JP4206024B2 - Atmospheric substance index distribution analyzer - Google Patents

Description

  The present invention relates to an atmospheric substance index distribution analyzing apparatus for simulating various index distributions (concentration distribution, temperature distribution, etc.) of a prediction target substance in the atmosphere.

  In recent years, improvement of the atmospheric environment has been demanded from the viewpoint of global environmental conservation, and when constructing a large-scale factory or a variety of facilities such as roads that release pollutants into the atmosphere, the planning and planning stage is of course. Of course, it is necessary to understand quantitatively the state of advection and diffusion of the pollutant to the surrounding area even during the operation of the facility, and take measures if there is a risk of problems.

  Conventionally, in order to predict such pollutants released into the atmosphere (hereinafter referred to as “air pollutants”), the diffusion distribution of target air pollutants is predicted by combining empirical formulas. Although the method was used, an accurate and detailed prediction could not be made, and therefore, an atmospheric environment simulation system 90 using an electronic computer has been proposed (FIG. 7).

The simulation system 90 includes an input unit 91 for simulation condition data, a database 92 having data of analysis examples, laws / guidelines, simulation models, and measurement values, a simulation model, its fixed parameters, and analysis conditions. The model selection means 93 selected from the above and the numerical simulation of the diffusion distribution state of the pollutant are performed, and the adjustment parameter is set so that the error between the measured value in the database 92 storing the actual measured value in a similar environment is reduced. The parameter tuning means 94 for selecting the data, the numerical calculation processing means 95 for executing the numerical simulation of the diffusion distribution state of the pollutant, the data storage means 96 for storing the analysis result data and the map data, and the numerical calculation processing means 95. It is configured as a device having a display processing unit 98 for displaying a map according to calculation results to the map data on the display unit 97 (see Patent Document 1).
JP 2000-19742 A ([0007]-[0036], FIG. 1)

  However, the simulation system 90 is created for the purpose of predicting atmospheric substances at the regional scale (several km square to several hundred km square) at the prefectural or municipal level, and is an area narrower than a few km square. (For example, in urban areas, etc.), there is a problem that the concentration of local atmospheric substances cannot be analyzed in consideration of the shape and height of individual structures.

  An object of the present invention is to solve the above-mentioned problems, and it is possible to analyze and predict various index distributions of local atmospheric substances quickly, simply and accurately, and analyze atmospheric substance index distributions. An object is to provide an apparatus.

  In order to solve the above problems, an atmospheric substance index distribution analyzer of the present invention (first invention) includes a map database storing two-dimensional map data including planar shape data relating to structures and emission sources, and the structure Structure data in which height data relating to objects and emission sources are stored in association with the two-dimensional map data, operational characteristic data relating to emission sources that emit atmospheric substances, chimney height data, and discharged air Emission source data including substance name data, emission intensity data and emission substance temperature data is stored in association with the two-dimensional map data, and temperature data for each predetermined region of the two-dimensional map data. A weather information database that stores weather data such as wind direction data, wind speed data, solar radiation data, and cloud cover data; An analysis target area extracting means for extracting an analysis target area from the map database, and the planar shape data of the structure and the emission source in the two-dimensional map data of the analysis target area, the corresponding structure in the structure database By combining the height data, the three-dimensional data generating means for generating the three-dimensional map data, and the three-dimensional map data of the analysis target area to combine the emission source data related to the emission sources Emission source data creation means, weather data creation means for extracting each weather data corresponding to the analysis target area from the weather information database, and the analysis target area into a plurality of minute divided areas having a predetermined size With respect to the divided area data creating means for dividing and a plurality of predetermined wind direction data, the wind speed data and the disturbance Given the condition of In addition, in the case where the airflow data storage means for storing the airflow data in the minute divided areas in association with each other and the meteorological data and the emission source data are given under predetermined conditions, the minute data A diffusion index calculating means for calculating a diffusion index of atmospheric substances in the divided areas, the diffusion index calculating means corresponding to the predetermined wind direction data of the airflow extracted from the airflow data storage means Obtaining a diffusion index of atmospheric substances in each of the finely divided regions by numerically solving a diffusion governing equation of the diffusion index under flow conditions It is said.

Here, as the atmospheric substance to be predicted, any substance can be selected. For example, nitrogen oxide (NOx), sulfur oxide (SOx), carbon monoxide (CO), suspended particulate matter (SPM) Air pollutants such as dioxins and odors can be targeted.
The atmospheric material diffusion index may be an index that can be analyzed by the diffusion governing equation, and representative indexes are atmospheric material concentration, atmospheric material temperature, and the like.
The source of the target substance differs depending on the target substance. For example, in the case of air pollutants, large-scale factories, surface smoke sources such as construction sites (construction machines), vehicles that are mobile emission sources, etc. Line smoke sources such as roads that run, and point smoke sources such as power plants, incineration facilities, and parking lots are applicable.

  In addition, the atmospheric substance index distribution analyzer of the present invention (second invention) includes a map database in which three-dimensional map data including plane shape data and height data relating to structures and emission sources is stored, and atmospheric substances. Emission source data including operating characteristic data, chimney height data, discharged atmospheric substance name data, emission intensity data, and emission substance temperature data relating to the emission source to be emitted is stored in association with the two-dimensional map data. Emission source database, weather information database storing temperature data, wind direction data, wind speed data, solar radiation data, and cloud data for each predetermined area of the 3D map data, and analysis from the map database The analysis target area extracting means for extracting the target area, and each of the emission source data, the three-dimensional map data of the analysis target area In addition, emission source data creation means for combining the emission source data relating to the emission sources, and weather data creation means for extracting the weather data corresponding to the analysis target area from the weather information database; , Divided region data creating means for dividing the analysis target region into a plurality of minute divided regions having a predetermined size, and given wind speed data and turbulence conditions for a plurality of predetermined wind direction data In this case, by numerically solving the three-dimensional advection diffusion governing equation, the air flow analyzing means for obtaining the flow of air flow in each of the minute divided regions, the wind direction data, and each of the air flow analyzing means obtained by the air flow analyzing means Airflow data storage means for storing the airflow data in the minute divided area in association with each other, and each weather data and each exhaust under a predetermined condition. Diffusion source calculation means for calculating a diffusion index of atmospheric substances in each of the minute divided regions when the source data is given, the diffusion index calculation means corresponding to the predetermined wind direction data Obtaining a diffusion index of atmospheric substances in each of the minute divided regions by numerically solving a diffusion governing equation of the diffusion index under a flow condition of the air flow extracted from the air flow data storage means. It is a feature.

