JP6136729B2 - Method, apparatus, program and storage medium for estimating amount of dustfall - Google Patents

Description

  The present invention relates to a method, apparatus, program, and storage medium for estimating the amount of dust fall based on the calculation results of the advection / diffusion behavior of the dust in the atmosphere, and in particular, the amount of dust fall that can take into account the effect of the cleaning effect due to rainfall. The present invention relates to an estimation method, apparatus, and computer program.

It is extremely important to evaluate the impact of dustfall on local residents by calculating the scattering behavior of dust particles emitted from dust sources such as factory chimneys by wind and estimating the amount of dustfall. In the present application, the term “dust” is used in the same meaning as “dust”.
Conventionally, assuming that the amount of dust fall depends on the one-dimensional distance between the dust source and the drop point, the distance between the dust source and the drop point, the dust generation rate, the dust particle size distribution and the dust density, and the effective A method has been proposed in which the dust distribution height, wind direction, and wind speed frequency distribution are input and the calculation result of equation (1) is multiplied by the wind direction / wind speed frequency distribution to estimate the amount of dustfall. 1). Q is the dust generation intensity, He is the effective dust generation height, w is the terminal sedimentation velocity determined by the particle diameter, x is the leeward distance between the dust generation source and the descent point, u is the wind speed, Γ is the gamma function, C (x ) Represents the amount of dustfall.

On the other hand, when the scattering material can be regarded as the same as the density of the carrier gas, a three-dimensional Gaussian amp room model has been proposed, and the scattering at an arbitrary spatial position (x, y, z) is given by equation (2). The substance concentration can be calculated (see, for example, Non-Patent Document 2). x is an arbitrary point on the coordinate axis (x axis) in the leeward direction from the dust generation source, y is an arbitrary point on the coordinate axis (y axis) perpendicular to the x axis on the horizontal plane, and z is the x axis and y. An arbitrary point on the coordinate axis (z axis) in the vertical direction with respect to the horizontal plane formed by the axes, c (x, y, z) is the dust concentration at the arbitrary coordinate point (x, y, z), and u is the wind speed , Q is the dust generation intensity, σ _y , σ _z are the spread of visible smoke in the y-axis and z-axis directions, He is the effective dust generation height, and y ₀ is the coordinate point on the y-axis where the dust generation source exists. Represents. In addition, the coordinate point on the x-axis where the dust generation source exists is set to zero.

  However, in the estimation method disclosed in Non-Patent Document 2, it is necessary to assume that the scattered substance has the same density as the carrier gas. In the case of dust particles, this assumption is not satisfied. The behavior cannot be estimated accurately.

Further, the present inventor has proposed a method for calculating the advection / diffusion behavior of soot dust when the sedimentation due to the dust gravity and the deposition effect on the ground surface cannot be ignored by the formula (3) (see Patent Document 1). . x is an arbitrary point on the coordinate axis (x axis) in the leeward direction from the dust generation source, y is an arbitrary point on the coordinate axis (y axis) perpendicular to the x axis on the horizontal plane, and z is the x axis and y. An arbitrary point on the coordinate axis (z axis) in the vertical direction with respect to the horizontal plane formed by the axes, c (x, y, z) is the dust concentration at the arbitrary coordinate point (x, y, z), and u is the wind speed , Q is the dust generation intensity, K _y and K _z are the turbulent diffusion coefficients in the y-axis and z-axis directions, w is the final sedimentation velocity of the particles, He is the effective dust generation height, and y0 is the presence of the dust generation source. A coordinate point on the y-axis, β, represents the deposition rate of particles on the ground surface. In addition, the coordinate point on the x-axis where the dust generation source exists is set to zero.

  Further, techniques for monitoring and predicting the diffusion of chemical substances and fine particles have been disclosed (see Patent Document 2 and Patent Document 3). However, since these techniques perform atmospheric diffusion simulation calculation, that is, complicated numerical calculation of partial differential equations, calculation is performed in comparison with prediction based on an analytically disclosed formula disclosed in Patent Document 1. Time is required and significant computational costs are required.

  Furthermore, a technique for describing the effect of precipitation on the amount of dustfall is disclosed in Non-Patent Document 5, but a technique for predicting the amount of dustfall at the time of rainfall is not disclosed.

