KR20210017955A - Method for estimating mean annual exposure dose of indoor radon based on method for estimating mean annual indoor radon concentration in residence - Google Patents

Abstract

Disclosed is a method for calculating the annual average effective dose of indoor radon exposure using indoor radon concentration by residence. The present invention is configured to calculate the lifetime effective dose of indoor radon exposure by estimating indoor radon concentration (C_i) by residence and inputting resident time by residence and age using the indoor radon concentration to estimate the annual average of the indoor radon concentration by residence without measuring the radon concentration of actual residence. Also, the present invention compares an infiltration coefficient value for infiltration factors different depending on building characteristics that are calculated depending on the type of housing and groundwater use with the actual radon concentration so as to estimate the indoor radon concentration.

Description

Calculation of annual average indoor radon exposure effective dose using indoor radon concentration by residence {METHOD FOR ESTIMATING MEAN ANNUAL EXPOSURE DOSE OF INDOOR RADON BASED ON METHOD FOR ESTIMATING MEAN ANNUAL INDOOR RADON CONCENTRATION IN RESIDENCE}

The present invention relates to the estimation of indoor radon concentration for each residence, and more specifically, a lifetime using indoor radon concentration for each residence that can calculate the effective dose of radon exposure by estimating the annual average indoor radon concentration for each residence without measuring the radon concentration for the actual residence. It relates to the method of calculating the annual average indoor radon exposure effective dose.

Radon is a noble, radioactive, colorless, odorless, tasteless gas known as a major component of natural radiation.

It accumulates in the lungs and can lead to lung cancer. It is the second leading cause of lung cancer after smoking. Long-term exposure to radon in an indoor environment where people spend a lot of time, such as in a residence, poses a health risk.

According to the UNSCEAR report (2000), it is reported that 56% of indoor radon comes from soil, 21% comes from building materials, 20% comes from outside air, 2% comes from water supply, and the remaining 1% comes from natural gas.

Long-term exposure to high concentrations of radon changes respiratory function and increases the incidence of lung cancer. The health risks associated with exposure to indoor radon are due to the radon concentration and duration of exposure. The health risk assessment of indoor radon and long-term exposure to indoor radon should be considered to determine the relationship between indoor radon exposure and lung cancer.

In order to assess the health effects of indoor radon exposure, long-term radon exposure assessments should be made. In order to estimate long-term exposure to indoor radon, information on indoor radon levels and indoor residence time is needed.

However, measuring long-term indoor radon concentrations is time consuming and expensive, and since variability due to movement of residence must be considered, studies on the harmful health effects associated with long-term indoor radon exposure are limited. In the world, only short-term radon concentration surveys are being used to evaluate the effective dose and risk of lung cancer caused by exposure to radon.

However, the average cumulative radon concentration in the short term is insufficient to represent the average cumulative radon concentration in one year. In the case of countries where the four seasons change throughout the year, the indoor environment changes according to the weather fluctuations in the four seasons as well as the outdoor environment. For example, in winter, the radon concentration in the indoor air is detected higher than that of other seasons due to the chimney effect, and in summer, the radon concentration in the indoor air is detected lower than that of other seasons due to frequent ventilation.

In addition, depending on the type of housing, for example, a single-family house or a non-single-family house, the seasonal radon concentration varies greatly. For example, there is a significant difference in the ratio of radon concentration in winter and summer between detached houses and non-detached houses.

This means that the radon concentration varies greatly depending on the time of measurement and the type of residence, and this change can be said to cause uncertainty in assessing the effective dose and risk of lung cancer caused by exposure to radon.

To overcome these problems, Park et al. Based on the method proposed by Pinel et al., a seasonal correction factor was calculated using the measured values at 3-month intervals for each season jointly conducted by the Korea Institute of Nuclear Safety and Technology (KINS) and the National Environmental Research Institute (NIER), and calculated the average accumulated radon over a year. A seasonal correction model to estimate the concentration was presented. Also, Park et al. Proposed a model for estimating the indoor radon concentration in a single-family house with only residence information and ventilation habit information based on the mass balance equation using survey data between January and April 2016.

However, since the seasonal correction model requires a short-term actual radon measurement value, it is difficult to evaluate radon exposure in past residences where radon concentration measurement is difficult, and a model for estimating indoor radon concentration in a single-family house with only residence information and ventilation habit information. Has a limitation that it is applicable only to single-family houses.

Therefore, in order to evaluate the health effects of indoor radon exposure, it is necessary to develop an indoor radon concentration model that can be applied to various types of houses and can estimate the average cumulative radon concentration for one year.

KR Registered Patent Publication No. 10-1528780 (2015.06.09)

An object of the present invention for solving this problem is to provide a method of calculating the annual average indoor radon exposure effective dose using the indoor radon concentration for each residence by estimating the annual average indoor radon concentration for each residence without measuring the radon concentration for the actual residence.

