KR20210050190A - Display method of effective dose of radon exposure by age or lifetime - Google Patents

Abstract

Disclosed is a display method of an average annual effective dose of radon exposure indoors in lifetime. According to the present invention, the display method of the average annual effective dose of radon exposure indoors in lifetime by estimating an average annual radon concentration indoors for each residence by using residence information of a resident, and by using the estimated value and the residence time information comprises: a step of displaying an algorithm related to the display of the average annual effective dose of radon exposure indoors in lifetime stored in a terminal on a web screen; a step of inputting the individual identification information and the residence information of the resident, in which the resident has ever lived in the displayed web screen; a step of calculating the effective dose of radon exposure indoors for each age group by using the residence information inputted by the previous step; and a step of displaying the average annual effective dose of radon exposure in lifetime calculated based on the effective dose of radon exposure indoors for each age group calculated on the web screen. The present invention is able to display the average annual effective dose of radon exposure indoors in lifetime by simply inputting residence information and residence period.

Description

How to display the effective dose of indoor radon exposure on average annually throughout life{DISPLAY METHOD OF EFFECTIVE DOSE OF RADON EXPOSURE BY AGE OR LIFETIME}

The present invention relates to an indoor radon concentration exposure effective dose, and more particularly, to a lifetime average indoor radon exposure effective dose display method that can calculate and display the annual average indoor radon exposure effective dose per individual lifetime by inputting residence information and residence time. will be.

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

It accumulates in the lungs and can cause lung cancer. It is the second major 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 living quarter, 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.

Measuring long-term indoor radon concentrations is time-consuming and expensive, and because 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 a country 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.

In addition, depending on the type of housing, for example, a single-family house and 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 radon concentration varies greatly depending on the time of measurement and housing type, and this change can be said to cause uncertainty in evaluating the effective dose and risk of lung cancer caused by exposure to radon.

Meanwhile, when products exposed to radon such as radon-releasing beds and sanitary napkins are sold on the market and become a problem, it is also true that interest in individual radon exposure is increasing using simple devices or methods.

Therefore, after calculating the average annual exposure for each individual using the indoor radon concentration estimation formula for each residence and implementing it on the web, the user needs a model that can easily estimate the average annual exposure for each individual lifetime by entering simple residence information and residence time. do.

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

An object of the present invention for solving this problem is to provide a display method capable of displaying the lifetime average indoor radon exposure effective dose by using the indoor radon concentration calculated based on the indoor radon estimation equation.

Another object of the present invention is to provide a display method capable of displaying the lifetime average annual indoor radon exposure effective dose by inputting residence information and residence time.

To solve such a problem, the method of estimating the annual average indoor radon concentration for each residence using the residence information in which a resident lives in the present invention and displaying the lifetime average indoor radon exposure effective dose using the estimated value and residence time information is: (a) Terminal Displaying on a web screen an algorithm related to displaying the effective dose of indoor radon exposure stored in a lifetime average, (b) inputting personal identification information and residence information on the displayed web screen, and (c) above Calculating the effective dose of indoor radon exposure for each age group using the residence information and residence time input in step (b), and (d) calculating the effective dose of indoor radon exposure for each age group calculated in step (c) above. It may be accomplished by including the step of displaying the effective dose of indoor radon exposure on a web screen, which is the average annual average of the lifetime.

In addition, in the step of inputting the residence information in step (b), there are four types depending on whether it is a single-family house and apartment including Western-style and hanok, or a multi-family house, and a non-single-family house including a row, and whether groundwater is used or not. The number of floors, the presence of indoor cracks, and the presence or absence of regular indoor ventilation can be additionally entered.

In addition, step (c) includes: c-1) _{estimating the indoor radon concentration (C i} ) for each dwelling place using the dwelling information, and (c-2) It may include the step of calculating the _{effective dose (E ij} ) of indoor radon exposure for each age group by inputting the residence time for each place of residence in the radon concentration (C _{i ).}

In addition, in the display of the lifetime average indoor radon exposure effective dose, the calculated lifetime average indoor radon exposure effective dose is displayed on one side of the screen, and the other side displays the exposure dose in general circumstances as reference data.

And the indoor radon exposure effective dose (E _ij ) for each age group is characterized in that it is expressed by the following equation.

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

Therefore, according to the method for displaying the effective dose of annual average indoor radon exposure for a lifetime of the present invention, there is an effect of calculating the lifetime average annual indoor radon exposure effective dose by simple residence information and residence time input without measuring the radon concentration for the actual residence.