International Publication No. 2010/001925 Japanese Patent Laid-Open No. 2001-42052 JP 2008-89418 A

C.H.Bosanquet et al., Proc Inst Mech Engrs, Vol.162, p355 (1950) Wind Meteorology Kiyohide Takeuchi The University of Tokyo Press Chemical Engineering Handbook 4th revised edition Briggs G. A. Plume rise, U.S. AEC (1969) Sano, Ota, Ichikawa, Tsuboi, Aichi Institute of Technology Research Report No. 20 B 101-107 1985

However, in the estimation method disclosed in Non-Patent Document 1, it is assumed that the amount of dustfall depends on the one-dimensional distance in the leeward direction between the dust generation source and the descending point. Therefore, a plurality of dust sources with the same distance between the dust source and the descent point, the same dust generation strength, dust particle size distribution and soot density, and effective dust height are arranged for one descent point. In this case, it is presumed that the contributions of the dust source 1 and the dust source 2 are the same, but in reality, the dust particles 2 turbulently diffuse in the three-dimensional direction, so the contribution of the dust source 2 that is close to the descending point There is a problem that the fact that it becomes larger than the dust generation source 1 cannot be described.
In addition, since the equation (1) is an experimental equation announced by Bosanquet in 1950, the accuracy of the mathematical formula depends on the environment of the experiment when the experimental equation is derived, and there is a problem that it lacks generality.

In addition, the estimation method disclosed in Patent Document 1 cannot explain the observation fact that a difference occurs in the amount of dust fall measurement at a specific measurement location under the same wind direction and wind speed conditions in rainy weather and clear weather.
In other words, there was no disclosure of a technique for quickly predicting dust precipitation and incorporating the effect of rainfall without reducing accuracy. For example, as disclosed in Non-Patent Document 5, a technique for expressing the influence of precipitation on the amount of falling dust by a quantity called a cleaning coefficient is disclosed. However, for example, even if an attempt is made to simply introduce a washing coefficient into the equation disclosed in Patent Document 1, the process of linking dust washed by rainfall to the amount of dust falling is not obvious, and the solution is divergent in calculations based on simple mathematical calculations. However, even if a solution is obtained, it takes a very long time and is not suitable for practical use.

  The present invention has been made in view of the above points, and the scattering behavior due to advection / diffusion in the three-dimensional direction by the wind from the dust generation source to the descending point is considered in consideration of the effect of the cleaning effect due to rain, The purpose is to be able to estimate the amount of falling dust theoretically based on the dust mass balance.

The method for estimating the amount of dustfall of the present invention divides the rainfall range into k divided ranges based on the time-series measured values of the rainfall amount, wind direction, and wind speed for a predetermined period, and for each of the k divided ranges. In addition, the range of the wind direction and the wind speed is divided into m and n divided ranges, a wind speed representative value is set for each divided range of the wind speed, and the predetermined time series measurement values included in each of the divided ranges are set. A wind direction / wind speed information input step in which l wind direction / wind speed frequency distributions of m × n matrix are respectively created by obtaining frequency in a period, and the wind direction representative value and the wind direction / wind speed frequency distribution are used as wind direction / wind speed information; A dust generation information input step for inputting dust generation information which is information of a dust generation source, a cleaning coefficient calculation step for calculating a cleaning coefficient used to calculate the degree to which atmospheric dust is washed away by rainfall, and the wind direction・ Wind speed information, dust generation information, The dust concentration calculation step of calculating the dust concentration at an arbitrary coordinate point using the cleaning coefficient and the reflectance of the dust on the ground surface, and the amount of dust falling at an arbitrary descent point based on the dust concentration And a dust concentration calculation step for calculating, and in the dust concentration calculation step, the amount of dust falling in the atmosphere as the dust in the atmosphere is washed away by the rain is calculated and added to the calculated value of the amount of dust falling It is characterized by that.
The apparatus for estimating the amount of dustfall according to the present invention divides a range of rainfall into k divided ranges based on time-series measured values in a predetermined period of rainfall, wind direction, and wind speed, and each k divided ranges are divided. In addition, the range of the wind direction and the wind speed is divided into m and n divided ranges, a wind speed representative value is set for each divided range of the wind speed, and the predetermined time series measurement values included in each of the divided ranges are set. Wind direction / wind speed information input means for generating l each of m × n matrix wind direction / wind speed frequency distributions by obtaining the frequency in a period, and using the wind speed representative value and the wind direction / wind speed frequency distribution as wind direction / wind speed information; A dust generation information input means for inputting dust generation information that is information of a dust generation source, a cleaning coefficient calculation means for calculating a cleaning coefficient used to calculate the degree to which atmospheric dust is washed away by rainfall, and the wind direction・ Wind speed information, dust generation information, The dust concentration calculation means for calculating the dust concentration at an arbitrary coordinate point using the cleaning coefficient and the reflectance of the dust on the ground surface, and the amount of dust fall at an arbitrary descent point based on the dust concentration. And a dust concentration calculation means for calculating, and in the dust concentration calculation means, the amount of dust falling in the atmosphere is washed away by the rain and the amount of dust falling to the ground surface is calculated and added to the calculated value of the amount of dust fall It is characterized by.
The program of the present invention divides the range of rainfall into k divided ranges based on the time-series measured values of rainfall, wind direction and wind speed for a predetermined period, and wind direction and wind speed for each of the k divided ranges. Are divided into m and n divided ranges, a wind speed representative value is set for each of the divided ranges of the wind speed, and the frequency of the time-series measured values included in each of the divided ranges is determined for the predetermined period. The wind direction / wind speed frequency distribution of the m × n matrix is created by calculating each of the wind direction / wind speed frequency distributions, and the wind direction / wind speed information input step using the wind direction representative value and the wind direction / wind speed frequency distribution as the wind direction / wind speed information, A dust generation information input step for inputting dust generation information that is information, a cleaning coefficient calculation step for calculating a cleaning coefficient used to calculate the degree to which dust in the atmosphere is washed away by rain, and the wind direction and speed information, The dust generation information and the cleaning unit And a dust concentration calculation step for calculating the dust concentration at an arbitrary coordinate point using the reflectance of the dust on the ground surface, and a descent for calculating the amount of dust falling at an arbitrary descent point based on the dust concentration The computer executes the dust amount calculation step, and in the dust concentration calculation step, the amount of dust falling in the air is washed away by the rain and falls to the ground surface, and is added to the calculated value of the amount of dust falling. It is characterized by.
A computer-readable storage medium according to the present invention records the program according to the present invention.

