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Article

Estimation of Indoor 222Rn Concentration in Lima, Peru Using LR-115 Nuclear Track Detectors Exposed in Different Modes

by
Patrizia Pereyra
1,*,
Cesar J. Guevara-Pillaca
1,2,
Rafael Liza
1,3,
Bertin Pérez
1,4,5,
Jhonny Rojas
1,
Luis Vilcapoma L.
1,
Susana Gonzales
6,
Laszlo Sajo-Bohus
7,8,
María Elena López-Herrera
1 and
Daniel Palacios Fernández
1
1
Departamento de Ciencias, Pontificia Universidad Católica del Perú, Lima 15088, Peru
2
Instituto Geológico, Minero y Metalúrgico INGEMMET, Lima 15034, Peru
3
Facultad de Ingenieria, Universidad Tecnológica del Perú, Lima 15046, Peru
4
Rad Elec Inc., Frederick, MD 21704, USA
5
Investigación, Desarrollo e Innovación, Anphysrad SAC, Lima 15074, Peru
6
Instituto Peruano de Energía Nuclear IPEN, Lima 15034, Peru
7
Nuclear Laboratory, Universidad Simón Bolívar, Caracas 1080 A, Venezuela
8
Alba Regia Technical Faculty, Óbuda University, 800 Szekesfehervar, Hungary
*
Author to whom correspondence should be addressed.
Atmosphere 2023, 14(6), 952; https://doi.org/10.3390/atmos14060952
Submission received: 18 April 2023 / Revised: 15 May 2023 / Accepted: 24 May 2023 / Published: 30 May 2023
(This article belongs to the Special Issue Atmospheric Radon Concentration Monitoring and Measurements)
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Versions Notes

Abstract

:
Radon is the main source of natural radioactivity, and its measurement is considered extremely important in radioprotection, given its relationship with the occurrence of lung cancer. In the last two years, measurements of this radioactive gas were carried out in Lima considering a grid of 5 km 2 and the population density to determine the number of measurements to be carried out. Cellulose nitrate nuclear track detectors exposed in bare mode and diffusion chamber mode were used to estimate 222 Rn concentrations. In diffusion chamber mode, non-commercial monitors and commercial monitors were used. The monitoring results are presented for 43 districts of the Lima Province whose population is approximately ten million inhabitants occupying a total area of 2655.15 km 2 . Measurements were made obtaining an average concentration of 49 Bq·m − 3 using bare detectors and 66 Bq·m − 3 using non-commercial diffusion chambers. Average concentrations obtained by both detector exposure modes were below the maximum concentration recommended by the WHO. A radon ( 222 Rn) map was also obtained as a visual representation of the 222 Rn levels in the Lima province using inverse distance weighting (IDW) interpolation.

1. Introduction

Human beings are continuously exposed to ionizing radiation. The majority of the population considers that this has its origin only artificially, when in reality, the greatest contribution comes from nature. Human beings receive radioactive natural doses mainly through (i) gamma radiation, (ii) 222 Rn and 220 Rn with their progenies (present in the air), and iii) radioisotopes present in water and food. Of these three sources, radon isotopes ( 222 Rn and 220 Rn, with the latter having sometimes having been historically named ‘thoron’) together with their short-lived progeny, mostly alpha emitters, account for just over 50% of the natural radiation contribution received by a human being [1]. Radon gas has a naturally occurring radioisotope 222 Rn from the 238 U chain, which has a half-life of 3.825 days and emits alpha particles of 5.48 MeV; its predecessor is 226 Ra. 222 Rn is present in the air and its progeny ( 218 Po, 214 Po), which are short-lived alpha emitters, adhere to environmental dust particles to form aerosols that enter into the human respiratory tract. When these aerosols are inhaled and attached in the tracts, they can cause damage to the lungs and respiratory tract by altering DNA strands. Various epidemiological studies indicate the relationship between radon and the incidence of lung cancer [2,3,4,5,6]. It has been reported that the risk of lung cancer increases linearly with the concentration of radon and that there is no threshold beyond which the effect is harmless [7]. Initially, the risk of lung cancer was associated exclusively with the high risk that miners are exposed to, but in 1984, a house was incidentally found with 222 Rn levels close to 100,000 Bq·m − 3 , which is comparable to the previous case [8]. From that moment on, 222 Rn measurements inside homes took importance [9]; several works have reported measurements, monitoring, and 222 Rn maps of countries and continents using different methodologies and scopes [10,11,12,13,14,15]. Radon in houses comes not only from the subsoil, but also from construction materials, coatings, pipes, and domestic water, and its concentration is affected by atmospheric and ventilation factors.
Monitoring systems have been used to evaluate the indoor radon concentration and, in some cases, relate its presence to the risk of developing neoplasia [16]. Additionally, they are used for several purposes, such as to identify radon priority areas, for identifying areas to prioritize mitigation, for developing policies, and for programs to reduce indoor radon air levels. These applications may include measures such as promoting the construction of low-radon-emitting homes and implementing building codes, which can also help to understand the relationship between the distribution of radon gas and its relationship with other environmental and geogenic factors.
Long-term and large-scale measurements with solid-state nuclear track detectors (SSNTDs) have been carried out in many countries to identify dwellings with high 222 Rn levels [17,18,19]. Currently, CR-39 (poly allyl glycol carbonate) detectors are the most widely used SSNTDs for indoor 222 Rn measurements [20,21,22,23,24]. However, in this work, we chose the non-strippable LR-115 type 2 detector due to its low cost and its suitability for use in both bare mode [25,26,27] and inside a diffusion chamber [28,29].
The LR-115 detector exposed in bare mode registers alpha tracks from airborne concentrations of 222 Rn and 220 Rn and their alpha emitting progeny, but they do not detect the alpha activity deposited on the detector [30]. This mode of exposure leads to large uncertainties in the estimation of the 222 Rn concentration, since the same 222 Rn concentration value can be associated with different concentrations of its progeny depending on various atmospheric factors [31]. This is also further complicated if 220 Rn and its non-equilibrated progeny are present. That is why its calibration in the laboratory may not be adequate for indoor conditions. The determination of 222 Rn concentrations strongly depends on equilibrium factor, which is estimated between 0.2 and 0.7 for typical indoor conditions [1]. Thus, the value of the equilibrium factor must be within this range when calibrating bare detectors.
The estimation of 222 Rn concentrations using diffusion chambers is direct and univocal, and its calibration is easier than bare detectors, because only 222 Rn is registered. These monitors are less sensitive to environmental conditions [32], and exposure time could be larger than in the case of bare detectors. Among the disadvantages, we can mention its higher cost and its higher probability of getting lost compared to bare detectors mounted on the wall. Generally, diffusion chambers limit the ingression to its internal volume for aerosols and progeny water vapor, thereby discriminating 222 Rn from 220 Rn.
The objective of this work was to determine the indoor 222 Rn concentrations in Lima, Peru using non-strippable LR-115 type 2 detectors exposed in bare mode and inside diffusion chambers. Results for each mode were compared between each other and with the reference level recommended by the WHO and IPEN [29,33].
To meet this objective, a survey was carried out, where the LR-115 detector exposed in bare mode was used as a 222 Rn monitor. To complement the previous statement, two types of 222 Rn monitors based on LR-115 detectors and diffusion chambers were also used: one commercial and the other homemade. The 222 Rn map was also obtained as a visual representation of 222 Rn levels in the Lima province.