In addition, the present invention provides a method for calculating a lifetime average indoor radon exposure effective dose using indoor radon concentration for each residence, which can estimate the indoor radon concentration by calculating the penetration coefficient value according to the type of house and groundwater use. To provide for a different purpose.

Another object of the present invention is to provide a method of calculating the annual average indoor radon exposure effective dose for a lifetime using indoor radon concentrations for each residence, which can calculate the annual average indoor radon exposure effective dose at the residence by entering residence information and residence time. do.

In the controller of the present invention for solving these problems, the method of estimating the indoor radon concentration for each residence using the residence information in which the resident resides, and calculating the annual effective dose of radon exposure by using this is the indoor radon concentration for each residence (C _i ) It can be achieved by configuring to be expressed by the following equation.

은 환기정보를 의미한다.Where C _bm = radon concentration in building materials (Bqm ^-3 ), C _i = indoor radon concentration by residence (Bqm ^-3 ), C _s = radon concentration (Bqm ^-3 ), C _o is radon concentration in outdoor air ( Bqm ^-3 ), E _bm is the effective radon expiration rate of building materials (Bqm ^-2 h ^-1 ), E _s is the effective soil expiration rate of radon, f _t and f _w are fitting variables, k _a is the advection transition of soil Coefficient (mPa ^-1 h ^-1 ), k _d,s = diffusion transfer coefficient of soil (mh ^-1 ), S _bm is the indoor surface area of radon containing building material (m ² ), S _g is the building facing the ground Area (m ² ), Ti and To are indoor and outdoor temperature (℃), u is wind speed (ms ^-1 ), V = indoor volume (m ³ ), λ is radon damping constant (h ^-1 ), λ _v is Ventilation rate (h ^-1 ), ΔP _si is the differential pressure between the soil and the room (Pa), T _o and u ² are weather information, and N is the number of ventilation.

Means ventilation information.

In addition, the penetration coefficient S is expressed by the following equation.

In addition, the equation for estimating the penetration coefficient from the building material of the residence i is expressed by the following regression equation.

Where S _i is the coefficient of penetration from soil and building materials for each dwelling i, X _1i is the percentage of green area in the administrative district to which the dwelling i belongs, X _2i is the geometric mean of the indoor radon level in the administrative district to which the dwelling i belongs, Y _1i is the type of building material in residential building i, Y _2i is the degree of cracking in residence i, Y _3i is the residential floor i, and ε _i is the measurement error of residence i.

In addition, the information on the residence where the resident resides is a single-family house and apartment or multi-family house including Western-style houses and Hanok houses, and non-detached houses including coalitions, and can be classified into 4 groups depending on whether or not groundwater is used.

In addition, (b) calculating the indoor radon exposure effective dose for each age group by inputting the residence time for each residence in the annual average indoor radon concentration (C _i ) for each residence estimated in step (a), and (c) above (b) It can be configured to include the step of calculating the indoor radon exposure effective dose for an individual lifetime by summing the indoor radon exposure effective dose for each age group extracted in step) to calculate the lifetime effective dose.

The step (b) calculates the effective dose of indoor radon exposure for each age group exposed to the resident by applying the average dwell time for each dwelling place to the estimated annual average indoor radon concentration for each dwelling place, and the indoor radon for each age group in the step (b). The effective exposure dose (E _ij ) can be expressed by the following equation.

Here, E _ij is by residence (i) by residence age (j), the effective dose of indoor radon exposure by age group, and (Tij*365day*Yij(unit: year)) means residence time by age group.

Therefore, according to the method for calculating the effective dose of annual average indoor radon exposure using indoor radon concentrations for each residence of the present invention, there is an effect of estimating the annual average indoor radon concentration for each residence without measuring the radon concentration for an actual residence.

In addition, according to the method of calculating the annual average indoor radon exposure effective dose using indoor radon concentrations for each residence of the present invention, infiltration factors that may vary depending on the characteristics of the building (e.g., area, building material, crack and number of floors) are used in houses and groundwater There is an effect of estimating the indoor radon concentration by calculating the permeation coefficient value according to the type of and comparing it with the actual radon concentration.

In addition, the present invention has the effect of calculating the annual average indoor radon exposure effective dose at the residence by inputting the residence time to the annual average indoor radon concentration of a house.

1 is a configuration diagram of an apparatus for calculating an effective dose of annual average indoor radon exposure for a lifetime using a method for estimating indoor radon concentration for each residence according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a method of calculating an effective dose of annual average exposure to radon according to an embodiment of the present invention;
3 is a summary table of average annual radon concentration according to residential type and groundwater usage,
4 is a coefficient estimation table of a penetration coefficient model according to residence time characteristics,
5 is a comparison table of the calculated and estimated values of the penetration coefficient S according to the house type,
And
6 is a comparison table of indoor radon calculated values and measured values according to the type of house.

Terms and words used in the present specification and claims are not limited to the usual or dictionary meanings, and the inventor is based on the principle that the concept of terms can be appropriately defined in order to explain his or her invention in the best way. It should be interpreted as a meaning and concept consistent with the technical idea of the present invention.