  According to the present invention, it is possible to estimate the dust behavior faithfully and accurately according to the principle of the actual phenomenon as compared with the conventional estimation formula. In other words, the scattering behavior due to advection / diffusion in the three-dimensional direction by the wind from the dust source to the descent point is theoretically determined based on the material balance of the dust, taking into consideration the effect of the cleaning effect due to the rain, and the amount of dust fall Can be estimated. This makes it possible to quantitatively evaluate how much dust generated from the dust generation source affects the city area as the amount of falling dust, and equipment for estimating the optimum scale of dust control equipment such as dust collectors. Become a design index.

It is a flowchart which shows the calculation process of the dust density | concentration and falling dust amount which concern on this embodiment. It is a flowchart which shows the calculation process of the dust density | concentration and falling dust amount which concern on this embodiment. It is a figure for demonstrating the dust scattering behavior from a dust generation source. It is a characteristic view for determining the spread of smoke by Pasquill-Gifford. It is a characteristic view which shows the relationship between raindrop density and raindrop diameter. It is a characteristic view which shows the relationship between raindrop fall speed and raindrop diameter. It is a characteristic view which shows the relationship between the washing | cleaning coefficient for every raindrop particle size calculated in the Example, and a raindrop diameter. It is a characteristic view which shows the relationship between the amount of dust falling calculated in the Example, and a cleaning coefficient. It is a figure which shows the hardware structural example of the computer system which functions as an estimation apparatus of the amount of falling dust. It is a figure which shows an example of the dust fall amount distribution calculated in the Example.

Preferred embodiments of the present invention will be described below with reference to the accompanying drawings.
In the present embodiment, as shown in FIG. 2, as an example of a dust generation source, scattering of dust particles from a factory chimney is considered. The dust particles are scattered with the x axis set in the leeward direction from the effective dust generation height He as the central axis. Assuming that the dust generation source exists at a position of x = 0, the vertical direction is taken as the z axis, and the axis of the horizontal plane intersecting the x axis is taken as the y axis. Let c (x, y, z) be the concentration of dust particles scattered by the wind from the dust generation source (hereinafter referred to as “dust concentration”). The dust concentration c (x, y, z) can be described by equation (4) when the mass balance is taken.

In equation (4), u represents wind speed, and as described above, the x axis is set at the center of the wind direction, and it is assumed that the wind blows only in the x axis direction.
The first term on the left side of the equation (4) indicates the amount of dust particles scattered by wind advection. The first term on the right side of equation (4) indicates the amount of dust particles scattered in the y-axis direction by turbulent diffusion, and the second term on the right side of equation (4) is in the z-axis direction due to turbulent diffusion. The amount of scattering is shown, and the third term on the right side of equation (4) indicates the amount of dust particles that settle by gravity. Here, it was assumed that the amount of dust particles scattered in the x-axis direction by turbulent diffusion is smaller than the amount of dust scattered in the x-axis direction due to wind advection and can be ignored. K _y and K _z represent turbulent diffusion coefficients in the y-axis direction and the z-axis direction, respectively. The fourth term on the right side of the equation (4) indicates an amount representing the cleaning effect due to rain, and indicates that dust in the atmosphere is cleaned by a primary expression of the dust concentration c multiplied by the cleaning coefficient p.