2. Materials and Methods

2.1. Description of Study Location

Lima, capital of Peru, is located on the central and western coast of South America. Its location is on a desert strip that extends from north to south between the Pacific Ocean and the Andean mountain range. The city mostly lies on flat terrain within the valleys of the Chillon, Rimac, and Lurin rivers, which drain into the Pacific Ocean. The Chillon River is located to the north of Lima, the Rimac River in the center, and the Lurin River to the south of the capital city. These rivers have played a vital role in the erosion and transportation of unconsolidated materials caused by the denudation of hills along their courses. The geology of the studied area has been described in a previous work [34]. The study area, also known as Metropolitan Lima, is divided into districts, which were grouped into 4 zones for this study: Lima Centro, Lima Este, Lima Norte, and Lima Sur [35].
Lima Centro is located in the metropolitan area center and comprises sixteen districts with an area of 147.73 km 2 and a population of 2,155,132 inhabitants [36]; it is the area with the highest population density of Lima. The soils in this zone have been formed mainly by alluvial deposits brought by the Rimac River. In addition, the water table is predominantly deep, and there are organic deposits that increase soil compaction in this area. There are some rocky outcrops in the zone that have been covered by silty and clayey materials. The conglomerate in this zone contains gravelly material that varies in density from loose to compact. This material is also mixed with layers of medium to fine sand, silt, and clay with a small amount of fine particles [37].
Lima Este is located in the eastern part of the metropolitan area and comprises eight districts with an area of 814.25 km 2 and a population of 2,937,764 inhabitants. In this zone, there are colluvial deposits due to the effect of gravity (e.g., San Juan de Lurigancho). The displacement of this eroded material has been very slow, but due to urban growth, there is the additional presence of fill material deposits formed by borrowed material, wastes, and debris that come from other zones [37]. The soil in this zone also presents alluvial deposits characterized by the presence of rounded cobblestone material.
Lima Norte is located in the northern part of the metropolitan area and comprises eight districts with an area of 857.26 km 2 and a population of 2,917,414 inhabitants. Colluvial and alluvial deposits over this zone have been covered over time by shallow stratas of fine granular and clayey materials [37]. These materials traveled across the Chillon River, starting their erosive detachment from the Nevado de la Viuda and surrounding areas.
Lima Sur is located in the southern part of the metropolitan area and comprises eleven districts with an area of 845.92 km 2 and a population of 2,901,224 inhabitants. This zone has eolian deposits [38] caused by strong winds from the south, and it is predominantly dry. Lima Sur also has marine deposits associated with its topography. These types of deposits increase the level of soil porosity, which can have an impact on indoor 222 Rn concentration.
Districts with low population densities have not been considered a priority in the surveys. The study area covers a total surface of 2819.26 km 2 , with a currently estimated population of 10,178,810 inhabitants [39].
Lima is one of the few capitals in the world located on the coast of a desert area.The monitoring area is shown in Figure 1. The climate in Lima is classified as hot arid (BWh) according to the Köppen–Geiger classification [40]. Lima is characterized by very low rainfall (annual average 7 mm); however, its relative humidity is quite high (reaching 99% in winter), and there is persistent cloudiness. Typically, temperatures range from 12 to 30 ° C [41].