Throughout the specification, when a part "includes" a certain component, it means that other components may be further included rather than excluding other components unless otherwise stated. In addition, terms such as "... unit", "... group", "module", and "device" described in the specification mean a unit that processes at least one function or operation, which is implemented by a combination of hardware and/or software. Can be.

Throughout the specification, the term "and/or" is to be understood as including all possible combinations from one or more related items. For example, the meaning of “a first item, a second item and/or a third item” may be presented from two or more of the first, second or third items as well as the first, second or third item. It means a combination of all possible items.

Throughout the specification, the identification code (for example, a, b, c, ...) in each step is used for convenience of description, and the identification code does not limit the order of each step, and each step is It may occur differently from the specified order unless a specific order is clearly stated in the context. That is, each of the steps may occur in the same order as specified, may be performed substantially simultaneously, or may be performed in the reverse order.

Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

First, the present invention develops a model for estimating indoor radon concentration because it is practically difficult to measure variability due to movement of residence and long-term indoor radon concentration, and since there are few studies on mathematical models based on actual data, indoor radon concentration model Among them, RAGENA (Font, 1997) model was modified to reveal that a Korean model for indoor radon concentration estimation was developed.

Also, the main factor in our model is the penetration rate, which can vary depending on the region, building material, cracks, and number of floors. In this study, the penetration coefficient was calculated according to the type of house and groundwater use and the result.

Overall, it turns out that the measured concentrations and estimates of indoor radon concentrations using the penetration coefficient model were similar. As a result of the experiment, the model of the present invention showed better performance than the previous model except for some houses with very high radon concentration.

The purpose of this study is to develop a widely applicable model for estimating the average annual indoor radon concentration in real residences considering seasonal changes in indoor radon.

The model is built on the basis of a mass balance equation using data on geographic factors, building characteristics, weather factors, and national radon surveys.

The examples described below estimate the indoor radon concentration of a house exposed to a resident of the dwelling by reflecting the average concentration of radon measured for a certain month and meteorological factors in a dwelling place, and calculate the effective dose of radon exposure using this. It features.

1 is a block diagram of an apparatus for calculating an effective dose of annual radon exposure using a method for estimating indoor radon concentration in a house according to an embodiment of the present invention.

Referring to FIG. 1, a device for predicting the annual exposure dose of radon is composed of a radon sensor 10, a controller 20, and an output device 30, and is installed inside a residence. The radon sensor 10 detects the concentration of radon contained in the indoor air. The controller 20 predicts the annual exposure dose of radon according to the method described below. The output unit 30 outputs the effective dose of radon exposure estimated by the controller.

The controller 20 is composed of a data receiving unit 21, a processor 22, a storage 23, and a user interface 24.

The storage 23 stores a program for estimating the average annual radon concentration for each residence reflecting the average concentration of radon and meteorological factors measured for a certain month in a certain residence and an effective dose of radon exposure to residents of the residence.

As the processor 22 executes the estimation program stored in the storage 23, the controller 20 estimates the lifetime cumulative radon exposure amount according to the estimation program and transmits it to the output unit 30.

The user interface 24 receives the type of residence from the user, more specifically whether a detached house or a non-detached house, and receives the residence time at the residence and the average concentration of radon measured for a set period of time, and receives the processor 22 ).

The data receiving unit 21 receives the average indoor radon concentration data of the seasonal residential area accumulated from the radon measuring institution, receives the outdoor monthly average temperature and monthly average wind speed data from the meteorological office and stores it in the storage 23 as a DB. At this time, the average indoor radon concentration data of the seasonal residence area is classified according to the type of residence, and more specifically, it is divided into single-family houses and non-single houses.

Accordingly, when the processor 22 executes the estimation program stored in the storage 23, the average indoor radon concentration data of the residential area may be classified and substituted according to the type of the residence transmitted to the processor 22.

The output unit 30 receives and outputs the effective dose of radon exposure calculated by the processor 22.

How to specifically build a mathematical prediction model used to calculate the annual effective dose of radon and how to determine the monthly correction factor reflecting meteorological factors will be described in detail with reference to FIGS. 3 and 4 below.

In general, as a process test method for measuring radon indoors, a method of calculating the accumulated radon concentration for 3 months, which is a long-term measurement method, is recommended. However, the radon concentration in the indoor air shows seasonal differences, so the cumulative radon concentration for 3 months by season is insufficient to represent the average radon concentration for a year. Accordingly, the prediction of the annual exposure dose of radon based on the average concentration of radon for three months by season may become uncertain, and thus the reliability of the assessment of the risk of lung cancer caused by radon exposure may be degraded.