Expression (5) is an expression related to setting of boundary conditions of dust particles in the dust generation source. Q · δ (y−y ₀ , z−He) is generated from the position of x = 0, y = y ₀ , z = He, and dust particles with a dust generation strength (also referred to as dust generation speed) Q are generated. Represents that. δ is a delta function. Here, the delta function is a real superfunction that satisfies the condition of equation (2). Further, the dust generation strength Q can be obtained by means such as a low volume sampler.

  Expression (6) is an expression related to setting of boundary conditions of dust particles on the ground surface. β represents the reflectance of dust particles on the ground surface, and β = 0 means complete deposition, and β = 1 means complete reflection. The reflectance β is a coefficient depending on the size of the dust particles, and usually the dust particles that reach and deposit on the ground surface are mostly targeted, and there is no problem even if β = 0 is approximated. If you want to set the value of β accurately, measure the amount of dust falling from a dust source such as a chimney with known dust generation intensity, effective dust generation height, and dust particle size distribution using a means such as a deposit gauge. For each particle diameter, an appropriate value is set so that the calculated value of the falling dust satisfies the measured value of the falling dust.

  When the equation (4) is solved analytically under the boundary condition between the equations (5) and (6), the equation (7) is derived.

  The amount of dust fall C (x, y) at the descending point is calculated by the formula (3) of the dust concentration c (x, y, z = 0) at an arbitrary plane coordinate point (x, y) at the surface z = 0. The calculated value is multiplied by the terminal sedimentation velocity (also referred to as the terminal falling velocity) w of the dust particles.

When soot dust falls in the air, the speed of the dust becomes constant over time as a result of the balance between soot gravity and buoyancy. This is called terminal sedimentation velocity, and can be calculated by equation (9). ρ _s is the density of the dust particles, ρ _a is the density of the atmosphere, d _k is the diameter of the dust particles corresponding to the particle size frequency k, and μ is the viscosity coefficient of the atmosphere.

On the other hand, Cγ represents the amount of dust that has fallen due to adsorption of raindrops collected at any point (x, y, z = z ₁ (falling dust trapping height)) on the surface of the earth. it can.

  Substituting equation (7) into equation (10) and calculating the integral on the right side of equation (10) yields equation (11). erfc is an error function.

Hereinafter, an example of a procedure for calculating the amount of dust fall using the equations (7) and (8) will be described with reference to the flowcharts of FIGS. 1A and 1B.
First, the step of inputting rainfall information in step S1 will be described. Based on the measured value of rainfall for a certain period, no rain is classified as level 1, and as rainfall increases, it is classified into level 2, ..., level k, and wind direction data and wind speed data for each rainfall level. collect. Thus, it was found that the convergence of the solution is accelerated by adopting the method of inputting the wind speed data and the wind direction data for each rainfall level.
For example, data input is first divided into levels for wind speed, and when rainfall data and wind direction data are collected for each wind speed level, or first, the wind data is divided into levels for wind direction, and similarly, rainfall data and wind speed data are collected for each wind direction level. When collected, the solution may diverge or vibrate and a convergent solution may not be obtained. Even if a solution is obtained, it takes several to several tens of times longer than collecting wind direction data and wind speed data for each rainfall level, and cannot be used practically. This is because the cleaning coefficient is not simply given as a function of rainfall, and it is necessary to go through various procedures in order to determine the cleaning coefficient from rainfall.