1 is a configuration diagram of a device for calculating an effective dose of indoor radon exposure on an average annual lifetime according to an embodiment of the present invention;
2 is a flowchart illustrating a method of displaying an effective dose of indoor radon exposure on a lifetime average according to an embodiment of the present invention;
3 is a flow chart for explaining a method for estimating indoor radon concentration for each residence;
4 is a summary table of average annual radon concentration according to residential type and groundwater usage,
5 is a coefficient estimation table of a penetration coefficient model according to residence time characteristics,
6 is a comparison table of the calculated value and the estimated value of the penetration coefficient S according to the house type,
7 is a comparison table of calculated and measured values of indoor radon according to the type of house,
8 is a diagram illustrating an initial screen of an algorithm displayed on a web screen according to an embodiment of the present invention;
9 is a diagram illustrating a web screen in which residence information can be input;
10 is a web screen displaying a list of created residences;
And
FIG. 11 is a diagram illustrating a screen for displaying a lifetime average indoor radon exposure effective dose and a situation according to the average radon exposure according to an embodiment of the present invention.

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 describe 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 specifically stated to the contrary. 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 items. It means a combination of all possible items.

In the entire specification, the identification code (for example, a, b, c, ...) 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 the specified order, 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.

2 is a flowchart for explaining a method of displaying an effective dose of indoor radon exposure on an average annual indoor radon according to an embodiment of the present invention. Before explaining the step of inputting residence information on the initial screen of the displayed web screen (S10), A method of estimating indoor radon concentration and calculating the annual average indoor radon exposure effective dose using this will be described.

first. Referring to the configuration diagram of the device for calculating the effective dose of annual radon exposure using the method for estimating the concentration of indoor radon in a house according to an embodiment of the present invention of FIG. 1, the device for predicting the annual exposure dose of radon is a radon sensor 10 ), a controller 20 and an output device 30, and may be installed inside the residence.

In addition, the device for predicting the annual exposure dose of radon may be configured as a terminal including the controller 20, the output device 30, and the storage 23, of course.

Of course, the output device 30 at this time can be of course configured as a display unit configured in the terminal.

The radon sensor 10 detects the concentration of radon contained in the indoor air. When the controller 20 predicts the annual exposure dose of radon according to the method described below and outputs it to the output device 30, it displays the lifetime average indoor radon "effective exposure dose" on the web screen of the present invention.

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 annual average radon concentration for each residence reflecting the average concentration of radon and meteorological factors measured for a certain month in a certain residence and a program for estimating the effective dose of radon exposure at the residence. It stores an algorithm that can display the lifetime average annual indoor radon exposure effective dose by entering residence information.

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 every month from the meteorological office, and stores the data in the storage 23 as a DB. At this time, the average indoor radon concentration data of the seasonal residential 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 is configured as a display unit or a transmission unit and operates to receive the effective dose of radon exposure calculated by the processor 22 and display it on a web screen or output to the outside through a transmission unit.

We will examine in detail how to construct the mathematical prediction model used to calculate the annual effective dose of radon and how to determine the monthly correction factor reflecting the meteorological factors in detail.

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 indoor air shows seasonal differences, so the cumulative radon concentration for three months by season is insufficient to represent the average radon concentration for one year. Therefore, the prediction of the annual exposure dose of radon based on the measured value of the mean concentration of radon for three months by season is greatly affected by the season, 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 constructing an equation that can estimate 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 season Regardless, the annual exposure dose to radon can be calculated. In addition, a more realistic annual exposure dose can be calculated because 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 the monthly average wind speed. 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 measures to reduce radon in consideration of the annual exposure dose of radon. I can.

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 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 model, 47 selected outliers were removed from the log-transformed radon data, and a model was developed using 1,343 settlements.

Since the indoor radon concentration was measured regardless of the season, the indoor radon concentration was converted to the annual average radon concentration at home using the seasonal correction factor described by Park et al. in order to correct the seasonal periodicity of the data obtained from the radon investigation. .

Figure 4 summarizes the average annual radon concentration according to the house 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 surveyed 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 with three floors or less 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 for 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 percentage of green space.

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 release rate of the building material (Bqm ^-2 h ^-1 ), E _s is the effective soil release rate of radon, f _t and f _w are the fitting coefficients, and k _a is the advection transfer coefficient of the soil (mPa ^-1 h ^-1 ), k _d,s = the 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 ), and S _g is the ground. Facing building 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 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 (Bqm ^-3 ), C _o is radon concentration in outdoor air (Bqm -3) ^-3 ), E _bm is the effective radon release rate of building materials (Bqm ^-2 h ^-1 ), E _s is the effective soil release rate of radon, f _t and f _w are fitting coefficients, N is the number of ventilation, S _bm is the building material The indoor surface area of radon containing (m ² ), S _g is the building area facing the ground (m ² ), Ti and To are the indoor and outdoor temperatures (℃), u is the wind speed (ms ^-1 ), and λ is the radon attenuation. The constant (h ^-1 ), λ _v means the ventilation rate (h ^-1 ).

