25% of the houses were observed having 220Rn concentration higher than 100 Bq/m3 (arbitrary reference) in winter and summer season. EETC concentration was observed higher than its yearly average of 0.58 Bq/m3 (discussed in Implication from yearly data section) in 63%, 40%, 41% the houses in winter, summer and rainy seasons, respectively.

Influence of building material

The effects of construction material qualities (radioactivity content, grain size, porosity, etc.) on indoor concentration levels are well documented (Prajith et al. 2019). The effect of building material on the distribution pattern of 222Rn and 220Rn has also been studied and the annual data has been segregated based on the building types (Mud, Stone and Cement). Detectors were exposed in each of these homes during all three seasons, resulting in three readings for each dwelling over the course of a year. The distribution of 222Rn, 220Rn, EERC and EETC for this case has been illustrated in Fig. 6a, b and statistical parameters are given in Table 3.

Fig. 6
figure 6

a Box-Whisker plot of 222Rn and 220Rn variations. b Box-Whisker plot of EERC and EETC variations

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Table 3 Statistical data of indoor 222Rn, 220Rn and their progeny for different types of dwellings
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It can be seen from Table 3, the GM for 222Rn, 220Rn and their decay products (EETC, EERC) can be observed to be varying in order of mud > stone > cement. Relatively high exhalation rates and radium/ thorium content (source term) for the soil (especially mud construction) can be attributed to higher values of 222Rn, 220Rn, EERC, and EETC. Lower concentration values in the cemented house can be related to the fact that the surfaces are coated with cement, resulting in decreased radiation inward flux, which affects interior concentration (Singh et al. 2016; Semwal et al. 2018, 2019). Besides, bare flooring (especially in stone houses) contributes towards higher flux/concentration in the dwellings.

Pie charts for 222Rn and 220Rn are also shown in Fig. 7 to dwell the effect of building material further. As can be observed, ~ 63%, 36% and 32% of the mud, stone and cemented houses, respectively, showed 222Rn concentration higher than global average (40 Bq/m3). For EERC, 63%, 36% and 20% of the mud, stone and cemented houses, respectively, were reported having a concentration higher than its yearly average. For 220Rn, 84% and 61% and 48% of the mud, stone and cemented houses, respectively, showed concentration values higher than its annual average of 34 Bq/m3. Besides, 5% of the mud houses were observed with 220Rn concentration higher than 100 Bq/m3 (arbitrary reference). While EETC concentration was observed higher than its yearly average of 0.58 Bq/m3 in 90%, 62% of the mud and stone houses, respectively. For cemented houses, 20% of houses were reported to have EETC higher than 0.58 Bq/m3.

Fig. 7
figure 7

The seasonal variation Pie chart of 222Rn and 220Rn concentration

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Implication from yearly data

After examining the effects of seasons and construction materials on observed concentration values, the complete year’s data were aggregated for further analysis. Therefore, available 240 data points (three readings for each dwelling) were clustered for annual analysis. Figure 8 corresponds to the frequency distribution curve for 222Rn and 220Rn concentrations calculated annually. Table 4 lists statistical parameters such as concentration range, mean values, and standard deviation. Table 4 shows the values of annual concentration of 222Rn lies in the range of 6 to 99 Bq/m3 with GM (GSD) as 34 (1.65) Bq/m3 which is relatively lesser to global average value of 40 Bq/m3 (UNSCEAR 2000) while for 220Rn lies in the range of 6–120 Bq/m3 with GM (GSD) as 34 (1.78) Bq/m3 which is significantly higher than the world average value of 10 Bq/m3 (UNSCEAR 2000) and comparable to 222Rn concentration value in the study region. EERC and EETC lie within the range of 3–56 Bq/m3 with GM (GSD) as 21 (1.57) Bq/m3 and 0.03 to 4.43 Bq/m3 with GM (GSD) as 0.58 (3.41) Bq/m3, respectively. The EERC and EETC values are reported bit higher than the worldwide average value of 10 and 0.02 Bq/m3, respectively (UNSCEAR 2008, 2009).

Fig. 8
figure 8

Frequency Distribution curve for annual 222Rn and 220Rn concentration

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Table 4 Annual statistical data of 222Rn, 220Rn and their progeny
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A comparison of the current study’s findings with those of other research conducted in different Indian regions was also carried out. Table 5 shows that the average concentrations of 222Rn, 220Rn, EERC and EETC observed in the study region are comparable to those found in other recent investigations (Bangotra et al. 2019; Kumar et al. 2020a; Ramola et al. 2016; Semwal et al. 2019).

Table 5 Comparison of results of present study with recent studies conducted for Indian regions
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Estimation of equilibrium factor (EF) and dose assessment

The estimation of equilibrium factor for 222Rn, 220Rn and their decay product is essential for dose assessment. With the invention of new techniques (DTPS/DRPS) to measure 222Rn and 220Rn progeny, it is easier to calculate the EF for 222Rn and 220Rn. Hence EF and annual effective doses (due to 222Rn, 220Rn and their decay products) are predicted for the study region using Eqs. 1–5 and tabulated in the form of Table 6.

Table 6 Estimation of equilibrium factor and annual inhalation dose
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EF for 222Rn and its decay products was found to be varying from 0.28 to 0.77, yearly averaged value (i.e. 0.52) which is higher to the global reference value (0.4) (ICRP 1991; UNSCEAR 2000). On the other hand, EF for 220Rn and its decay products was estimated (0.02) is comparable to the reference value (0.02) (UNSCEAR 2000) but with significant uncertainty.

The average effective dose attributed to 222Rn and 220Rn was 1.33 (1.34) and 0.20 (2.7) mSv/y, respectively. Annual inhalation dose (AID) due to 222Rn, 220Rn and their progeny was found to be 0.85–3.93 mSv/y with GM (GSD) as 1.71 (1.76) mSv/y. Data from Table 6 indicate the significant contribution of 220Rn and its progeny to the annual inhalation dose.

Conclusion

This study presented the results of measurements of indoor 222Rn, 220Rn and their decay product activity levels in Nainital District of Uttarakhand, India. Measurements were taken in selected 80 houses situated at different altitudes. Single entry-based pin hole dosimeter and progeny sensors have been employed for the measurement of gases and decay products, respectively. The results have been interpreted on the basis of difference in building materials (mud, stone and cement) and seasonal variations (winter, summer and rainy). These were further utilized for deducing yearly averaged parameters and for performing dose calculations. Q–Q plots made for seasonal interpretations implied that 222Rn and 220Rn and their decay products follow a positive skewness towards the right tail. Wintertime values for 222Rn, 220Rn and EERC-EETC concentration were found to be higher than the corresponding values for summer and rainy season. These levels varied in the order of mud > stone > cement when the data were segregated on the basis of difference in building materials. Estimated yearly averaged equilibrium factor for the study region came out to be 0.52 and 0.02 for 222Rn and its decay products and 220Rn and its decay products, respectively. However large dispersion for the factor has been seen in the study region. Annual inhalation dose (AID) due to 222Rn, 220Rn and their progeny was found to be 0.85–3.93 mSv with GM (GSD) as 1.71 (1.76) mSv. The present study showed the significant contribution of 220Rn and its progeny to the annual inhalation dose. It was also seen that in most of the mud houses, gases and their decay product’s concentration remained significantly higher than their respective global average in the winter season. The concentration range, mean values were also compared with the other studies conducted in nearby regions. The results of this study follow the general conclusions of similar studies conducted in nearby region.