Next, the step of inputting wind direction / wind speed information in step S2 will be described. The frequency distribution of wind direction and wind speed is calculated for each rain level classified in the rain information input step. Based on the measured values of the wind direction and the wind speed for a certain period, the wind direction is classified into m categories and the wind speed is classified into n categories so that the sum of each element b _ij of the matrix of n rows and m columns is 100%.
Usually, the classification of wind direction data can be calculated in any number of directions of one or more directions, and may be set in the number of available direction data. Preferably, it corresponds to AMeDAS data of the Japan Meteorological Agency, adopts 16 directions, and m = 16.
The wind speed data classification can be calculated by directly using the actual measurement value of the wind speed, but preferably, the minimum wind speed and the maximum wind speed in the set period are defined as light wind, light wind, normal wind, strong wind, light wind, light wind, What is necessary is just to divide | segment into 4 or 5 into classification | category, such as a normal wind, a strong wind, and a super strong wind, and set it as n = 4 or n = 5. The calculation can be performed even when n is 6 or more, but in some cases, the calculation becomes complicated. A representative value is selected for each of the divided wind speed ranges. In selecting the representative value, a value with the highest occurrence frequency of the wind speed in the divided section may be adopted, or an average value of the wind speed in the divided section may be adopted.
In addition, it is preferable to install an anemometer and an anemometer in the position which is not influenced by obstructions, such as a building around. If the Japan Meteorological Agency is in the vicinity, the Meteorological Agency observation data such as AMeDAS may be used.
The period for measuring the wind direction and the wind speed is set to be the same as the period for calculating the amount of dustfall. And the sampling period of a wind speed and a wind direction is determined so that the statistic of a frequency distribution of a wind speed and a wind direction can be determined in the set period. For example, in the case of m = 16 and n = 4, when setting a period of one month, it is sufficient to collect wind speed and wind direction data of one hour period.

  As described above, based on the time-series measured values of rainfall, wind direction, and wind speed for a predetermined period, the range of rainfall is divided into k divided ranges, and the wind direction and wind speed are divided for each of the k divided ranges. The range is divided into m and n divided ranges, wind speed representative values are set for each of the wind speed divided ranges, and the frequency of the time-series measurement values included in each of the divided ranges is obtained in the predetermined period. As a result, one m × n matrix wind direction / wind speed frequency distribution is created, and the wind speed representative value and the wind direction / wind speed frequency distribution are used as wind direction / wind speed information.

Next, the step of inputting dust generation information in step S3 will be described. Information of dust generation source, specifically, dust source x-y coordinates (x = 0, y = y ₀ ), chimney height H, dust generation strength Q, dust particle diameter d, dust particle density ρ, exhaust gas Input air volume W and exhaust gas wind speed V.

  Next, the step of inputting descent point information in step S4 will be described. Information on the descent point, specifically, the coordinates (x, y) of the descent point for which the amount of dustfall is to be estimated is input.

  The input direction of wind direction / wind speed information, the input step of dust generation information, and the input step of descent point information described so far may be switched regardless of the input order.

Next, the dust concentration calculation step is entered. In the dust concentration calculation step, the dust concentration at the descending point is calculated using the information input in the above steps.
In step S5, the wind speed u _i at each frequency distribution b _ij of wind direction and wind speed is defined as the wind speed u used for the calculation. The wind speed u is used for calculating the effective dust generation height He according to the later-described equation (13) and calculating the dust concentration at the descending point according to the equation (7).

In step S6, the cleaning coefficient p is calculated. As a mathematical formula for calculating the cleaning coefficient, the formula (12) may be used (for example, see Non-Patent Document 5). Here, D is the raindrop diameter, N is the raindrop concentration, v is the raindrop fall speed, and ε is the collision efficiency.
p = π (D / 2) ² εvN (12)

When the dust is discharged at a higher temperature than the surrounding air, such as a factory chimney, the effective dust generation height He is the chimney height H, the chimney height exhaust gas volume W, and the exhaust gas wind speed V input in step S3 in step S7. Based on the above, the effective dust generation height He is calculated by, for example, equation (13). Q _{e _gas} is the exhaust gas flow rate, T ₁ is the atmospheric temperature, ΔT is the difference between the exhaust gas and T ₁ , g is the gravitational acceleration, dθ / dz is the atmospheric temperature gradient (usually calculated at 0.03), w is The final settling velocity of the particles, u represents the wind speed.

On the other hand, if soot dust is scattered by the wind, record the dust generation status with a camera or other means, identify and determine the maximum density of soot dust from the color density, and use the determined dust generation height Set as the dust generation height He.
The dust particle size is measured by putting a dust particle sample collected with a low volume sampler into a particle size distribution measuring device and measuring the particle size distribution. Based on the measured particle size distribution, the average particle size is determined using the definition such as the maximum frequency diameter. Further, the measurement range of the particle diameter may be divided, and the average particle diameter in each divided range may be the dust particle diameter. In this case, the weight ratio of the dust particles existing in the range of the divided particle diameter is obtained in advance, and the frequency distribution is calculated for each average particle diameter so that the total weight becomes 100%. The total amount of dust fall is obtained by multiplying the dust fall amount for each diameter by the frequency distribution calculated for each average particle diameter. As the particle size distribution measuring apparatus, there are a precipitation method, a light transmission method, and the like (for example, see Non-Patent Document 4).