Although Equation 2 is the final equation for calculating the indoor radon concentration. It is impractical to apply the above equation as it is. For example, _{the calculation of "E s *} (S _g /V) + E _{bm *} (S _bm /V)" requires information on radon release rates from soil and building materials. Since the possible information of the input values is limited, _{in order to propose a regression model that can estimate "E s *} (S _g /V) + E _{bm *} (S _bm /V)", the infiltration coefficient model (infiltration coefficient model) is as follows. factor model).

Penetration coefficient model

"E _{s *} (S _g /V) + E _{bm *} (S _bm /V)" in Equation 2 represents the infiltration factor from soil and building materials, and is represented by S. In addition, 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 influenced 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 residential floor i, and ε _i is the measurement error of the residence i.

S in Equation 3 is a value calculated based on the 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 is constructed (regression model), and S _i is an equation that can estimate S with the information of the i-th house even without actual measured values, 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), and β ₀ Based on the values of, β ₁ , β ₂ , β ₃ , β ₄ , and β ₅ and residence information (X1 to Y3), S can be estimated even if there is no actual radon concentration measurement value.

Model application

By applying the research data to Equations 3 and 4, the parameters of the model are estimated by separating them into the four types shown in Table 1 below so that the parameters of the model can be estimated.

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

Deciding the type of residence 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, it _{is described as indoor radon concentration (C i} ) for each residence, but it should be 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 house type.

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 of residence is a coefficient extracted from research data.

That is, it is a coefficient extracted by inputting research data and other data of 1,343 residences based on the disclosed public data.

The comparison of the calculated value and the estimated value 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 coefficient of penetration from soil and building materials for individual houses must be obtained.

Therefore, S is calculated using Equation 3 and the parameter of Equation 4 is estimated using it. As can be seen in Table 1, since the indoor radon concentration in a single-family house is higher than that in a non-detached house, it must be estimated differently depending on the type of house.

Radon in groundwater is known to affect indoor radon concentration, but information on radon in groundwater is insufficient in Korea. In order to consider the effect of house type and groundwater on indoor radon concentration, the model was divided into 4 types according to house type and groundwater usage, and the penetration coefficient S was estimated. Therefore, we have integrated Equation 3 and Equation 4 to predict the _{indoor radon concentration C i by residence.} Finally, predictive ability was evaluated for each of the four types of residence.

Assumptions of the input variable for calculating S in Equation 3 and the parameter 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.

Radon used in the present invention The radon survey data nationwide on the measured radon was 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 were the same as those of Park et al. In addition, the N value was assigned differently according to the resident's 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 indoor radon levels in administrative districts were found to be statistically significant (P<0.05).

However, the influence of building materials was not statistically significant or significant. As the number of cracks increased, the penetration factor value also increased, but it was not statistically significant (P>0.05). In addition, the greater 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). On the other hand, the calculated ventilation rate is from 0.24 to 0.73 h ^-1 (AM = 0.34 ± 0.08 h ^-1 )). The estimated values of were compared (see Fig. 5).

6 is a comparison table of the calculated and estimated values of the penetration coefficient S according to the type of residence. The model showed better performance in the non-detached house than in the detached house, but followed a similar trend overall.

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

7 is a comparison table of the measured and estimated values of indoor radon concentration according to the type of residence, and is a degree of agreement between the measured indoor radon concentration and the estimated indoor radon concentration. The results showed that the results were better for non-single houses than for single-family houses in the penetration coefficient model, so when estimating indoor radon concentration, the agreement was better for non-single houses than for single-family houses. In the case of type 3, the number of target samples was small, but the variance was small, so the agreement was best. Our model outperformed the previous model, except for a few houses with very high radon concentrations.

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

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

"0.4*9*10 ^-6 " is a constant required to calculate the exposure E and is the product of the dose conversion coefficient and the equilibrium factor between radon and radon progeny.

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

Referring to Table 6, for example, E ₃ and 4 refer to the effective dose of indoor radon exposure for those aged 35 to 44 in Jung-gu, Daejeon, the third place of residence.

In other words, the effective dose of indoor radon exposure in an individual's lifetime is to estimate the indoor radon concentration in a 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.