2.2. Site-Selection Criteria

A similar site-selection criterion to those carried out in other countries [42,43,44,45] was adopted. In order to ensure that the sampling accurately represented the entire population under investigation, stratified sampling was employed through the use of grids, wherein a random sampling was conducted within each grid. As per [44] findings, this methodology is an effective means of ensuring sample representativeness. The criterion consisted of sectioning each district in a grid system of 5 km 2 , where a minimum number of dwellings or working places per grid was chosen according to Table 1.
Since the study was carried out for research purposes by our research group (GITHUNU—PUCP [Available online: https://investigacion.pucp.edu.pe/grupos/githunu/(accessed on 7 May 2023)]), sampling dwellings were member’s homes of our community university, who voluntarily carried some monitors to their homes after internal awareness campaigns (e.g., website, videos, digital media) in order to motivate participation. They filled out a google form with information about monitoring places (Appendix A).
It is worth mentioning that this monitoring was the first of its kind conducted in our country on a medium regional scale. Since the focus of measurements was to determine the 222 Rn concentration indoors, a minimum number of detectors was established per district based on the population density in each district [29]. This criterion was assumed based on available resources for 222 Rn concentration measurements. Although no sampling dwellings were planned for type A districts, some measurements were made thanks to the participation of volunteers.
To calculate the minimum number of dwellings to sample in each district, the habitable area of each district was divided by the grid area. The data presented in Table 2 corresponds to the estimated population.
In addition, this monitoring was carried out from 2016 to 2019, where, in some sampling dwellings, only one monitor was placed; it represented a single value of 222 Rn concentration. In the other sampling dwellings, where there was more than one monitor; the average 222 Rn concentration was taken into account.

2.3. Methods of Measurements

Indoor 222 Rn levels were measured by using LR-115 detectors in two modes: bare and diffusion chambers. Detectors in bare mode recorded the total radon concentration ( 222 Rn, 220 Rn, and their progeny), and two diffusion chambers—a commercial DPR monitor (ALGADE’s laboratory, France) and a home-made plastic monitor, referred to hereafter as G2—registered 222 Rn level concentrations in air. The measurements were carried out using 508 bare detectors, as well as 140 G2 and 98 DPR monitors.

2.3.1. Bare LR-115 Detectors

Bare mode detectors are low-cost and easy-to-use. This exposure mode was employed for indoor measurements, where detectors were affixed onto a plastic plate and positioned on the walls at a height of roughly 1.5 m above the floor, with their sensitive surfaces facing the air.
It has been reported that concentrations of 232 Th are generally low in the building materials that are commonly used in Lima households [46]. However, even in walls with significant thorium content, 220 Rn exhalation may be reduced due to paint layers covering most studied dwellings. In any case, 220 Rn concentrations rapidly decrease with distance from the wall due to their short half-lives, which reach only about 10–15 cm from the wall under low ventilation rates [47]. This results in a reduction in 220 Rn concentration in its effective volume to around 25–30% of its value very close to the wall. Furthermore, given that most exhaled 220 Rn atoms decay in close proximity to the wall, it is anticipated that nearly all of their progeny will deposit on the wall before decaying in the air, except for 216 Po. Experimental findings have confirmed this approach, since a very low 220 Rn equilibrium factor was observed near the wall [48]. Therefore, a negligible contribution of the 220 Rn progeny to the detector track density is expected. In summary, the primary contribution to track density in the LR-115 bare detector placed on a wall is from 222 Rn and its non-equilibrated progeny. However, in cases of walls with high thorium content and a permeable paint layer, the 220 Rn contribution may be significant, especially considering its doubling due to the daughter 216 Po decaying practically at the same location and time as its parent.
If the interference of 220 Rn and its progeny can be considered negligible, the 222 Rn calibration factor for bare LR-115 detectors ( K B ) will depend on the partial sensitivity for each species k B and the 222 Rn equilibrium factor F R n , which can be expressed as [49]:
K B = k B · ( 1 + 2 · F R n )
By considering a partial sensitivity of 0.02 tracks·cm − 2 · Bq − 1 · d − 1 · m 3 [50] and an equilibrium factor within the range of 0.2 to 0.7 [51], a mean calibration factor of (0.038 ± 0.005) tracks·cm − 2 · Bq − 1 · d − 1 · m 3 was derived using an equilibrium factor of 0.45, where the lower and higher uncertainty limits were calculated using 0.2 and 0.7 respectively. This value closely matches the experimentally obtained calibration factor in our laboratory while taking into account their respective experimental uncertainties.
For the bare mode exposure, an LR-115 detector of size 15 × 15 mm 2 was fixed to a plastic mica sheet; this 222 Rn monitor was mounted on an internal wall of the dwelling for measurements. Each volunteer was provided with an envelope containing two monitors of this type and an information guide on how to position them for measurements. Detectors were exposed for 8 to 12 weeks in different seasons according to previous studies [29], and research was carried out by us [52]. The lower limit of detection (LLD) was 15 Bq·m − 3 [53].