  Further, in the atmospheric substance index distribution analysis device of the present invention, the airflow analysis means includes the advection diffusion composed of a Naviestokes equation and a continuous equation using the motion elements of the velocity of the airflow and the direction of the airflow as variables. For the governing equation, the velocity of the airflow and the airflow in each of the divided regions when it is repeatedly calculated using a numerical analysis by a difference method for each of the minute divided regions and every minute time, and when a predetermined equilibrium state is obtained. It is preferable to configure so that the value of the direction can be determined.

  Further, in the atmospheric substance index distribution analyzer of the present invention, the diffusion index calculation means uses a difference method for the diffusion governing equation of the diffusion index of the target atmospheric substance for each of the minute divided regions and for each minute time. It is preferable that the calculation is performed repeatedly using numerical analysis to determine the value of the diffusion index of atmospheric substances in each of the divided regions when a predetermined equilibrium state is reached.

In the atmospheric substance index distribution analyzer of the present invention, a virtual structure and a virtual structure that are not recorded as the two-dimensional map data (or three-dimensional map data, hereinafter may be referred to as “two-dimensional map data”). Structure condition setting means that plays a role of combining the position and height data of the discharge source with the two-dimensional map data or the like may be provided.
Further, emission source data creating means for determining emission source data such as the type of virtual emission source and emission source conditions and for combining with the two-dimensional map data of the emission source may be provided.

  In this way, the atmospheric material index distribution can be analyzed by setting or changing the virtual structure or the virtual emission source. Therefore, in the planning stage of various development plans in the analysis target area, the atmospheric material The index distribution can be predicted. Therefore, based on the result, it is possible to take appropriate measures in advance, and to change the plan as necessary.

Furthermore, in the atmospheric substance index distribution analyzer of the present invention, if the analysis result display means for displaying the calculation result of the atmospheric substance index distribution calculated by the analysis calculation unit is provided, the calculation result is displayed on the output means. It is very suitable because it is possible to easily make a judgment or the like by visual recognition.
Further, in the atmospheric substance index distribution analyzer of the present invention, if the airflow analyzing means includes a wind direction correcting means for dividing the wind direction into an arbitrary angle, a more accurate analysis can be performed.

  According to the present invention (the first invention and the second invention), it is possible to predict various indicator distributions of local atmospheric substances quickly, simply and accurately by performing a minimum necessary operation.

  Further, in the first invention, a map database storing two-dimensional map data including planar shape data relating to structures and emission sources, and height data relating to the structures and emission sources are associated with the two-dimensional map data. The height data of the corresponding structure in the structure database is combined with the structure database stored and the planar shape data of the structure and the emission source in the two-dimensional map data of the analysis target area. By doing so, instead of the three-dimensional data generating means for creating the three-dimensional map data, the map database storing the three-dimensional map data including the planar shape data and the height data regarding the structure and the emission source Also in the second aspect of the invention having the configuration, it is possible to achieve the same effect as the first aspect of the invention.

A best mode for carrying out the present invention (hereinafter referred to as “embodiment”) will be described in detail with reference to the drawings.
In the present embodiment, the atmospheric substance concentration is used as an index of the atmospheric substance to be analyzed, and the atmospheric substance concentration distribution analyzing apparatus 1 will be described as an example of the atmospheric substance index distribution analyzing apparatus.

<Atmospheric substance concentration distribution analyzer>
As shown in FIG. 1, an atmospheric substance concentration distribution analysis apparatus 1 (hereinafter referred to as “this apparatus”) according to the present invention includes a main body 2 composed of a computer, an input means 3 and an output means 4 as main parts. It is configured as.

The input means 3 is a means for inputting instruction information for various operations requested from the input request unit 20 in the apparatus body 2, and includes a keyboard 3a, a mouse 3b, a recording medium reading device 3c, and the like.
The output means 4 is a means for outputting various analysis results, data, and the like, and includes a display 4a, a printer 4b, a recording medium writing device 4c, and the like.

  The apparatus body 2 includes a storage unit 10, an input request unit 20, an analysis data creation unit 30, an analysis calculation unit 40, an analysis result display unit 50, and a control unit 60 as main parts.

(1) Storage Unit The storage unit 10 is an element that plays a role of storing various data and the like, and includes a database 11 and an analysis result storage unit 18b.

Database The database 11 includes a map database 12, a structure database 13, an emission source database 14, a weather related database 15, an air pollution concentration measured value database 16, and an analysis model database 17.

[Map database]
The map database 12 stores so-called electronic housing map data, and the position coordinate data of various structures and emission sources in a wide area (national) and its planar shape data are stored as digital map data (two-dimensional map data). At the same time, data of names and floors of various structures (when the structure has a floor attribute) are stored (stored) in association with the two-dimensional position coordinate data.
The structures include various facilities such as office buildings, commercial buildings, condominiums, houses, public facilities, transportation facilities, roads, railway tracks, and the like.