Referring again to FIG. 2, a method of displaying the effective dose of indoor radon exposure on a lifetime average of the present invention will be described.

Referring to the drawings, the device for calculating the lifetime average indoor radon exposure effective dose drives an algorithm that can display the lifetime average indoor radon exposure effective dose only by inputting the simple residential information of the present invention stored in the storage unit 23 on the web screen. Do (S10).

When the algorithm is driven in step S10, the output unit 30 composed of a display unit may display an initial screen as shown in FIG. 8.

8 is a diagram illustrating an initial screen of an algorithm displayed on a web screen according to an embodiment of the present invention. At the top, a step-by-step selection screen for selecting a personal lifetime cumulative radon exposure estimation system step by step is "STEP1, STEP2, STEP3". Is displayed, and the initial screen is the "STEP1" screen.

In the "STEP1" screen, an introduction to the basic estimation system and explanation of a simple input method and the possibility that it may be different from the actual value because it is a representative value based on research data are mentioned in the upper part, and in the lower part, "STEP1. Along with the description of "Generate", a "Residence Creation Key (K1)" for creating a residence along with the user's identification number is displayed.

In general, the user identification number may be a code that can be identified for each user, such as a resident registration number or a mobile phone number, but in the present invention, it is provided to input the date of birth for convenience.

When the initial screen is displayed in step S10, a residence information input step of inputting identification information including residence information is performed (S20).

If you enter the four-digit year and two-digit month (K21) and click the residence creation key (K1), a web screen for entering residence information as shown in Fig. 9 is displayed as a residence list screen and related data is input. Performs the step of creating a list of residences (S22).

9 is a screen for creating a list of residences, and a screen for sequentially inputting residence power according to the movement of the residence.

On the "Residence list" screen, a field is provided at the top to input the time of residence, age of residence, and average daily residence time.

The time of residence is entered sequentially in the order of residence 1, residence 2, and so on.

Specifically, if you enter “10-19 years of age” as an example of the period of residence in Residence 1, and click Confirmed (K2), the residence age entered on the right screen is displayed, and the daily average residence time is entered in the input field. If you enter the residence time as "13.7" hours as an example and click the confirmation key (K3), the data is entered.

When the upper data is input, the residence information displayed at the bottom of the screen is input.

In the residence information input field, a screen for inputting the address, type of house, house wall material, number of floors, indoor cracks, use of groundwater, and indoor ventilation is displayed.

In the housing type, it is indicated to select whether it is a single (hanok) house, a single (western house) house, an apartment, a multi-family house, and a row house, and the house wall itself is concrete, cement block, red brick, soil (ocher), wood, etc. And so on.

In addition, in the indoor crack category, if there is a crack, you can select from wall, floor, ceiling, seam, etc., and in case of using groundwater, drinking water and washing dishes. It is desirable to configure it to be able to select laundry, shower, car wash, and other items.

When data for the above item is input, the data is saved by pressing the "Save Current Residence Information" key K4 with the input information at the bottom of the screen.

Of course, if there is uninputted data, the screen can be configured so that the data can be entered again when saving.

Next, when looking at the web screen displaying the list of places of residence created in FIG. 10, an input screen for "Residence 2" is displayed, and the age of residence is input in the same manner as the input screen for "Residence 1", and related data is input.

Therefore, it is possible to complete the residence information by inputting data for each residence on the web screens shown in FIGS. 9 and 10.

For example, if you lived in residence 2 from 20 to 64 years old, enter the average residence time by age as shown in the right screen.

Since you lived from 10 to 19 years of age on the Residence 1 screen, the ages on the Residence 2 input screen are classified into "20~24", "25~34", "35~44", "45~54", and "55~64". Therefore, it is desirable to enter the average daily residence time separately.

The processor 22 calculates an effective exposure dose when all the residence information is input (S100).

A detailed method of calculating the effective exposure dose will be described with reference to the above.

3 is a flowchart for explaining a method of calculating a lifetime average radon exposure effective dose according to an embodiment of the present invention. As shown, a lifetime average radon exposure effective dose prediction method is performed by the controller 20 as follows. It consists of the steps to be performed. 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 irradiation. The method of predicting the effective dose of annual average radon exposure shown in FIG. 3 may be performed by the processor 22 of the controller 20 executing an estimation program stored in the storage 23.

In step S100, the annual average radon exposure effective dose for a lifetime is calculated _{by estimating the indoor radon concentration (C i} ) for each residence (S110 to S120) and by entering the residence time for each age group in the _{extracted C i to calculate the effective dose of indoor radon exposure throughout the individual lifetime.} It may consist of a process of calculating (S130 to S140) and a process of calculating an effective dose of annual average radon exposure throughout a lifetime (S150).