2.3.2. Diffusion Chambers

The diffusion chamber of the G2 monitor is a cylindrical container composed of a white polypropylene double-walled container of 100 mL internal volume. A 1.5 × 1.5 cm 2 LR-115 detector was fixed inside the chamber. It was expected that the double-wall design would mitigate the influence of environmental conditions, particularly air temperature. The 222 Rn and 220 Rn atoms enter the diffusion chamber through threads between the cup lid and the body chamber by diffusion. It was expected that, due to the short half-life of 220 Rn (55.6 s), a few atoms would enter the monitor and lead to a negligible 220 Rn concentration in its effective volume. The transmission factor was experimentally determined, and this hypothesis was confirmed. Therefore, the LR-115 placed inside the monitor only recorded alpha particles of 222 Rn and its progeny. A calibration factor of (0.0238 ± 0.0007) tracks·cm − 2 · Bq − 1 · d − 1 · m 3 was used to convert the track density to 222 Rn concentration [54]. The exposure time of G2 monitors was approximately twelve weeks. LLD was expected to be approximately 20 Bq·m − 3 [53].
Commercial DPR monitors were also used for 222 Rn monitoring. They were based on an LR-115 detector encapsulated in a sealed conductive plastic half-dome. The 222 Rn enters into the detection volume by diffusion through a specific membrane, which prevents 220 Rn, as well as radioactive aerosol particles, from entering and affecting the measurement. An OFF/ON system allows for the establishment of the measurement period. DPR recommends a minimum exposure period of two months for indoor measurements, which permits a 222 Rn activity of 20 Bq·m − 3 to be measured properly [55]. DPR monitor was exposed during a period of approximately twelve weeks.
After exposure, DPR monitors were sent to ALGADE’s laboratory for analysis, and results were reported with a global uncertainty of ±2 σ . LR-115 detectors from bare and G2 monitors were etched at PUCP Nuclear Tracks Laboratory using a 2.5 N NaOH solution at a temperature of 60 ° C for 90 min and rinsed using a magnetic stirrer. The track counting process of the LR-115 detectors was carried out with the POLITRACK reading system [Available online: https://miam.it/prodotti/politrack/ (accessed on 7 May 2023)].

2.4. 222 Rn Concentration

The 222 Rn concentration in Bq·m − 3 was calculated according to the following formula:
C i , R n = ρ K i · t i = B , G 2
where C i , R n is the 222 Rn concentration (Bq·m − 3 ), ρ is the effective track density (tracks·cm − 2 ) that is calculated by subtracting the background density from the total density, t is the exposure time (days), and K i is the calibration factor for the bare (B) detector or diffusion chamber G2 (G2).

2.5. Statistical Treatment of Data and Mapping

Indoor 222 Rn concentration often exhibits a skewed distribution with a long tail to the right, and log-normal distribution is usually used to model it [56]. The Anderson–Darling test was applied to evaluate the normal distribution of the logarithmically transformed data. The analysis of variance (ANOVA) was utilized to make comparisons between the four defined zones and other variables. The OriginPro 2023b software was employed.
The inverse distance weighted (IDW) interpolation model was utilized to map 222 Rn concentrations. IDW interpolation is based on the principle that nearby measured points have a stronger influence on the estimation of unknown values. This method calculates a weighted combination of known points, where the weight is a function of the inverse distance. To predict a value for an unmeasured location, IDW takes into account the measured values surrounding the prediction location. Points closer to the prediction location are given greater importance than those farther away [57].
Another powerful interpolation is the Kriging model, which can also be effectively utilized to map 222 Rn concentration levels. By employing Kriging, we can create accurate and detailed maps of 222 Rn concentration, because it takes into account the spatial autocorrelation of 222 Rn data, thereby capturing the relationship between nearby measurements and generating predictions for unobserved locations. This model also considers both the observed data and the underlying spatial structure, which results in reliable estimates of 222 Rn levels throughout the study area [58].
Both models were implemented by using ArcGISPro 2022 software.