[Structure database]
The structure database 13 includes name data, type data (office buildings, commercial buildings, condominiums, houses, public facilities, transportation facilities, roads, railroad tracks, etc.), floor data, and height of each region. A structure characteristic value data group related to data or the like is stored in a state associated with two-dimensional position coordinate data in the digital map data.

[Emission source database]
The emission source database 14 includes main emission source name data, emission source type data (surface smoke source, line smoke source, point smoke source, etc.), and actual chimney height data (hereinafter “actual chimney height data”). Emissions regarding 3D position coordinate data of emission sources, substance name data of discharged air pollutants, emission intensity (emission amount) data, operating characteristics of emission sources, temperature data of discharged air pollutants, etc. A source characteristic value data group is stored in association with the two-dimensional position coordinate data of the emission source in the digital map data.
Here, in the case of a point smoke source, three-dimensional position coordinate data of the emission center is stored (for example, in the case of an underground parking lot, in the case of an exhaust port position, in the case of a ground parking lot, In the case of incineration facilities, multistory parking lots, etc., chimneys and louvers correspond to the emission center).

  In addition, each emission source differs according to 365 days and every time zone (every hour) as well as the kind and emission amount of the air pollutant to be discharged differ according to the type and the size. Since it has operation characteristics, the operation characteristics for each 365-day time zone are stored as operation rate data corresponding to each emission source.

[Meteorological database]
The weather related database 15 includes a weather information database 15a, an atmospheric stability database 15b, and a diffusion coefficient database 15c.

◎ Meteorological information database In the weather information database 15a, basic weather data groups related to temperature data, wind direction data, wind speed data, solar radiation data, and cloud data are stored for a predetermined time for each predetermined area of the two-dimensional map data in the map database 12. It is stored as each data.
In this embodiment, the wind direction is 16 azimuth data (north, north northeast, northeast, etc.), and the wind direction and wind speed data store average wind direction data and wind speed data for 10 minutes.

◎ Atmospheric Stability Database In the atmospheric stability database 15b, there is a data group indicating the contents of the Paskir Atmospheric Stability Classification (Japanese style, 1959) in order to determine the atmospheric stability, which is an index indicating the state of the atmosphere. Stored.
This Paskir Atmospheric Stability Classification Table is based on A, AB, B, depending on the wind speed 10m above the ground (category classified so as to be within the range of the predetermined value), solar radiation, and cloud cover. , BC, C, CD, D, E, F, and G (Table 1).

Diffusion coefficient database The diffusion coefficient database 15c stores diffusion coefficient (horizontal diffusion coefficient and vertical diffusion coefficient) data indicating the degree of diffusion of air pollutants. Since this diffusion coefficient is determined depending on the atmospheric stability and the wind speed, it is stored as a value corresponding to the atmospheric stability classification and a predetermined wind speed classification obtained by a hot wind tunnel experiment.

[Air pollution concentration actual value database]
The air pollution concentration measured value database 16 stores measured value data (hereinafter referred to as “hereinafter referred to as“ NO _X , SO _x , CO, CO ₂ , SPM, etc.) of a predetermined air pollutant (eg, NO _x , SO _x , CO, CO ₂ , SPM, etc.) at a preset constant monitoring measurement station. Concentrated actual measurement data ”(sometimes referred to as“ concentration actual measurement data ”) is stored in association with the two-dimensional position coordinate data of the constantly monitoring measurement station in the two-dimensional map data, by air pollutant, 365 days, and by time zone (10-minute value) Has been.

[Analysis model database]
The analysis model database 17 stores a model formula group used in the later-described analysis calculation unit 40, various parameter groups used in the model formula group, and various data groups created by the analysis data creation unit 30. Yes.

Airflow data storage means and analysis result storage means The airflow data storage means 18a can store predicted wind speed data and predicted wind direction data calculated by the airflow analysis unit 41 described later.
The analysis result storage unit 18b can store various analysis results calculated by the later-described analysis calculation unit 40.

Various data groups and the like stored in the storage unit 10 can be extracted via the stored data management means 19 and a predetermined operation can be executed. It can be stored in an appropriate location.
The storage unit 10 has a program storage unit (not shown), and stores programs for driving the entire system and executing various operations.

(2) Input request unit The input request unit 20 is an element that plays a role of requesting the operator to perform various input operations. The analysis target region input request unit 21, the structure condition input request unit 22, and the discharge Source condition input request means 23, analysis condition input request means 24, and operation input request means 25 are provided.

[Analysis target area input request means]
The analysis target area input requesting means 21 is a means for requesting the operator to input for selecting the two-dimensional map data of the analysis target area R from the map database 12.
The analysis target area input requesting means 21 performs an operation for requesting the operator to input an area desired to be analyzed on the display 4a on which wide-area two-dimensional map data is displayed at a predetermined scale. Become. In response to the request, when the operator designates the position on the screen using the input means 3 (FIG. 2), the analysis target area R is determined via the analysis target area extraction means 32 to be described later, and on the screen. (FIG. 3).

[Structural condition input request means]
The structure condition input requesting means 22 requests the operator to input related to correction of the height dimension of the structure and, if necessary, requests input of structure characteristic value data related to the virtual structure. It is.

For example, the structure condition input requesting means 22 is stored in the structure database 13 by causing the operator to specify the target structure on the analysis target area R displayed on the display 4a. The corresponding structure characteristic value data is displayed on the screen, and it is possible to request that the necessary data be input or selected, or that the value be corrected.
Further, this means requests the input of the position of the virtual structure and the structure characteristic value data, and when the operator designates the installation position using the input means 3, the position is determined, and the structure condition setting means 33 described later The two-dimensional position coordinate data and the like are recognized through the.