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

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

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

When data input for the residence is completed in step S110, the controller 20 calculates an estimated indoor radon concentration (Ci) for each residence in the corresponding residence stored in the storage 23 (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 average daily 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 age groups by dividing it in units of approximately 10 years.

This is because even though the average daily residence time in the corresponding residence may be predicted, 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 by dividing it in units of about 10 years.

In other words, if the average daily residence time for each residence is input in step S131, the residence time for each input age group is calculated (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 in 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 for each age group (S140).

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

Referring to Table 6, 10 years from residence 1 to 10-19 years old, 5 years from residence 2 20 to 24 years old, 10 years from residence 3 to 25 to 34 years old, 10 years from 35 to 44 years old, 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 ages are all 55 years, so dividing the effective dose of indoor radon exposure calculated in step S140 by 55 years This is the calculation of the average lifetime indoor radon exposure effective dose.

When the lifetime average indoor radon exposure effective dose is calculated in step S100, the exposure effective dose may be displayed on the web screen (S200).

Referring to the diagram illustrating the display of the lifetime average indoor radon exposure effective dose and the situation according to the average radon exposure in FIG. 11, the calculated lifetime average indoor radon exposure effective dose is displayed on one side of the screen, and the other side The amount of exposure under normal circumstances should be indicated as reference data.

Specifically, the calculated lifetime average indoor radon exposure effective dose is displayed on the top of the screen in the item "Effective exposure dose".

In the drawing, "Effective Exposure Dose" is displayed in units of "mSv/yr", and in the lower part, "0.025~0.1mSv" of radon exposure during chest X-ray is displayed first, and the arctic route between Seoul and New York is displayed in the second line The amount of radon exposure when traveling by plane is expressed as "0.15 mSv", and the amount of exposure under normal circumstances is indicated as reference data.

As described above, according to the method for displaying effective dose of annual average indoor radon exposure according to the present invention, if simple residential information is input using a device such as a terminal, the average annual effective dose of indoor radon exposure during a personal lifetime can be simulated. You can escape, and if it is judged as a risk to your health, you can take appropriate measures.

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 (6)

In the method of estimating the annual average indoor radon concentration for each dwelling place using information on the dwelling place where the resident resides, and displaying the annual average indoor radon exposure effective dose for a lifetime using the estimated value and dwell time information,
(a) displaying an algorithm related to the display of the effective dose of indoor radon exposure on a lifetime average stored in the terminal on a web screen;
(b) inputting personal identification information and residence information on the displayed web screen;
(c) calculating the effective dose of indoor radon exposure for each age group using the residence information and residence time input in step (b); And
(d) displaying a lifetime average indoor radon exposure effective dose calculated based on the indoor radon exposure effective dose for each age group calculated in step (c) on a web screen;
Lifetime average indoor radon exposure effective dose display method comprising a.
The method according to claim 1,
In the step of entering the residence information in step (b),
A method of displaying the average annual indoor radon exposure effective dose for life, classified into four types depending on whether it is a single-family house and apartment or multi-family house including Western-style and Hanok houses, and non-single-family houses including row houses, and whether groundwater is used or not.
The method according to claim 2,
In the step of entering the residence information in step (b)
A method of displaying the annual average indoor radon exposure effective dose for a lifetime, further including the number of floors, the presence of indoor cracks, and the presence or absence of regular indoor ventilation.
The method according to claim 2,
Step (c) is
(c-1) estimating the _{indoor radon concentration (C i} ) for each residence using the residence information; And
(c-2) calculating an _{effective indoor radon exposure dose (E ij} ) for each age group by inputting a residence time for each residence to the _{indoor radon concentration (C i} ) for each residence estimated in step (c-1);
Lifetime average indoor radon exposure effective dose display method comprising a.
The method according to claim 1,
The above lifetime average indoor radon exposure effective dose display
The calculated lifetime average indoor radon exposure effective dose is displayed on one side of the screen and the exposure dose under normal circumstances is displayed on the other side as reference data.
The method of claim 4,
_{The effective dose (E ij} ) of indoor radon exposure by age group is
A method of displaying an effective dose of indoor radon exposure on a lifetime average, characterized in that it is expressed by the following equation.

Where E _ij is the effective dose of indoor radon exposure by age group, C _i is the indoor radon concentration by place of residence, and (Tij*365day*Yij(unit: year)) is the residence time by age group. Means.

Images