3. Results and Discussion

3.1. Results for Bare Detectors

The distribution of indoor 222 Rn concentration is typically skewed, and the logarithmic transformation can be useful for assessing the risk of exposure to 222 Rn. Figure 2a depicts the indoor 222 Rn concentration distribution. As anticipated, the indoor 222 Rn concentrations followed a log-normal distribution (shown by the continuous line in figure Figure 2a). The Q–Q plot in Figure 2b represents the natural log-transformed 222 Rn concentration, which followed the expected trend. The Anderson–Darling test, with a 95% confidence level, suggests that the observed distribution matches a normal distribution, since the p-value is greater than 0.05. Approximately 2.27% of the measurements exceeded the reference level of 200 Bq·m − 3 established by IPEN.
Basic descriptive statistical parameters and other parameters such as the geometric mean (GM), geometric standard deviation (GSD), minimum (Min), maximum (Max), median, population density, and number of dwellings are reported in Table 3. In addition, the ANOVA analyses are listed in Table 3.
The average 222 Rn concentration in the total number of dwellings using bare detectors was (49 ± 2) Bq·m − 3 . This value is above the world average 39 Bq·m − 3 , but below the WHO recommended reference level of 100 Bq·m − 3 [29]. We also determined that the values of the bare monitor in 5% of the measured dwellings were lower than the detection limit.
Table 3 also shows the ANOVA analysis on the 222 Rn concentration data for different zones. In this case, the obtained F-value indicates that there were differences in 222 Rn concentration between the zones, and the small p-value suggests that these differences are statistically significant. Therefore, we can infer that the zone has a significant impact on the 222 Rn concentration, and these findings can be attributed to unique factors associated with each specific zone, including the geological characteristics commonly examined in previous studies [34]. Additionally, Ref. [34] also identified high levels of 222 Rn concentrations near alluvial deposits such as Lima Centro, which potentially corroborates the findings obtained in this study.
In this study, we also analyzed the 222 Rn concentration related to the construction age, vehicular traffic, construction materials, wall painting, and floor type, as shown in Table 4.
Basic descriptive statistical results from Table 4 suggest that different variables were associated with higher levels of 222 Rn in dwellings. For instance, dwellings over 40 years old had the highest GM of 222 Rn concentration, which may be due to the fact that older dwellings are more likely to have cracks and other openings that can allow 222 Rn to enter. Similarly, dwellings located near highways may have higher 222 Rn levels due to the high vehicular traffic that produces vibrations leading to larger 222 Rn exhalations from soils [59]. In the construction materials group, we found that dwellings with other materials had a slightly higher 222 Rn concentration. It suggests that, for wall painting, the dwellings with older wall painting had higher 222 Rn concentrations. This finding is connected to the fact that older painting may have become worn or damaged over time, thereby no longer providing a barrier to prevent the 222 Rn exhalation from walls. Finally, the tapestry had higher concentrations, because it has more porosity compared to more dense materials such as cement.
On the other hand, ANOVA analyses suggest that only the construction age variable showed a statistically significant effect on the 222 Rn concentration. This could indicate that cracks and other openings that can allow 222 Rn to enter due to the construction age are statistically significant factors, as previous studies have also observed a correlation between indoor radon concentration and the presence of cracks [60].

3.2. Results for Diffusion Chambers

The 222 Rn concentration measured with G2 monitors inside dwellings from September 2017 to December 2018 gave a mean value of (66 ± 2) Bq·m − 3 . In addition, it was determined that 40.7% of the measurement dwellings presented values lower than the detection limit of the G2 monitor (<20 Bq·m − 3 ), and 6.4% gave values within the range of the level of action for chronic exposure to 222 Rn in dwellings (200–600 Bq·m − 3 ) indicated in the Peruvian National Regulation of Radiological Safety (D.S. No. 009-97-EM) [33]. The distribution of the obtained values is presented by means of a histogram in Figure 3. Results of the statistical parameters of indoor 222 Rn concentration are shown in Table 5.
Apparently, high humidity conditions and temperature changes are known to potentially cause condensation at 95% humidity and a temperature of 23 ° C. In certain months, the humidity in Lima reaches 99%, which could have caused this effect (citation), along with condensation effects and the dew point [61]. This would mainly affect the filtering membrane of the DPRs, as the manufacturer recommends avoiding condensation. Another possible factor that could have affected the response of the DPR membrane was the high particulate matter in the city of Lima [62], which would have hindered the proper functioning of the filtering membrane. In the case of the G2 monitors, their double-walled structure with thermal insulation effects may have helped reduce the influence of condensation and allow the passage of 222 Rn without major issues.
Based on the findings of our study, it is evident that the ALGADE monitors did not perform optimally under the high air humidity conditions of Lima province. Only five monitors (<6%) yielded results above their detection limit of 20 Bq·m − 3 [63]; therefore, the results obtained with ALGADE monitors were not considered for the analysis and interpretation data.These findings underscore the importance of carefully selecting monitoring equipment that is capable of reliably and accurately measuring environmental parameters under a wide range of local conditions. As such, it is imperative that future efforts are directed towards developing technologies that can withstand ’extreme’ conditions and provide accurate data for informed decision making.

3.3. Comparing the Bare Mode Detector and the G2 Monitor Results

The average 222 Rn concentration per zone using bare mode detectors and G2 monitors is depicted in Figure 4.
The results were compared, and we found that the 222 Rn concentrations measured by G2 monitors were higher than those obtained by bare mode detectors. The G2 monitor measurements showed a range of (45 ± 7) to (94 ± 8) Bq·m − 3 , whereas the average bare mode detector values ranged from (37 ± 5) to (57 ± 7) Bq·m − 3 .
In order to evaluate the statistical difference between both bare mode and G2 monitor, the Mann–Whitney U test was used. The results of this test indicate that both modes were significantly different (p = 0.0004) with a 95% confidence level. These results may be due to two reasons: either the 220 Rn and its progeny contributed to the density of tracks recorded in the bare detector, or the assumed equilibrium factor was higher than the actual value. These factors should be taken into account in future investigations.
Therefore, it can also be concluded that the average 222 Rn concentrations in both cases were below the reference level (200 Bq·m − 3 ) established by the IPEN. Besides, the contributions of 220 Rn and its progeny to the track density detected in the exposed bare mode detectors were low. As such, any subsequent assessments of inhalation dose estimations should take this into account. Another possible cause of the fact that the concentrations measured with the bare monitors were consistently lower than those obtained with the G2 monitors is that the actual equilibrium factor may have been lower than the assumed value used to calculate the calibration factor using Equation (1).