According to this example, by predicting the annual exposure dose of radon based on the average concentration of radon measured over a certain period of time, for example, the average concentration of radon measured over any recommended 3 months, the annual exposure of radon regardless of the season The exposure dose can be calculated. In addition, since the annual exposure dose of radon is predicted by considering the weather factors, specifically the monthly correction factor reflecting the outdoor monthly average temperature and monthly average wind speed, it is possible to calculate a more accurate annual exposure dose considering the environment of the residential area. . Therefore, since the annual exposure dose of radon can be applied to the calculation of the risk of lung cancer caused by exposure to radon, it is possible not only to calculate the risk of developing lung cancer with confidence, but also to establish radon reduction measures by considering the annual exposure dose of radon. It can prevent lung cancer, a chronic disease caused by radon.

Hereinafter, a method of estimating the annual average indoor radon concentration in a house will be described with reference to the drawings.

In order to estimate the annual average indoor radon concentration in a house, the indoor radon concentration and questionnaire were first conducted.

Indoor radon concentrations were collected from 1,437 dwellings using a manual alpha track detector (Raduet Model RSV-8, Radosys Ltd., Budapest, Hungary) for a minimum of 90 days from October 2015 to December 2018.

Radon concentrations were measured at two points in each residence, and the measurement points were selected in the most time-consuming spaces such as living rooms and bedrooms. It turns out that the geometric mean of radon concentrations from two points in the settlement was used to develop a radon concentration model.

Because of the lognormal distribution of radon concentration, a logarithmic transformation value is applied, and also, address (city/province and city/county/district), house type, building material, location and number of cracks in the building, groundwater use, ventilation habits, walls Building materials, number of floors, and information of residents were collected through a questionnaire to a total of 1,390 residents.

Finally, for the stability of the developed model, 47 selected outliers were removed from the log-transformed radon data and a model was developed using 1,343 dwellings.

Since indoor radon concentrations were measured regardless of the season, in order to eliminate seasonal variations by standardizing the data obtained from the radon survey, indoor radon concentrations were converted to annual average radon concentrations at home using the seasonal correction factor described by Park et al. I did.

3 is a summary of the average annual radon concentration according to the residential type and groundwater usage.

National Radon Survey Data

For model development, three surveys (https://iaqinfo.nier.go.kr) were conducted during the winter season from 2011 to 2016 using the national radon survey data of the National Institute of Environmental Research in Korea. I did it.

Surveys for 2011-2012, 2013-2014 and 2015-2016 were conducted for approximately 11,000, 8,000 and 12,000 settlements, respectively.

The homes for the survey were selected for the types of housing structures such as single-family homes, apartments and multi-family homes. For apartments and multi-storey buildings, only residences of three or less floors were selected. About 14,152 radon values measured in separate houses were extracted (AM = 116.71 ± 103.43 Bq m ^ -3), GM = 86.52 ± 2.14 Bq m ^ (- 3)) After removing the singular values after log transformation, 13,975 Radon concentration values in dog dwellings were used.

Geographic information and meteorological data

Using the structural equation model, Lee et al. [6] It has been shown that indoor radon concentrations are high in areas with a high green area ratio.

The Environmental Geographic Information Service, hosted by the Ministry of Environment, provides access to green area ratio data reflecting the green area compared to the management area according to the city/county/gu (https: //eais.me.go). . Green areas consist of forests and grasslands and do not include agricultural lands such as rice paddies.

Ventilation is well-known as a major factor affecting indoor radon concentration, and ventilation rate is known to be affected by weather factors such as indoor and outdoor temperature differences and wind speed. To smooth the annual deviation of the monthly average weather factor, a 30-year average (1981-2010) was used by the national climate data service system (https://sts.kma.go.kr).

Indoor radon mass balance equation

Indoor radon is affected by several parameters, so many parameters are required to develop a model. However, we have only focused on factors that have been shown to influence indoor radon concentrations in previous studies, such as soil, building materials, outdoor air and ventilation.

The mass balance equation is the mass balance relationship of a substance derived by applying the law of conservation of mass to part or all of a process. The difference between the mass entering the system and the mass leaving the system is the same as the mass that accumulates in the system or is created or destroyed by a chemical reaction in the system. For indoor radon concentration, the following equation can be applied.

Accumulated indoor radon = inflow ± movement by ventilation-decomposition by radiation decay

Park et al. The differential equation system for estimating the indoor radon concentration (C _i ) for each Korean residence by modifying the RAGENA (Font, 1997) model is shown in Equation 1 below.

The actual (C _i ) means the annual average indoor radon concentration by residence, but hereinafter, it will be described as "indoor radon concentration by residence".