  In step S8, the terminal settling velocity w is calculated by equation (9) based on the dust particle diameter d and the dust particle density ρ input in step S3.

In step S9, turbulent diffusion coefficients K _y and K _z are determined. The turbulent diffusion coefficients K _y and K _z are based on Eq. (14) based on the experimental values of smoke spread σ _y and σ _z observed by Pasquill-Gifford in the US grassland as shown in FIG. The converted value is set (for example, refer nonpatent literature 4).

Here, symbols A, B, C, D, E, and F in FIG. 3 represent the atmospheric stability, A is very unstable, B is unstable, C is slightly unstable, D is neutral, and E is stable. , F corresponds to a very stable state. Atmospheric stability is turbulent diffusion coefficient K _y, related to the magnitude of K _z, as the atmospheric stability of 3 is stabilized, the turbulent diffusion coefficient K _y, K _z decreases.

  In step S10, the reflectance β is input.

  In step S11, the dust concentration c (x, y, z = 0) at the coordinate position (x, y) of the descending point at z = 0 is calculated by the equation (7).

  In step S12, based on the dust concentration c (x, y, z = 0) calculated in step S11 and the final settling velocity w of the dust particles calculated in step S8, the amount of dust fall is calculated by equation (8). , ΔC (x, y).

In step S13, the dustfall amount C (x, y) in adds the calculated value of the dustfall amount corresponding to the frequency distribution b _ij of each was calculated wind direction and velocity △ C (x, y) in the step 12, The amount of dustfall is updated using equation (15).
C (x, y) = C (x, y) + b _ij · ΔC (x, y) (15)
Here, C (x, y) on the right side is the amount of dust fall before adding the second term on the right side, and C (x, y) on the left side is the amount of dust fall after adding the second term on the right side. . That is, the dust concentration c (x, y) at a certain wind direction, wind speed, and rainfall level is obtained, and the frequency bij in the wind direction, wind speed, and rain level is weighted, and the sum is obtained for various wind directions, wind speeds, and rain levels. To determine the amount of dust fall C (x, y)).

  In step S14, if the conditions of l <k, i <n, and j <m are satisfied, the loop of step S15 is executed, and the calculation of steps S5 to S13 is repeated, so that the dust fall amount C (x, y ). The loop of step S15 first updates the value of l one by one, and repeats the calculations from step S5 to step S13 until l = k is reached. If l = k, the value of j is similarly updated one by one, and the calculations from step S5 to step S13 are repeated until j = m is reached. If j = m, the value of i is similarly updated one by one, and the calculations in steps S5 to S13 are repeated until i = n is reached. If it is determined in step S15 that l = k, i = n, and j = m, this calculation ends.

The method described above describes the behavior of soot when it is scattered by the wind in accordance with the principles of physical phenomena. Soot contained in exhaust gas from automobiles and automobile exhaust It can be applied to scattering phenomena such as pollen, yellow sand, and desert sand causing hay fever.
In particular, the amount of dust fall in the atmosphere due to rainfall can be incorporated into the calculation of the advection / diffusion behavior of dust when the precipitation due to gravity and the deposition effect on the ground surface cannot be ignored, so that the amount of dust fall can be improved.

The calculation procedure and results of the dust concentration by the method to which the present invention is applied (hereinafter referred to as the present method) are shown below.
Based on the data of the Japan Meteorological Agency, the rain level is divided into two stages, ie, “Rain level 1: No rain” and “Rain level 2: Rain present”. It was created.

The (x, y) coordinates of the dust generation source were set to (700, 700). The unit is m. Further, as information on the dust generation source for calculating the effective dust generation height He by the equation (13), the exhaust gas flow rate Q _e _ _gas is 14300 Nm ³ / min, the atmospheric temperature T ₁ is 20 ° C., the exhaust gas and T ₁ The difference ΔT was 227 ° C., g was gravitational acceleration, and the atmospheric temperature gradient (dθ / dz) was set to 0.03. The dust generation strength Q was set to 40 kg / Hr.
As information for calculating the terminal sedimentation velocity w by the equation (9), the dust particle diameter d _k was set to 110 μm, and the density ρ _{s of the} dust particles was set to 3770 kg / m ³ .
The coordinates (x, y) were set to (0, 0) as the descent point information.