3.4. 222 Rn Map in Lima

This section discusses the 222 Rn map of Lima province, which is a visual representation of the 222 Rn levels. The map is based on data from 222 Rn measurements taken in various locations throughout Lima province using bare mode outcomes. This map represents an important tool for identifying areas of high 222 Rn levels, which can pose a health risk to the residents of the Lima province. According to this and depending on the high 222 Rn level, there are various mitigation methods to control those levels. Thus, color 222 Rn maps indicating the indoor 222 Rn levels in the Lima province were elaborated using IDW and the Kriging model. Both results are shown in Figure 5 and Figure 6, respectively. These interpolation models use measurement results of the 222 Rn concentration in known locations to estimate the 222 Rn concentration in locations where no measurements have been taken.
The ranges of 222 Rn concentrations required to obtain the map vary depending on the regulations and recommendations of each country or public health authority. In this study, the following ranges were used [64,65,66,67]:
  • Low: less than 50 Bq·m − 3 ;
  • Moderate: between 50 and 100 Bq·m − 3 ;
  • High: between 100 and 200 Bq·m − 3 ;
  • Very high: greater than 200 Bq·m − 3 .
In Figure 5 and Figure 6, the legend on the right side of the map shows the color codes and the corresponding 222 Rn levels in Bq·m − 3 , and the 222 Rn concentration results represent the mean value. In the Lima Sur zone, both models depict that the distribution was clearly lower than 100 Bq·m − 3 , while the Lima Centro zone depicts the highest 222 Rn levels. It should be emphasized that, although the map results confirmed the trend of the statistical data analysis, they allow the spatial visualization of the 222 Rn levels of each zone.
The cross-validation results for the IDW and Kriging models indicate that neither model provided a good fit to the residential 222 Rn concentration data, as shown in Table 6. Both models presented negative values of R 2 , which indicates that the model could not well explain the variability in the data. Furthermore, the MAE, RMSE, and RMS values indicate that both models have a high error in the prediction of the 222 Rn concentration. A possible explanation for these results is that the density of measurements was not uniform throughout the study area. That is, some areas may have had more measurements than others, which may have affected the accuracy of the models. For example, if there are areas with fewer measurements, the model may have difficulty estimating the 222 Rn concentration in those areas. Therefore, the models could be improved if more measurements are made in the whole study area. It should be noted that these results are preliminary, and further measurements are needed to fully assess the accuracy of the IDW and Kriging models. In addition, there are other factors, such as geology and soil characteristics, that can also affect the 222 Rn concentration and must be considered in modeling. Therefore, it is recommended to continue taking measurements and improving the models to provide a more accurate map of the 222 Rn concentration in the study area.
The indoor 222 Rn map of Lima is not a finalized map, and it will be improved when new measurement data is obtained.

4. Conclusions

Indoor 222 Rn concentrations were measured in 508 dwellings using 508 bare detectors. Simultaneously and randomly, in 140 and 98 of them, G2 and DPR monitors were used, respectively. Using bare mode, the geometric mean was 49 Bq·m − 3 and, for the G2 diffusion chambers, the GM was 66 Bq·m − 3 ; both of them were under the action level. In the case of bare detectors, 88.98% of the devices recorded measurements below 100 Bq·m − 3 , which includes those that reported values below the detection limit. A total of 9.84% of the detectors registered measurements between 100 and 200 Bq·m − 3 , while only 1.18% registered concentrations above 200 Bq·m − 3 .
On the other hand, in the case of the G2 monitors, 63.05% of the devices recorded measurements below 100 Bq·m − 3 . A total of 7.17% of the monitors recorded measurements between 100 and 200 Bq·m − 3 , while only 9.78% of the monitors recorded values above 200 Bq·m − 3 . Most of the 222 Rn concentration results reported by the ALGADE laboratory where below the DPR’s detection limit.
Bare detectors follow a log normal distribution, which are in contrast to G2 diffusion chamber distributions; this is due to the low number of measurements. From the results obtained by measuring 222 Rn concentrations using bare detectors and G2 detectors, we can infer that bare detectors mainly register the contribution of 222 Rn, with the contribution of 220 Rn and its progenies are recorded as negligible. In the case of G2 detectors, they register the concentration of 222 Rn and the progeny that is produced inside the chamber, and they meet the requirements of the critical angle and appropriate energy range to produce tracks.
The concentration of 222 Rn was closely related to the zones, as evidenced by the strong correlation with the geological characteristics. It was also evidently related to other variables such as the construction age. This factor seems to indicate that the proper maintenance of dwellings (without cracks or fissures) contributes to lower levels of 222 Rn inside dwellings.
The first 222 Rn map in Peru, specifically in its capital Lima, has been created. Although the results are not conclusive, it can be said that the detected levels do not pose a high risk to the population, since the average 222 Rn concentration values for both exposure modes were below the reference level suggested by WHO. Further measurements are necessary to study other regions. These findings highlight the importance of monitoring indoor 222 Rn levels and implementing proper dwelling maintenance practices to reduce exposure.
Based on the obtained results, it is evident that LR-115 detectors, whether used in a bare mode or diffusion chamber mode, exhibit good performance and can be employed in this type of study, given their low-cost and ease-of-use. They are suitable for laboratories conducting research on related topics.