Where C _bm = radon concentration in building materials (Bqm ^-3 ), C _i = indoor radon concentration by residence (Bqm ^-3 ), C _s = radon concentration (Bqm ^-3 ), C _o is radon concentration in outdoor air (Bqm ^-3 ) ^-3 ), E _bm is the effective radon expiration rate of building materials (Bqm ^-2 h ^-1 ), E _s is the effective soil expiration rate of radon, f _t and f _w are fitting variables, k _a is the advection transfer coefficient of soil (mPa ^-1 h ^-1 ), k _d,s = diffusion transfer coefficient of the soil (mh ^-1 ), N is the number of ventilation, S _bm is the indoor surface area of radon containing building materials (m ² ), S _g is Building area facing the ground (m ² ), Ti and To are indoor and outdoor temperature (℃), u is wind speed (ms ^-1 ), V = indoor volume (m ³ ), λ is radon damping constant (h ^-1 ) , λ _v is the ventilation rate (h ^-1 ), and ΔP _si is the differential pressure between the soil and the room (Pa).

Assuming an equilibrium state in Equation 1, an equation such as the following Equation 2 can be obtained.

Where C _bm = radon concentration in building materials (Bqm ^-3 ), C _i = indoor radon concentration by residence (Bqm ^-3 ), C _s = radon concentration in soil (Bqm ^-3 ), C _o is radon concentration in outdoor air (Bqm ^-3 ), E _bm is the effective radon expiration rate of building materials (Bqm ^-2 h ^-1 ), E _s is the effective soil expiration rate of radon, f _t and f _w are fitting variables, N is the number of ventilation, S _bm is the indoor surface area of radon containing building materials (m ² ), S _g is the building area facing the ground (m ² ), Ti and To are indoor and outdoor temperatures (℃), u is the wind speed (ms ^-1 ), λ is the radon attenuation constant (h ^-1 ), λ _v is the ventilation rate (h ^-1 ).

Equation 2 is the final equation for calculating the indoor radon concentration. Since it is difficult to apply the above equation as it is, although the parameters of the model must be estimated, the infiltration factor model should be considered as follows.

In Equation 2, "E _{s *} (S _g /V) + E _{bm *} (S _bm /V)" represents the coefficient of penetration from soil and building materials. However, information on all parameters in the equation is required to calculate radon release rates from soil and building materials, and this approach is impractical because the information available is limited.

For this reason, in order to suggest a regression model that can estimate "E _{s *} (S _g /V) + E _{bm *} (S _bm /V)", the penetration coefficient model is defined as follows.

Penetration coefficient model

"E _{s *} (S _g /V) + E _{bm *} (S _bm /V)" in Equation 2 was expressed as an infiltration factor S representing the infiltration concentration according to the type from soil and building materials. Also, based on the mass balance equation and input variables described above, the penetration coefficient S can be directly calculated using Equation 3 below.

On the other hand, the penetration coefficient from soil and building materials is affected by geographic and architectural characteristics. The green area ratio can be considered a factor related to the soil release rate of radon from the surface. Since cracks in buildings and floors may be a factor, a statistical model of the following equation (4) can be considered for estimating the penetration coefficient from soil and building materials of residence i.

Where S _i is the coefficient of penetration from soil and building materials for each dwelling i, X _1i is the percentage of green area in the administrative district to which the dwelling i belongs, X _2i is the geometric mean of the indoor radon level in the administrative district to which the dwelling i belongs, Y _1i is the type of building material in residential building i, Y _2i is the degree of cracking in residential building i, Y _3i is the living floor i, and ε _i is the measurement error of the residence i.

S in Equation 3 is a value calculated based on actual data.In order to calculate S, various information such as the measured radon concentration is required.If you do not know the radon concentration in the future, use the S _i equation in Equation 4 to calculate S. It was constructed (regression model), and S _i is an equation that can estimate S with the information of the i-th house even without an actual measurement value, and this estimated S _i is regarded as S and substituted into Equation 2 or Equation 3 Thus, C _i can be estimated.

In Equation 4, β ₀ , β ₁ , β ₂ , β ₃ , β ₄ , and β ₅ are coefficient values estimated based on the S calculated in Equation 3 and the data of residence information (X1 to Y3), β ₀ Based on the values of, β ₁ , β ₂ , β ₃ , β ₄ , β ₅ and residence information (X1~Y3), S can be estimated even if there is no actual radon concentration measurement value.

Model application

By applying the research data to Equation 3, the parameters of the model are estimated separately for each of the four types shown in Table 1 below so that the parameters of the model can be estimated.

In other words, the type of house is determined according to the type of house and the use of groundwater.

TYPE decision Housing type
Detached (Western house) house
Detached (hanok) housing Apartment
Multi-family housing
Alliance Groundwater use Yes
(Check at least one of washing dishes, laundry, and shower) TYPE1 TYPE3 no
(etc) TYPE2 TYPE4

As the study data, indoor radon concentration values, indoor radon concentration in winter, meteorological data, and green area data are used.

First, data for measuring indoor radon concentration are shown in Table 2.

In the present invention, the indoor radon concentration (C _i ) for each residence is described, but it is noted that it means a concentration value expressed as a numerical value.

In addition, indoor radon concentration in winter is measured by setting the conditions shown in Table 3 below.

This data is public data, and there is no information on residence other than city/county/gu and type of house.

Table 4 was used for the published meteorological data and green area data.