[Emission source condition input request means]
The emission source condition input requesting means 23 is a means for requesting the operator to input and correct various conditions relating to the emission source and, if necessary, requesting the input of emission source specific value data relating to the virtual emission source. is there.

For example, the emission source condition input requesting means 23 displays various emission source data stored in the emission source database 14 on the screen displayed on the display 4a, and is necessary for the operator. It is possible to request that data be input or selected, or that the value be corrected.
In addition, this means requests the operator to input the position of the virtual emission source and the emission source specific value data on the analysis target area R displayed on the display 4a. In response to the request, when the operator designates the position of the emission source shown on the screen using the input means 3, the position and the emission source specific value data are determined, and the emission source data creation means 35 will be described later. Thus, the three-dimensional position coordinate data is recognized.

[Analysis condition input request means]
The analysis condition input request unit 24 is a unit that requests the operator to input various analysis conditions necessary for performing an analysis by the analysis calculation unit 40.
This means requires input of the total number of minute divided regions (cells), initial conditions and boundary conditions for performing the finite difference analysis on the screen displayed on the display 4a. Can designate predetermined data using the input means 3.

[Operation input request means]
The operation input request means 25 is an element for playing a role of requesting the operator to perform various operations of the entire system other than the above.

(3) Analysis data creation unit The analysis data creation unit 30 is an element for creating various data groups used in the analysis calculation unit 40, and includes target area data creation means 31, emission source data creation means 35, Meteorological data creation means 36, divided region data creation means 37, and analysis condition data creation means 38 are provided, and the data group created by each means is stored in the analysis model database 17. .

Target area data creation means The target area data creation means 31 is a means for creating an analysis target area data group serving as a reference for performing analysis in the entire analysis target area R. The analysis target area extraction means 32 And a structure condition setting means 33 and a three-dimensional data conversion means 34.

[Analysis target area extraction means]
The analysis target area extraction means 32 extracts the analysis target area R inputted by the request of the analysis target area input request means 21 from the map database 12 and determines it as the two-dimensional map data of the analysis target area R, and the analysis model database 17 means for storing.

[Structure condition setting means]
The structure condition setting unit 33 receives the structure condition (including the virtually input virtual structure condition) and the virtually input virtual structure, which are input in response to the request from the structure condition input request unit 22. 2D position coordinate data of the object is recognized as data in a format corresponding to the position coordinate data of the 2D map data, and structure characteristic value data of each structure stored in the structure database 13 is extracted. It is an element that plays a role of associating the two-dimensional position coordinate data of the structure in the two-dimensional map data with the structure characteristic value data and connecting to the two-dimensional map data.

[Three-dimensional data conversion means]
The three-dimensional data conversion means 34 combines the analysis height data (analysis space height data) with the two-dimensional map data in the analysis target region R so as to match the analysis conditions, thereby generating the analysis space in the three-dimensional space. It is a means for setting. Further, by integrating the structure height data in the structure characteristic value data stored in the structure database 13 with the name of the structure included in the analysis target area R, the height of the structure is added to the two-dimensional map data. 3D map data (hereinafter referred to as “analysis target area data”) is created by combining the data and stored in the analysis model database 17.

Emission source data creation means The emission source data creation means 35 extracts the emission source characteristic value data of the corresponding emission sources stored in the emission source database 14 for the emission sources included in the analysis target area R, and This is a means for associating the three-dimensional position coordinate data of the emission source associated with the map data with the emission source characteristic value data, and combining it with the analysis target area data.
In the present embodiment, the three-dimensional position coordinate data of the line emission source is handled as a set of point emission sources at predetermined intervals for the convenience of calculation processing.

  The emission source data creating means 35 includes an effective chimney height setting means 35a, and the effective chimney height data calculated by the means is combined with the emission source characteristic value data group.

The effective chimney height setting means 35a adds an increase (H _t ) due to buoyancy and an increase (H _m ) due to an output to the actual chimney height data (H ₀ ) at the emission source, thereby increasing the effective chimney height. This is means for calculating the data (He) (Equation 3-1).
He = H ₀ + H _t + H _m (3-1)
This effective chimney height data can be calculated using a well-known equation, but it is generally known that it varies depending on the wind speed and the size of the smoke source. For example, the Moses-Carson equation (when wind is present) In the case of a large-scale smoke source), the CONCAWE formula (when there is a wind, in the case of a medium-scale smoke source), the Briggs formula or the like can be used when there is no wind.
When the emission source is a road, road height data is used as the actual chimney height data.

◎ Meteorological data creation means The meteorological data creation means 36 obtains each data of wind direction data, wind speed data, solar radiation data, and cloud data corresponding to the analysis target area R from the weather information database 15a (hereinafter "analysis target"). It is a means of extracting “local weather data”.

Divided area data creating means The divided area data creating means 37 is a means for creating a plurality of minute divided areas (cells) in the three-dimensional analysis space.
The divided area data creating means 37 is a three-dimensional mesh pattern in the plane direction and the height direction in the simulation space other than the structure in the analysis space so as to have a predetermined dimension (or the total number of divisions). The three-dimensional position coordinate data of the lattice points of each minute divided region can be set and stored in the analysis model database 17.
Note that this minute divided region is an analysis unit for performing the finite difference method analysis in the analysis calculation unit 40, and can be set with an arbitrary dimension suitable for the analysis.