Next, a method for calculating the cleaning coefficient p based on the equation (12) will be described.
The raindrop density N is calculated from the characteristic diagram showing the relationship between the raindrop density distribution n and the raindrop diameter D shown in FIG. FIG. 4 is a graph when the rainfall amount is 4.7 mm / Hr.

  The raindrop descending speed v is determined according to the raindrop diameter D from the characteristic diagram showing the relationship between the raindrop descending speed and the raindrop diameter shown in FIG. The collision efficiency ε was set to 0.9. FIG. 5 is a graph when the rainfall amount is 4.7 mm / Hr.

  Based on the above procedure, the impact coefficient p ^ (D) is calculated for each raindrop diameter (the notation of p ^ assumes that ^ is attached on p) as shown in FIG. . The collision coefficient p is calculated by the equation (17) obtained by integrating the collision coefficient p ^ (D) for each raindrop diameter over the entire raindrop diameter, and its value is 0.0016 [1 / sec].

FIG. 7 shows the amount of dustfall that is calculated based on the above procedure with the reflectance β = 0. The amount of dust fall when the value of the cleaning coefficient p is 0.0016 is 0.25 ton / km ² · month, and the influence of the amount of dust falling when the rainfall increases or decreases is set to 0 (rainfall None), 0.0008 (light rain), and 0.0024 (heavy rain), and calculation was performed based on the above procedure. FIG. 7 shows that the amount of dustfall increases as the cleaning coefficient increases, in other words, increases as the level of rainfall increases, and gradually approaches a saturated state at some point. Match.

FIG. 8 shows a hardware configuration example of a computer system that functions as a dust fall amount estimation device to which the present invention is applied. The dust fall amount estimation device 100 includes a CPU 20, an input device 21, a display device 22, and a recording device 23, and each unit is connected via a bus.
The input device 21 receives rainfall amount information, wind direction / velocity information, dust generation information, descent point information, reflectivity β, and cleaning coefficient p necessary for estimating the amount of dustfall. The CPU 20 determines the effective dust generation height He, the final settling velocity w of the dust particles, the turbulent diffusion coefficients K _y and K _z based on the information input to the input device 21, and the amount of dust fall at the descending point is determined. Calculated.
The display device 22 displays, for example, a contour map of the amount of dustfall as shown in FIG. 9 based on the calculated value of the dustfall amount calculated by the CPU 20 at a plurality of descending points.
In the recording device 23, all the information input to the input device 21 and the amount of dust falling at the descending point calculated by the CPU 20 are recorded. The recording device 23 includes a ROM, a RAM, an HD, and the like, and stores a computer program that controls the operation of the dust fall amount estimation device 100.
When the CPU 20 executes the computer program, the functions and processing of the dust fall amount estimation device 100 are realized. A database is stored in the recording device 23.
Note that the dust fall amount estimation apparatus to which the present invention is applied may be applied to a system constituted by a plurality of devices or an apparatus constituted by a single device.
The object of the present invention can also be achieved by supplying a computer program for realizing the above-described functions to a system or apparatus and executing the computer (CPU or MPU) of the system or apparatus. In this case, the computer program itself Constitutes the present invention.
As mentioned above, although this invention was demonstrated with various embodiment, this invention is not limited only to these embodiment, A change etc. are possible within the scope of the present invention.

100: Estimation device for the amount of dust fall, 20: CPU, 21: Input device, 22: Display device, 23: Recording device

Claims (8)

Based on the time-series measurement values of rainfall, wind direction, and wind speed over a predetermined period, the range of rainfall is divided into k divided ranges, and for each of the k divided ranges, the range of wind direction and wind speed is m, m × n by dividing into n division ranges, setting a wind speed representative value for each division range of the wind speed, and determining the frequency of the time-series measurement values included in each of the division ranges in the predetermined period. A wind direction / wind speed frequency distribution is prepared by creating l each of the wind direction / wind speed frequency distribution of the matrix and using the wind speed representative value and the wind direction / wind speed frequency distribution as the wind direction / wind speed information,
A dust generation information input process for inputting dust generation information which is information of a dust generation source;
A cleaning coefficient calculation step for determining a cleaning coefficient used to calculate the degree to which atmospheric dust is washed away by rainfall;
A dust concentration calculation step of calculating a dust concentration at an arbitrary coordinate point using the wind direction / wind speed information, the dust generation information, the cleaning coefficient, and the reflectance of the dust on the ground surface,
A dust falling amount calculation step for calculating a dust falling amount at an arbitrary descent point based on the dust concentration,
In the dust concentration calculation step, the amount of dust falling in the atmosphere is washed away by rain and the amount of dust falling to the ground surface is calculated and added to the calculated value of the amount of dust falling.   In the dust generation information input step, coordinates in a three-dimensional space of the dust generation source, dust generation intensity, dust particle diameter, dust particle density, and dust generation height are input. The method for estimating the amount of dustfall according to claim 1. In the dust concentration calculation step, the dust generation intensity Q, the wind speed u, the y axis, the turbulent diffusion coefficients K _y and K _z in the z axis direction, the final sedimentation velocity w of the particles, the cleaning coefficient p, and the effective dust generation height He are calculated. And the soot concentration c (x, y, z) at the point (x, y, z) on the three-dimensional coordinate is set with the x coordinate of the dust source being 0 and the y coordinate of the dust source being y _0. The method for estimating the amount of dust fallen according to claim 2, wherein calculation is performed according to equation (101).