As a result of applying the model, the estimated coefficients of the infiltration factor model for each house type are shown in Table 5.

Each coefficient of S for each type is a coefficient extracted from research data.

In other words, it is a coefficient extracted by inputting research data data by type of 1,343 residences.

A comparison of the calculated and estimated values of the penetration coefficient S is shown in FIG. 5.

Since the model of the present invention depends on the penetration rate, values for the penetration coefficient from soil and building materials for individual houses must be obtained.

Therefore, S is calculated using Equation 4 and the parameter of Equation 5 is estimated using it. As can be seen in Table 1, factors to be considered depend on the type of residence because indoor radon concentrations in single-family homes are higher than those in other homes.

Radon in groundwater is known to affect indoor radon concentration, but information on radon in groundwater is lacking in Korea. In order to consider the effect of dwelling type and groundwater on indoor radon concentration, the model was divided into four according to the dwelling type and groundwater usage, and the penetration coefficient S was estimated. Therefore, we have integrated Equation 3, Equation 4, and Equation 5 to predict the indoor radon concentration C _i by residence. Finally, we evaluated the predictive ability of all four model types.

Input variables for calculating S in Equation 3 and parameters for estimating S in Equation 4 are as follows.

1) As mentioned above, the indoor radon concentration was converted to the annual average radon concentration in the residence using Park et al.'s correction factor.

2) Since the radon inflow from the building material is relatively small compared to the soil, it was assumed to be "C _s + C _bm ≒ C _s ". In addition, it is not suitable to be used as a representative value of C _s because information on radon concentration in Korean soil is insufficient and radon concentration in soil is not measured under the same conditions. Indoor radon concentrations in Korea are higher in granite areas than in other areas.

Radon data used in the present invention were obtained only in winter, and the influence of factors such as ventilation was reduced. Therefore, using data obtained from the national radon survey, we determined the radon concentration in the soil in 233 administrative districts (city/county/gu) as a weight. The weights were calculated as the ratio of the regional geometric mean to the total geometric mean. For the radon concentration C _{o in} the outdoor air, the radon concentration in the outdoor air in 17 administrative districts (city/province) was estimated by the same method as above.

Because the ventilation rate is affected by weather factors such as indoor and outdoor temperature differences and wind speed, the monthly average wind speed and outdoor temperature were used to calculate the ventilation rate. A 30-year average (1981 to 2010) was used to smooth the annual deviation of the average monthly weather factor. In addition, in order to calculate the ventilation rate λ _v , it was assumed that the values of the fitting parameters f _t and f _w are the same as those of Park et al. In addition, N values were assigned differently according to the residents' ventilation habits.

Parameter estimation result

Figure 4 shows the coefficient estimates of the penetration coefficient model (R version 3.5.1). Here, the influence of geographic parameters such as the green area ratio and the geometric mean of the indoor radon level in the administrative area showed that the influence of the building material was statistically significant (P<0.05).

However, the influence of building materials was not greater than 0.05 in P. As the number of cracks increased, the penetration factor value also increased, but it was not statistically significant (P>0.05). In addition, the higher the number of floors, the higher the penetration rate, but the difference in this value was statistically significant only in single-family houses (P <0.05). Meanwhile, the calculated ventilation rate varied from 0.24 to 0.73 h ^-1 (AM = 0.34 ± 0.08 h ^-1 )), and this value was included to calculate the penetration rate and ranged from 0.1 to 1 h ^-1 . Based on these results, a penetration coefficient model was applied to estimate the penetration coefficient from soil and building materials. In order to evaluate the fit of the model, the calculated value of S using Equation 3 and the estimated value of S using Equation 4 were compared (see FIG. 5).

5 is a comparison table of the calculated and estimated values of the penetration coefficient S according to the type of house. The model performed better in other houses than in a single-family house, but the calculated and estimated penetration rate values followed a similar trend overall.

With the penetration coefficient S estimated in Equation 4, we predicted the indoor radon concentration using Equation 2.

6 is a comparison table of calculated and estimated values of indoor radon concentration according to house type, and is a degree of agreement between measured indoor radon concentration and estimated indoor radon concentration. The results were better for other houses than for single-family houses in the penetration coefficient model, so when estimating the indoor radon concentration, the agreement was better for other houses than for single-family houses. In the case of Type 3, the number of dwellings was small, but the variance was small, so the agreement was the best. Our model performed better than the previous model, except for a few houses with very high radon concentrations.

The average annual exposure amount (E) per individual is calculated by Equation 5 below.

Table 6 summarizes the calculation process of the annual average indoor radon exposure effective dose so far.

Here, E _ij is by residence (i) by residence age (j), the effective dose of indoor radon exposure by age group, and (Tij*365day*Yij(unit: year)) means residence time by age group.

Referring to Table 6, for example, E ₃ and 2 refer to the effective dose of indoor radon exposure for those aged 35 to 44 in Jung-gu, Daejeon, the third residence.