Analysis condition data creation means The analysis condition data creation means 38 is a means for setting analysis conditions (initial conditions, boundary conditions, etc.) to be analyzed by the analysis operation unit 40, and according to a request from the analysis condition input request means 24. The analysis conditions input on the display 4 a via the input means 3 are converted into recognizable data, recognized, and stored in the analysis model database 17.
The input analysis conditions are constants used in the airflow analysis unit 41, boundary conditions (wind velocity distribution on the windward side surface in the simulation space, wind direction, vertical distribution of turbulence, ground surface in the simulation space, and side surfaces other than the windward and the sky. Wind speed, turbulence conditions, etc.) and initial conditions (wind speed distribution on windward side, wind direction, turbulence vertical data in simulation space), boundary conditions and initial conditions used in the concentration calculation means 47 Etc. (each data such as wind speed and direction at each grid point of the minute divided area calculated by the airflow analysis means, discharge intensity of each pollution source, etc.).

(4) Analysis calculation unit The analysis calculation unit 40 uses the data generated by the analysis data generation unit 30 to generate a three-dimensional advection-diffusion governing equation using the finite difference method (or finite element method, etc.). By calculating numerically, the velocity of the airflow, the direction of the airflow, and the pressure of the airflow (wind speed, wind direction, wind pressure) are obtained, and the concentration diffusion calculating unit 45 (diffusion index calculating means) Elements for determining the concentration distribution of air pollutants (hereinafter sometimes referred to as “concentration distribution”) by discretizing the concentration-diffusion governing equations and numerically solving them using the finite difference method (or finite element method, etc.) It is.

  The apparatus 1 can set a calculation target period, and not only performs concentration calculation at a predetermined time point, but also continuously over a desired period (one year) within a predetermined time width. Thus, the concentration can be calculated and the average concentration can be obtained by the concentration polymerization means 48 described later.

Airflow analysis unit The airflow analysis unit 41 includes a wind direction correction unit 42 and an airflow calculation unit 43.

[Wind direction correction means]
Normally, 16 direction data (22.5 degree intervals such as north, north-northeast, northeast, etc.) observed at a meteorological observatory are used as the wind direction data stored in the weather information database 15a. However, in order to analyze local air pollutant advection, it is necessary to obtain a more detailed wind direction. This wind direction correction means 42 considers the wind direction data of 16 directions (hereinafter referred to as “16 direction wind direction data”) in consideration of the appearance probability of the wind direction (in this embodiment, 32 directions (11.25 degrees)). It is a means for converting into detailed wind direction data (32 azimuth wind direction data) at intervals)).
In the present embodiment, the wind direction data (FIG. 4 (a)) given in one direction of 16 azimuth wind directions appears with the same frequency of 1/3 of the adjacent 32 azimuth wind directions on both sides centering on the 16 azimuth wind directions. Thus, the wind direction data can be converted into three directions represented by 32 azimuth wind directions (FIG. 4B).
In addition, when correcting the wind direction data of 16 azimuths to detailed wind direction data, if the azimuth is a positive multiple of 16, the division interval can be arbitrarily determined.

[Airflow calculation means]
The airflow calculating means 43 discretizes the three-dimensional advection diffusion governing equation by a finite difference method and numerically solves it in the simulation space, thereby calculating the airflow velocity and the airflow direction (wind velocity, wind direction) (hereinafter referred to as “airflow state”). It is a means to ask for.

That is, the airflow calculating means 43 is a κ-ε turbulence model (momentum transport equation (4-1), turbulent energy) based on the Navier-Stokes equation with the velocity and direction of the airflow and each motion element of pressure as variables. Each of the subdivided regions with respect to the advection-diffusion governing equation composed of the transport equation (4-2), the turbulent dissipation rate transport equation (4-3)) and the continuous equation (4-4). When the wind speed, wind direction, and wind pressure between the lattice points are in a predetermined equilibrium state, the calculation is repeated using a numerical analysis by a finite difference method every minute time between adjacent lattice points. Can be determined as predicted wind speed data and predicted wind direction data of each grid point.
The predicted wind speed data and predicted wind direction data (hereinafter sometimes referred to as “predicted airflow data”) at each calculated grid point are associated with the wind direction data given to the simulation space under the initial conditions. It is stored in the storage means 18a.

  Further, in the present embodiment, when the wind direction correction unit 42 corrects the wind direction data given in one direction of the 16 azimuth wind directions into the three detailed wind directions represented by the 32 azimuth wind directions, the detailed wind directions in the three directions. Is used to calculate the predicted airflow data, and the corrected predicted airflow data (simple average in the present embodiment) weighted by the appearance probability of the wind direction (equal appearance probability in the present embodiment) is obtained, and the airflow data storage means 18a Can be stored.

  In actual analysis, predicted airflow data is calculated in advance for each of the 32 azimuth directions (multiple of 16 azimuths, the same applies hereinafter) and stored in the airflow data storage unit 18a, and the concentration diffusion calculation unit 45 is used. Thus, when obtaining the concentration distribution, predicted airflow data corresponding to the wind direction of the simulation space given by the initial condition is extracted from the airflow data storage means 18a and given as the boundary condition.

[Continuous expression]
∂U _i / ∂X _i = 0 (4-4)
C ₁ , C ₂ , C ₃ , C _t : Constant U _i : Components of the instantaneous wind speed vector of the air flow P: Air pressure (wind pressure)
ρ: Air density ν: Kinematic viscosity coefficient ν _t : Eddy viscosity coefficient k: Turbulent energy ε: Turbulent dissipation rate

Concentration diffusion calculation unit The concentration diffusion calculation unit 45 is an element for obtaining a concentration distribution by discretizing and numerically solving a three-dimensional concentration diffusion governing equation by the finite difference method. This is obtained as the concentration at the lattice points of the finely divided regions in consideration of the concentration diffusion of the air pollutant discharged from the discharge source under the airflow state and the predetermined initial concentration condition.
The concentration diffusion calculation unit 45 includes parameter setting means 46, concentration calculation means 47, and concentration polymerization means 48.

[Parameter setting means]
The parameter setting means 46 is a means for setting various conditions and parameters for performing concentration calculation based on the analysis conditions in the analysis target region R, and includes an atmospheric stability setting means 46a and a diffusion coefficient setting means. 46b.