Author Contributions

Conceptualization, P.P., M.E.L.-H. and D.P.F.; methodology, P.P., M.E.L.-H., L.V.L. and D.P.F.; software, B.P., J.R. and R.L.; validation, L.S.-B., B.P., J.R. and C.J.G.-P.; formal analysis, P.P., D.P.F., L.S.-B., B.P., J.R. and R.L.; investigation, P.P., M.E.L.-H., D.P.F., C.J.G.-P. and L.V.L.; writing—original draft preparation, P.P. and M.E.L.-H.; writing—review and editing, D.P.F., B.P., J.R., C.J.G.-P., R.L. and L.S.-B.; supervision, P.P., M.E.L.-H., D.P.F. and S.G.; project administration, P.P.; funding acquisition, P.P. and S.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by INNOVATE PERU, grant number 120-PNICP-PIAP-2015 (“Desarrollo de un sistema de monitoreo de Rn-222 ambiental mediante la técnica de huellas nucleares, en la ciudad de Lima, Perú”); by the IAEA, grant number PER 9024 (“Radon levels in dwellings in three regions of Peru and creating radon maps for policy makers”); and by CienciActiva-CONCYTEC 2017. We also express gratitude to Departamento de Ciencias—PUCP for funding the open access option for our paper publication.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author (P.P.) upon reasonable request.

Acknowledgments

All the authors would like to express their gratitude to the community university, to the volunteers who allowed this work to be carried out, and to IAEA for technical support.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Appendix A