In other words, the effective dose of indoor radon exposure in an individual's lifetime is to estimate the indoor radon concentration in the house by residence, enter the residence time by age group in the estimated indoor radon concentration (C _i ) by residence, and use the sum of the indoor radon exposure throughout the individual lifetime. It is to calculate the effective dose.

As described above, the present invention was carried out as part of an effort to evaluate the health effect of indoor radon exposure, and a model capable of estimating the cumulative exposure to radon and establishing a relationship with lung cancer through additional research You will be able to develop it.

In other words, it is difficult and seemingly impractical to develop a model for estimating indoor radon concentration due to many factors affecting indoor concentration, but estimating indoor radon concentration is necessary to determine the effect of long-term radon exposure indoors. Because it does.

In the present invention, the indoor radon concentration is estimated when the actual measured value for the input variable cannot be obtained in consideration of topographic characteristics, building characteristics, meteorological characteristics, and living behavior. Compared to the previous indoor radon model proposed by Park et al. It can be seen that the applicability and performance have improved.

There were several limitations to this study. First, this model did not show better results in some houses with very high radon concentrations. This may be due to the radon concentration of about 88% of the residences investigated in this study is less than 100 Bqm ^-3 .

Model performance may have improved if indoor radon concentrations are evenly distributed in low and high concentration dwellings. And ventilation rate, an important factor influencing indoor radon level, was calculated based on ventilation habits reported in questionnaire without actual measurement.

Thus, false resident responses can lead to large differences in measured and estimated radon concentrations. Therefore, it is expected that the accuracy of the model will improve if we consider other parameters that can be used to predict ventilation rates rather than the responses of the questionnaire. In addition, our model could be improved to take into account other building characteristics, such as the year of construction.

A method of calculating the effective dose of annual radon exposure using the method of estimating indoor radon concentration in a house based on the above-described survey will be described.

2 is a flowchart illustrating a method of calculating an effective dose of annual average exposure to radon according to an embodiment of the present invention.

Referring to FIG. 2, the method for predicting an effective dose of annual average radon exposure in a lifetime according to the present embodiment includes steps performed by the controller 20 as follows. As shown in FIG. 1, the storage 23 stores a program for predicting the lifetime average annual exposure effective dose of radon estimated through the above-described investigation. The method of predicting the effective dose of annual average exposure to radon shown in FIG. 2 may be performed by the processor 22 of the controller 20 executing an estimation program stored in the storage 23.

The method for predicting the effective dose of annual average radon exposure for a lifetime of the present invention is the process of estimating the indoor radon concentration (C _i ) for each residence (S110 to S120) and inputting the residence time for each age group in the extracted C _i to provide a personal lifetime indoor radon exposure effective dose. It may be made in the process of calculating (S130 ~ S140).

First, in the process of estimating the indoor radon concentration (C _i ) for each residence (S110 to S120), the controller 20 receives data on the residence where the user resides through the user interface 24 in S110 first.

Data on residential area, type of housing, groundwater use, and ventilation habits are entered.

As for the house type, you can enter whether it is single or apartment/multi-family/column.

When data input for the dwelling place is completed in step S110, the controller 20 reflects the average concentration of radon measured in the dwelling place stored in the storage 23, the outdoor monthly average temperature, and the monthly average wind speed, and the indoor radon concentration ( Ci) is calculated (S120).

In order to calculate the indoor radon concentration (C _i ) for each residence, the controller 20 first calculates the ventilation rate (λ _v ) and the penetration coefficient (S) as described above (S121 to S122).

When the ventilation rate (λ _v ) and the penetration coefficient (S) are calculated, the indoor radon concentration (Ci) for each residence is calculated using these (S123).

Indoor radon concentration (Ci) for each residence is calculated by Equation 2.

The controller 20 calculates the annual indoor Ladong concentration for each residence according to the estimated model stored in the storage 23.

When the indoor radon concentration (C _i ) for each residence is calculated in step S120, the indoor radon exposure effective dose for each age group is calculated by incorporating the residence time for each age group into the annual average indoor radon exposure effective dose (E _ij ) in Equation 5 (S130). .

Specifically, in step S130, the annual residence time for each residence is input (S131).

In step S110, when entering a residence address, the residence age of the corresponding residence is also input. If the period of residence is long, it is desirable to form the age group by dividing it in units of approximately 10 years.

This is because even though the annual residence time is predicted in the corresponding residence, the actual residence time may be different for each age, so in the present invention, the effective dose of indoor radon exposure for each age group is calculated based on the residence time for each age group in about 10 years.

In other words, if the annual residence time for each residence is input in step S131, the residence time is calculated for each input age group (S132).

When the residence time for each age group is calculated in step S132, the processor 22 calculates the indoor radon exposure effective dose for each age group by adding it to the annual average indoor radon exposure effective dose E _ij of Equation 5 (S133).

Eventually, when the effective dose of indoor radon exposure for each age group is calculated in step S130, the effective dose of indoor radon exposure for an individual lifetime may be calculated by summing it by age group (S140).