  The atmospheric stability setting means 46a generates the atmospheric stability data corresponding to the wind speed data in the analysis target area R, the daytime and nighttime, the amount of solar radiation or cloudiness in the daytime, and the cloudiness in the nighttime. This is a means for making a determination based on the Paskir atmospheric stability class classification table stored in the degree database 15b.

The diffusion coefficient setting means 46b extracts from the diffusion coefficient database 15c diffusion coefficient data corresponding to the atmospheric stability set by the atmospheric stability setting means 46a and the wind speed in the analysis target area R set in the analysis conditions. It is a means for determining.
The atmospheric stability data determined by the atmospheric stability setting means 46a and the diffusion coefficient data determined by the diffusion coefficient setting means 46b are stored in the analysis model database 17.

[Density calculation means]
The concentration calculation means 47 is a means for obtaining a concentration distribution by discretizing a three-dimensional concentration diffusion governing equation by a finite difference method and numerically solving it for the target air pollutant in the simulation space.
That is, the concentration calculation means 47 is a concentration constituted by the transport equation (4-5) of the air pollutant (diffusion material) when the airflow state at each lattice point in the calculated minute divided region is given. The diffusion governing equation is repeatedly calculated using numerical analysis by the finite difference method for each minute time between adjacent lattice points in each minute divided region, and the concentration of air pollutants between the lattice points becomes in an equilibrium state. In such a case, the value can be determined as predicted air pollutant concentration data of each lattice point.
Then, the predicted air pollutant concentration data at each calculated grid point is stored in the analysis result storage unit 18b.

  The concentration calculation by the concentration calculation means 47 is performed based on the initial condition that a predetermined amount of air pollutant is generated from each emission source based on the operation rate data of each emission source created by the emission source data creation means 35. Therefore, it is possible to make a prediction that matches reality.

[Concentration polymerization means]
The concentration polymerization means 48 extracts the predicted air pollutant concentration data of each lattice point, which is performed under a plurality of conditions (for example, differences in wind direction, time, etc.) from the storage means 18b of the analysis results and the like. The predicted air pollutant concentration data at the grid points are added together (hereinafter, the added predicted air pollutant concentration data is referred to as “predicted polymerization concentration data”), and if necessary, the average value is calculated by an average value calculation means (not shown). This is a means for calculating a value (hereinafter referred to as “predicted average density data”).
By using this means, it is possible to calculate hourly, daily, or yearly predicted average concentration data by adding the predicted air pollutant concentration data at each time point over a predetermined period and calculating an average value.

  Further, the concentration polymerization means 48 is created from the actual measured value data (or the actual measured value data, etc.) of the concentration of a predetermined air pollutant in the constant monitoring measurement station, which is stored in the actual air pollution concentration measured value database 16. The average concentration data at each point in time (hereinafter referred to as “background concentration data”) can be added to the predicted air pollutant concentration data.

  Note that the predicted polymerization concentration data and predicted average concentration data at each lattice point calculated are stored in the analysis result storage unit 18b.

(5) Analysis Result Display Unit The analysis result display unit 50 is a three-dimensional analysis result area R indicating the analysis results such as wind direction data, wind speed data, wind pressure data, and air pollutant concentration data calculated by the analysis calculation unit 40. Various data can be displayed on the output original map in association with the positional relationship, and various data can be displayed from a desired cut surface direction of the output original map. In addition, a contour diagram in which regions of the same concentration distribution are displayed with respect to air pollutants and streamlines of air pollutants can be displayed in vectors.
The analysis result display unit 50 can display various data stored in the storage unit 10 on the output unit 4 in a desired format.

(6) Others The apparatus main body 2 includes a control unit 60 for controlling each means (including the input means 3 and the output means 4) when the apparatus 1 is operated.

[Prediction method of air pollutant concentration distribution]
A concentration distribution prediction method using the apparatus 1 will be described with reference to FIG.
In the present apparatus 1, various concentration distributions can be analyzed at a desired point in time. Hereinafter, a case where the NOx concentration distribution is predicted and analyzed reproducibly at one point will be described.

  (1) When the apparatus 1 is operated, two-dimensional map data of a predetermined area is displayed on the display 4a (FIG. 2), and the analysis target area input requesting means 21 sends the analysis target area R to the operator. An input is requested (S1). When the operator performs an operation of designating a desired position by the input means 3 on the display 4a, the analysis target area R is determined by the analysis target area extraction means 32 and the area is displayed (S2).

  Then, the three-dimensional data conversion means 34 integrates the name of the structure in the analysis target area R and the height data of the structure stored in the structure database 13, thereby adding the height of the structure to the two-dimensional map data. The data is combined and stored as analysis target area data in the analysis model database 17 (S3), and a three-dimensional analysis space is displayed on the display 4a (FIG. 3).

  If necessary, the structure condition setting means 33 can correct the height data of the structure. Further, when adding a new virtual structure on the analysis target area R, by inputting its position, planar shape and height on the display 4a in accordance with an instruction from the structure condition input requesting means 22, These data can be combined with the analysis target area data group via the structure condition setting means 33.

  (2) The emission source data creation means 35 extracts the emission sources (in this embodiment, elevated roads and overpasses in FIGS. 2 and 3) that are present in the analysis target area R and stores them in the emission source database 14. The emission source characteristic value data group is extracted, the effective chimney height setting means 35a calculates the effective chimney height data of the emission source, and each of these data is stored in the analysis model database 17 as the analysis target area data group. (S4).

  When a new virtual emission source is added to the analysis target area R, by inputting the position and emission source characteristic area data on the display 4a in accordance with an instruction from the emission source condition input request unit 23, These data are combined with the analysis target area data group via the emission source data creation means 35.