The amount of dust fall calculation step is the effect of dust concentration c (x, y, z), dust fall amount C, dust in the atmosphere being washed away by rain and falling at the point (x, y, z) on the three-dimensional coordinates. The amount of dust fall is calculated by the equations (102) and (103) using the dust amount Cγ, the trapped height z ₁ of the dust fall amount, the cleaning coefficient p, and the final sedimentation velocity w of the particles. Item 4. The method for estimating the amount of dust fall according to Item 2 or 3.

The amount of dust fall calculation step is the effect of dust concentration c (x, y, z), dust fall amount C, dust in the atmosphere being washed away by rain and falling at the point (x, y, z) on the three-dimensional coordinates. Dust amount Cγ, trapped height of falling dust amount z ₁ , wind velocity u, y axis, z-axis turbulent diffusion coefficient K _y , K _z , particle terminal settling velocity w, cleaning coefficient p, error function erfc, effective 5. The method for estimating the amount of dustfall according to claim 4, wherein the amount of dustfall is calculated by the formulas (104) and (105) using the dust generation height He.

Based on the time-series measurement values of rainfall, wind direction, and wind speed over a predetermined period, the range of rainfall is divided into k divided ranges, and for each of the k divided ranges, the range of wind direction and wind speed is m, m × n by dividing into n division ranges, setting a wind speed representative value for each division range of the wind speed, and determining the frequency of the time-series measurement values included in each of the division ranges in the predetermined period. A wind direction / wind speed frequency distribution of the matrix, each of which creates l wind direction / wind speed frequency distribution, and uses the wind speed representative value and the wind direction / wind speed frequency distribution as wind direction / wind speed information;
A dust generation information input means for inputting dust generation information which is information of a dust generation source;
A cleaning coefficient calculation means for determining a cleaning coefficient used to calculate the degree to which atmospheric dust is washed away by rainfall;
A dust concentration calculating means for calculating a dust concentration at an arbitrary coordinate point using the wind direction / wind speed information, the dust generation information, the cleaning coefficient, and the reflectance of the dust on the ground surface;
A falling dust amount calculating means for calculating a falling dust amount at an arbitrary descent point based on the dust concentration,
The dust concentration calculation means is configured to calculate the amount of falling dust that falls to the ground surface after washing away dust in the atmosphere due to rain, and adds it to the calculated value of the amount of falling dust. Based on the time-series measurement values of rainfall, wind direction, and wind speed over a predetermined period, the range of rainfall is divided into k divided ranges, and for each of the k divided ranges, the range of wind direction and wind speed is m, m × n by dividing into n division ranges, setting a wind speed representative value for each division range of the wind speed, and determining the frequency of the time-series measurement values included in each of the division ranges in the predetermined period. A wind direction / wind speed frequency distribution is prepared by creating l each of the wind direction / wind speed frequency distribution of the matrix and using the wind speed representative value and the wind direction / wind speed frequency distribution as the wind direction / wind speed information,
A dust generation information input process for inputting dust generation information which is information of a dust generation source;
A cleaning coefficient calculation step for determining a cleaning coefficient used to calculate the degree to which atmospheric dust is washed away by rainfall;
A dust concentration calculation step of calculating a dust concentration at an arbitrary coordinate point using the wind direction / wind speed information, the dust generation information, the cleaning coefficient, and the reflectance of the dust on the ground surface,
Based on the dust concentration, let the computer execute a dust fall calculation step to calculate the dust fall amount at any descent point,
In the dust concentration calculation step, a dust amount in the atmosphere is washed away by rain and the amount of dust falling to the ground surface is calculated and added to the calculated value of the amount of dust falling.   A computer-readable storage medium having recorded thereon the program according to claim 7.

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