Thereafter, if the lifetime indoor radon exposure effective dose is calculated in step S140, the lifetime average indoor radon exposure effective dose is calculated by dividing by the counted age (S150).

Referring to Table 6, residence 1 to 10 to 19 years old, residence 2 to 20 to 24 years old, residence 3 to 25 to 34 years 10 years, 35 to 44 years 10 years, 45 Since 10 years to 54 years old and 10 years from 55 to 64 years of age, all of them are 40 years at the place of residence 3, so the counted age is 55 years, so dividing the effective dose of indoor radon exposure calculated in step S140 by 55 years The average lifetime indoor radon exposure effective dose is calculated.

As described above, the present invention estimates the annual average concentration of radon by applying other research data to the measured average concentration of radon, and applies the residence time to the estimated average concentration value of radon. The dose can be calculated and provided to the user.

In the above, the present invention has been described in detail with respect to the described embodiments, but it is obvious to those skilled in the art that various modifications and modifications are possible within the scope of the technical idea of the present invention, and it is natural that such modifications and modifications belong to the appended claims.

10 ... Radon sensor 20 ... Controller
22: processor 23: storage
24: User interface 30...Printer

Claims (7)

In the method of estimating the annual average indoor radon concentration for each residence using the residence information where the resident resides in the controller, and calculating the annual effective dose of radon exposure by using this,
The indoor radon concentration (C _i ) by the residence is
A method for calculating an effective dose of annual average indoor radon exposure for a lifetime using indoor radon concentrations for each residence, which is represented by the following equation.

Where C _bm = radon concentration in building materials (Bqm ^-3 ), C _i = indoor radon concentration by residence (Bqm ^-3 ), C _s = radon concentration (Bqm ^-3 ), C _o is radon concentration in outdoor air ( Bqm ^-3 ), E _bm is the effective radon expiration rate of building materials (Bqm ^-2 h ^-1 ), E _s is the effective soil expiration rate of radon, f _t and f _w are fitting variables, k _a is the advection transition of soil Coefficient (mPa ^-1 h ^-1 ), k _d,s = diffusion transfer coefficient of soil (mh ^-1 ), S _bm is the indoor surface area of radon containing building material (m ² ), S _g is the building facing the ground Area (m ² ), Ti and To are indoor and outdoor temperature (℃), u is wind speed (ms ^-1 ), V = indoor volume (m ³ ), λ is radon damping constant (h ^-1 ), λ _v is Ventilation rate (h ^-1 ), ΔP _si is the differential pressure between the soil and the room (Pa), T _o and u ² are weather information, and N is the number of ventilation.

Means ventilation information.
The method according to claim 1,
Penetration coefficient (S) is characterized in that expressed by the following equation, a lifetime average indoor radon exposure effective dose calculation method using the indoor radon concentration for each residence.

The method according to claim 2,
Equation for estimating the penetration coefficient from the soil and building materials of the residence i is characterized in that it is expressed by the following equation, a lifetime average indoor radon exposure effective dose calculation method using indoor radon concentration for each residence.

Where S _i is the coefficient of penetration from soil and building materials for each dwelling i, X _1i is the percentage of green area in the administrative district to which the dwelling i belongs, X _2i is the geometric mean of the indoor radon level in the administrative district to which the dwelling i belongs, Y _1i is the type of building material in residential building i, Y _2i is the degree of cracking in residential building i, Y _3i is the living floor i, and ε _i is the measurement error of the residence i.
The method according to claim 1,
For information on where the resident resides
It is a single-family house and apartment or multi-family house including Western-style houses and Korean houses, and non-single-family houses including row houses, and is classified into 4 groups depending on whether or not groundwater is used. How to calculate the effective dose of indoor radon exposure.
The method according to claim 1,
(b) calculating the effective dose of indoor radon exposure for each age group by inputting the dwelling time for each dwelling place in the indoor radon concentration (C _i ) estimated in step (a); And
(c) calculating the effective dose of indoor radon exposure for a personal lifetime by summing the effective dose of indoor radon exposure for each age group extracted in step (b);
Lifetime average indoor radon exposure effective dose calculation method using the indoor radon concentration for each residence including a.
The method of claim 5,
Step (b)
A lifetime average indoor radon exposure using indoor radon concentration for each residence, characterized in that the effective dose of indoor radon exposure for each age group exposed to the resident is calculated by applying the average residence time for each residence to the estimated indoor radon concentration for each residence. How to calculate the dose.
The method of claim 5,
The effective dose (E _ij ) of indoor radon exposure by age group in step (b) is
A method of calculating an effective dose of annual average indoor radon exposure for a lifetime using indoor radon concentrations for each residence, characterized in that expressed by the following equation.

Here, E _ij is by residence (i) by residence age (j), the effective dose of indoor radon exposure by age group, and (Tij*365day*Yij(unit: year)) means residence time by age group.

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