  Then, the divided area data creating means 37 divides the simulation space other than the structure in the analysis target area R into a predetermined number of minute divided areas, and sets lattice points (S5).

(3) The analysis condition input requesting means 24 is used for the prediction target pollutant (NO _x ), the prediction time (00/00/00/00), the setting conditions for the minute divided area, the air flow analysis and the concentration analysis. Request input of initial conditions, boundary conditions, etc. When the operator performs input using the input means 3 such as the mouse 3b in accordance with the input screen on the display 4a, analysis condition data is created by the analysis condition data creating means 38 and stored in the analysis model database 17 ( S6).

(4) The meteorological data creation means 36 extracts the analysis target area meteorological data at the prediction time stored in the meteorological information database 15a (S7).
Further, in the analysis target area R where the grid points are set, the airflow analysis unit 41 calculates predicted airflow data in advance for every wind direction in 32 directions and stores it in the airflow data storage unit 18a (S8). .

  (5) The wind direction data (FIG. 4A) corresponding to the 16 azimuth wind directions given as the initial condition in the analysis target area R is 1/3 in each of the 32 azimuth wind directions on both sides centering on the 16 azimuth wind directions. Are corrected by the wind direction correcting means 42 (S9). Then, predicted airflow data corresponding to the decomposed three-direction wind direction data is extracted from the airflow data storage unit 18a, and weighted with the appearance rate of each wind direction and superimposed, thereby calculating corrected predicted airflow data. And stored in the airflow data storage means 18a (S10).

  (6) The operation rate data corresponding to the input emission source is extracted from the emission source database 14 and stored in the analysis model database 17 (S11).

(7) By the atmospheric stability setting means 46a, the wind speed data in the analysis target area R extracted by the meteorological data creation means 36, the daytime and nighttime, the amount of solar radiation or cloudiness during the daytime, the case of the nighttime Atmospheric stability data corresponding to the cloud amount is extracted based on the Pasquil atmospheric stability class classification table stored in the atmospheric stability database 15 b and stored in the analysis model database 17.
Then, the diffusion coefficient setting unit 46b extracts the atmospheric stability data set by the atmospheric stability setting unit 46a and the diffusion coefficient corresponding to the wind speed in the analysis target area R set by the analysis conditions from the diffusion coefficient database 15c. And stored in the analysis model database 17 (S12).

  (8) The concentration diffusion calculation unit 45 calculates predicted air pollutant concentration data. In the present embodiment, the predicted value is calculated by predicting the predicted air pollutant concentration data when 16-direction wind direction data is given as the initial condition, and calculating the average value thereof. It is said.

  Specifically, the corrected predicted airflow data corresponding to the wind direction data in the simulation space given in the initial condition is extracted from the airflow data storage unit 18a by the storage data management unit 19, and the concentration calculation unit 47 under the airflow condition. The predicted air pollutant concentration data of each lattice point is calculated and stored in the analysis result storage means 18b (S13).

  Then, predetermined background concentration data in the simulation space corresponding to the predicted time point is extracted from the air pollution concentration actual measurement value database 16 by the storage data management means 19 and added to the predicted average concentration data by the concentration polymerization means 48, The final predicted concentration data is stored in the analysis calculation result storage means 18b (S14).

  (9) Thereafter, the final predicted concentration data calculated by the storage data management means 19 can be extracted, and the result can be output to the display 4a by the analysis result display unit 50 as shown in FIG. 6 (S15). ). Further, if necessary, a contour diagram in which the same concentration distribution is displayed for air pollutants and streamlines of air pollutants can be displayed in vectors.

In the above description, the air pollution concentration distribution at one time point is calculated. However, the analysis calculation unit 40 performs analysis for a continuous period (for example, 1 hour, 1 day) at a predetermined time interval (for example, every 10 minutes). , 30 days, 365 days), and the time-series change in air pollution concentration can be obtained.
And the average value (1 hour average value, daily average value, monthly average value, annual average value) of the predicted air pollutant concentration in each period can also be calculated by using the concentration polymerization means 48 (S16).
Further, based on the same idea, it is possible to calculate the air pollution concentration distribution for each weekday and holiday.

[effect]
According to the present invention, it is possible to predict a local concentration distribution quickly, simply and accurately by performing a minimum necessary operation.

  In addition, the structure condition input requesting means 22, the structure condition setting means 33, the emission source condition input requesting means 23, and the emission source data creating means 35 are used to set various data when a virtual structure part or virtual emission source is constructed. By executing the analysis in advance, it is possible to analyze the concentration distribution in advance, so that it is possible to take appropriate measures according to the concentration distribution situation at the planning and planning stage, and to predict with high accuracy. Depending on the result, it is possible to change the plan at each stage. Therefore, it is very useful in supporting the determination of various development plans in the analysis target area.

As mentioned above, although an example about a suitable embodiment was explained about the present invention, the present invention is not restricted to the embodiment concerned, and a design change is possible suitably in the range which does not deviate from the meaning of the present invention. In particular, in the present invention, each data to be input may be stored in the storage unit 10 in advance, and the analysis operation unit 40 may perform analysis.
In the above embodiment, the case where the atmospheric substance concentration distribution is analyzed has been described. However, when other diffusion indices (for example, the temperature distribution of the atmospheric substance) are obtained, the concentration of the concentration diffusion governing equation in the concentration diffusion calculation unit is determined. This can be dealt with by reading the variable as a calorific variable, converting the calorific variable into data, and analyzing it by the same method.