Atmosphere 14 00952 g0a1

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Atmosphere 14 00952 g001 550
Figure 1. Map of the studied areas in Lima province. The scale only refers to the left map.
Figure 1. Map of the studied areas in Lima province. The scale only refers to the left map.
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Atmosphere 14 00952 g002 550
Figure 2. (a) Log−normal distribution of the indoor 222 Rn concentration. (b) Q–Q plot of natural log-transformed 222 Rn concentration.
Figure 2. (a) Log−normal distribution of the indoor 222 Rn concentration. (b) Q–Q plot of natural log-transformed 222 Rn concentration.
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Atmosphere 14 00952 g003 550
Figure 3. 222 Rn concentration distribution using the G2 monitors.
Figure 3. 222 Rn concentration distribution using the G2 monitors.
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Atmosphere 14 00952 g004 550
Figure 4. Comparing average 222 Rn concentrations per zone obtained by using the bare mode and the G2 monitor.The numbers of dwellings are displayed above the bars.
Figure 4. Comparing average 222 Rn concentrations per zone obtained by using the bare mode and the G2 monitor.The numbers of dwellings are displayed above the bars.
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Atmosphere 14 00952 g005 550
Figure 5. Indoor 222 Rn map of Lima province using the IDW model.
Figure 5. Indoor 222 Rn map of Lima province using the IDW model.
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Figure 6. Indoor 222 Rn map of Lima province using the Kriging model.
Figure 6. Indoor 222 Rn map of Lima province using the Kriging model.
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Table
Table 1. Criteria to determine the minimum number of sampled dwellings per grid.
Table 1. Criteria to determine the minimum number of sampled dwellings per grid.
District TypePopulation Density (inhabitants·km − 2 )Number of Sampled Dwellings by Grid (Minimum)
A≤2500none
B≤50001
C≤10,0002
D≤20,0004
E≥20,0006
Table
Table 2. Number of 222 Rn monitors and dwellings sampled by grid cells in each district based on the estimated population.
Table 2. Number of 222 Rn monitors and dwellings sampled by grid cells in each district based on the estimated population.
ZonesDistrictPopulation Density * (inhabitants·km − 2 )Sampled Dwellings per Grid (Minimum)Number of Grids (Urban Zone)Number of Monitors (Minimum)Number of Monitors (Real)
Lima CentroBarranco10,9512123
Breña29,56161616
Cercado de Lima12,198441631
Jesús María18,36041413
La Victoria21,69362129
Lince20,3594147
Magdalena del Mar18,7704147
Miraflores11,79942810
Pueblo Libre22,27661650
Rímac15,4074285
San Borja13,14142811
San Isidro625322410
San Luis16,13241412
San Miguel16,85142835
Surco12,087472822
Surquillo29,6056166
Lima EsteAte9043271426
Cieneguilla1670100
Chaclacayo11420102
El Agustino18,2964141
La Molina251015511
Lurigancho12790100
San Juan de Lurigancho93342183629
Santa Antita21,284612727
Lima NorteAncon3060100
Carabayllo12220100
Comas12,0394104042
Independencia15,668431210
Los Olivos19,666441651
Puente Piedra57911141413
San Martín de Porres20,881472828
Santa Rosa19580100
Lima SurChorrillos942729184
Lurin6330102
Pachacamac9500101
Pucusana5560100
Punta Hermosa1960100
Punta Negra670100
San Bartolo2060100
San Juan de Miraflores17,60648329
Santa María del Mar1230100
Villa María del Triunfo12,188214285
Villa El Salvador635947289
* National Census, 2017 [36].
Table
Table 3. Descriptive statistical results and ANOVA analysis of the indoor 222 Rn concentration obtained using bare detectors by zones.
Table 3. Descriptive statistical results and ANOVA analysis of the indoor 222 Rn concentration obtained using bare detectors by zones.
VariablesNumber of DwellingsPopulation Density per km 2 Min (Bq·m − 3 )Max (Bq·m − 3 )Median (Bq·m − 3 )GM (Bq·m − 3 )GSD (Bq·m − 3 )]-3*ANOVA
F-Valuep-ValuePercentage of Variation (%)
ZonesLima Centro23514,5881630663572 69.24
Lima Este5813,22616228393928.576590.000028.90
Lima Norte11011,2781616645452 17.67
Lima Sur3441881513334372 4.19
Table
Table 4. Descriptive statistical results and ANOVA analysis of the indoor 222 Rn concentration using bare detectors for some variables.
Table 4. Descriptive statistical results and ANOVA analysis of the indoor 222 Rn concentration using bare detectors for some variables.
VariablesNumber of DwellingsMin (Bq·m − 3 )Max (Bq·m − 3 )Median (Bq·m − 3 )GM (Bq·m − 3 )GSD (Bq·m − 3 )ANOVA
F-Valuep-ValuePercentage of Variation (%)
Construction Age (years)  0 to 201951525550502 45.99
  20 to 397815166504824.585410.0108216.90
  Over 40821630664592 37.12
Vehicular TrafficLow2401530650512 65.81
Medium12416232505020.254290.7755928.95
High252414555542 5.24
Construction MaterialsBricks3351530650512 87.75
Adobe1016145374320.205690.814172.55
Others321723255542 9.70
Wall Painting (years)Over 510815212535222.039900.15437144.62
Below 51651625549492 55.38
Floor Type *Cement991529246492 27.78
Wood651630650512 20.27
Majolica14616255545120.074290.9899536.31
Tapestry121612564532 2.95
Others551818655512 12.69
* All detectors were placed on first level.
Table
Table 5. Descriptive statistical results and ANOVA analyses of the indoor 222 Rn concentration using G2 monitors by zones.
Table 5. Descriptive statistical results and ANOVA analyses of the indoor 222 Rn concentration using G2 monitors by zones.
VariablesNumber of DwellingsMin (Bq·m − 3 )Max (Bq·m − 3 )Median (Bq·m − 3 )GM (Bq·m − 3 )GSD (Bq·m − 3 )ANOVA
F-Valuep-ValuePercentage of Variation (%)
ZonesLima Centro2325306109942 37.39
Lima Este3120292726721.986290.1225534.55
Lima Norte172023564672 19.98
Lima Sur142220837452 8.08
Table
Table 6. The cross-validation results for the IDW and Kriging models.
Table 6. The cross-validation results for the IDW and Kriging models.
MethodMAE *RMS *RMSE *R 2
IDW36.3584.97149.546−0.637
Kriging32.45141.47443.856−0.240
* MAE is the mean absolute error; RMS is the root mean square; and RMSE is the root mean square error.
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Pereyra, P.; Guevara-Pillaca, C.J.; Liza, R.; Pérez, B.; Rojas, J.; Vilcapoma L., L.; Gonzales, S.; Sajo-Bohus, L.; López-Herrera, M.E.; Palacios Fernández, D. Estimation of Indoor 222Rn Concentration in Lima, Peru Using LR-115 Nuclear Track Detectors Exposed in Different Modes. Atmosphere 2023, 14, 952. https://doi.org/10.3390/atmos14060952

AMA Style

Pereyra P, Guevara-Pillaca CJ, Liza R, Pérez B, Rojas J, Vilcapoma L. L, Gonzales S, Sajo-Bohus L, López-Herrera ME, Palacios Fernández D. Estimation of Indoor 222Rn Concentration in Lima, Peru Using LR-115 Nuclear Track Detectors Exposed in Different Modes. Atmosphere. 2023; 14(6):952. https://doi.org/10.3390/atmos14060952

Chicago/Turabian Style

Pereyra, Patrizia, Cesar J. Guevara-Pillaca, Rafael Liza, Bertin Pérez, Jhonny Rojas, Luis Vilcapoma L., Susana Gonzales, Laszlo Sajo-Bohus, María Elena López-Herrera, and Daniel Palacios Fernández. 2023. "Estimation of Indoor 222Rn Concentration in Lima, Peru Using LR-115 Nuclear Track Detectors Exposed in Different Modes" Atmosphere 14, no. 6: 952. https://doi.org/10.3390/atmos14060952

APA Style

Pereyra, P., Guevara-Pillaca, C. J., Liza, R., Pérez, B., Rojas, J., Vilcapoma L., L., Gonzales, S., Sajo-Bohus, L., López-Herrera, M. E., & Palacios Fernández, D. (2023). Estimation of Indoor 222Rn Concentration in Lima, Peru Using LR-115 Nuclear Track Detectors Exposed in Different Modes. Atmosphere, 14(6), 952. https://doi.org/10.3390/atmos14060952

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