It is a block diagram which shows the atmospheric substance concentration distribution analyzer of this invention. It is a top view which shows map data. It is a perspective view which shows the analysis object area | region converted into three dimensions. It is explanatory drawing which shows the correction | amendment concept of the wind direction using a wind direction correction | amendment means, (a) shows 16 direction wind direction, (b) shows 32 direction wind direction after correction | amendment. It is a flowchart which shows the atmospheric substance concentration prediction method. It is the figure which displayed the streamline of the air pollutant which is an example of an analysis result as a vector. It is a block diagram which shows the conventional simulation system.

Explanation of symbols

1 Atmospheric substance concentration distribution analyzer (this device)
DESCRIPTION OF SYMBOLS 2 Apparatus main body 10 Memory | storage part 11 Database 12 Map database 13 Structure database 14 Emission source database 15 Weather related database 15a Weather information database 15b Atmospheric stability database 15c Diffusion coefficient database 18a Airflow data storage means 20 Input request part 21 Analysis object area input Request means 22 Structure condition input request means 23 Emission source condition input request means 30 Analysis data creation section 31 Target area data creation means 32 Analysis target area extraction means 33 Structure condition setting means 34 Three-dimensional data conversion means 35 Emission source data creation Means 36 Meteorological data creation means 37 Divided area data creation means 40 Analysis calculation section 41 Airflow analysis section 42 Airflow direction correction means 43 Airflow calculation means 45 Concentration diffusion calculation section (diffusion index calculation means)
46 Parameter setting means 46a Atmospheric stability setting means 46b Diffusion coefficient setting means 47 Concentration calculation means 48 Concentration polymerization means

Claims (4)

A map database storing two-dimensional map data including planar shape data relating to structures and emission sources;
A structure database in which height data relating to the structure and the emission source is stored in association with the two-dimensional map data;
Emission source data including operating characteristic data, chimney height data, emitted atmospheric substance name data, emission intensity data, and emission substance temperature data relating to emission sources that emit atmospheric substances are stored in association with the two-dimensional map data. Emission source database,
For each predetermined area of the two-dimensional map data, a meteorological information database in which weather data of temperature data, wind direction data, wind speed data, solar radiation data, and cloud data is stored;
An analysis target area extracting means for extracting an analysis target area from the map database;
Three-dimensional map data is created by combining the structure height data of the structure in the structure database with the planar shape data of the structure and emission source in the two-dimensional map data of the analysis target area. 3D data conversion means for
Emission source data creating means for combining each emission source data related to each emission source to the three-dimensional map data of the analysis target area;
Weather data creating means for extracting each weather data corresponding to the analysis target area from the weather information database;
Divided region data creating means for dividing the analysis target region into a plurality of minute divided regions having a predetermined size;
For a plurality of predetermined wind direction data, given the wind speed data and turbulence conditions, numerically solve the three-dimensional advection-diffusion governing equation to obtain the flow of the air flow in each of the finely divided regions. Air flow analysis means,
Airflow data storage means for storing the wind direction data in association with the flow data of the airflow in each of the minute divided areas obtained by the airflow analysis means;
A diffusion index calculating means for calculating a diffusion index of atmospheric substances in each of the micro-partitioned areas, when given each weather data and each emission source data under a predetermined condition,
The diffusion index calculating means includes
By solving numerically the diffusion governing equation of the diffusion index under the flow condition of the air flow extracted from the air flow data storage means corresponding to the predetermined wind direction data, An atmospheric substance index distribution analyzer characterized by obtaining a diffusion index of a substance. A map database in which 3D map data including plane shape data and height data relating to structures and emission sources is stored;
Emission source data including operating characteristic data, chimney height data, emitted atmospheric substance name data, emission intensity data, and emission substance temperature data relating to emission sources that emit atmospheric substances are stored in association with the two-dimensional map data. Emission source database,
For each predetermined area of the three-dimensional map data, a weather information database in which temperature data, wind direction data, wind speed data, solar radiation data and cloud data are stored,
An analysis target area extracting means for extracting an analysis target area from the map database;
Emission source data creating means for combining each emission source data with respect to each emission source to the three-dimensional map data of the analysis target area,
Weather data creating means for extracting each weather data corresponding to the analysis target area from the weather information database;
Divided region data creating means for dividing the analysis target region into a plurality of minute divided regions having a predetermined size;
When the wind speed data and turbulence conditions are given for a plurality of predetermined wind direction data, numerically solving a three-dimensional advection-diffusion governing equation, An air flow analysis means for obtaining a flow;
Airflow data storage means for storing the wind direction data in association with the flow data of the airflow in each of the minute divided areas obtained by the airflow analysis means;
A diffusion index calculating means for calculating a diffusion index of atmospheric substances in each of the micro-partitioned areas, when given each weather data and each emission source data under a predetermined condition,
The diffusion index calculating means includes
By solving numerically the diffusion governing equation of the diffusion index under the flow condition of the air flow extracted from the air flow data storage means corresponding to the predetermined wind direction data, An atmospheric substance index distribution analyzer characterized by obtaining a diffusion index of a substance. The air flow analysis means includes
About the advection-diffusion governing equation composed of the Naviestokes equation and the continuous equation with each motion element in the velocity and direction of the airflow as variables, the difference method is used for each minute divided region and every minute time. Perform repeated calculations using numerical analysis,
The structure according to claim 1 or 2, wherein a value of an airflow velocity and an airflow direction in each of the divided regions can be determined in a predetermined equilibrium state. Atmospheric substance index distribution analyzer described. The diffusion index calculating means includes
For the diffusion governing equation of the diffusion index of the target atmospheric substance, iterative calculation is performed using a numerical analysis by the difference method for each minute divided region and every minute time,
The structure according to any one of claims 1 to 3, wherein a diffusion index value of the atmospheric substance in each of the divided regions can be determined when a predetermined equilibrium state is reached. The atmospheric substance index distribution analyzer according to item 1.

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