Pollutant diffusion modeling method and model based on meteorological data

Abstract

The invention belongs to the technical field of meteorological prediction, and particularly relates to a pollutant diffusion modeling method and model based on meteorological data. The specific process of the modeling method is as follows: step 1, meteorological data acquisition and preprocessing; step 2, based on the Gaussian diffusion model of the continuous point source, obtaining a three-dimensional Gaussian diffusion model changing along with time; and step 3, calculating meteorological factors based on the meteorological element information, and obtaining a pollutant Gaussian diffusion model considering meteorological data based on the meteorological factors. According to the method, both time change and meteorological elements can be considered, so that the established model can better reflect the real pollutant diffusion condition.

Description

Technical Field

[0001] The present invention belongs to the technical field of pollutant diffusion simulation, and in particular relates to a pollutant diffusion modeling method and model based on meteorological data. Background Art

[0002] The Gaussian diffusion model is a classic mathematical model widely used in simulating the diffusion of atmospheric pollutants. The model is based on the Gaussian probability distribution theory and is used to predict the concentration distribution of pollutants released from point sources in the atmosphere. Its basic assumptions include that atmospheric turbulence is isotropic, pollutants follow a normal distribution in the horizontal and vertical directions, and meteorological conditions (such as wind speed, wind direction, etc.) are stable and unchanged.

[0003] However, existing technologies are mainly applicable to situations where meteorological conditions are stable and uniform, which makes it difficult to handle complex meteorological changes. In addition, the traditional Gaussian diffusion model does not consider the impact of atmospheric factors such as precipitation, air pressure, density, humidity and temperature on the diffusion effect of pollutants, and its simulation results have certain limitations.

[0004] In view of the defects of the above-mentioned traditional Gaussian model, the present invention aims to propose a more accurate and comprehensive modified atmospheric pollutant diffusion model, which can fully consider the complex and changeable meteorological conditions (including precipitation, density, humidity, air pressure, etc.), obtain meteorological data in real time and update the pollutant diffusion simulation results. By introducing advanced numerical calculation methods and meteorological model data, the present invention will improve the accuracy and reliability of atmospheric pollutant diffusion simulation and provide more scientific and accurate decision support for related fields. Summary of the invention

[0005] In view of this, the present invention provides a pollutant diffusion modeling method and model based on meteorological data, which can take into account both temporal changes and meteorological factors, so that the constructed model can better reflect the actual pollutant diffusion situation.

[0006] The technical solution for implementing the present invention is as follows:

[0007] In a first aspect, the present application provides a pollutant diffusion modeling method based on meteorological data, and the specific process is as follows:

[0008] Step 1: Acquisition and preprocessing of meteorological data;

[0009] Step 2: Based on the Gaussian diffusion model of continuous point sources, a three-dimensional Gaussian diffusion model that changes with time is obtained;

[0010] Step 3: Calculate meteorological factors based on meteorological element information, and obtain a pollutant Gaussian diffusion model that takes meteorological data into account based on the meteorological factors.

[0011] Furthermore, the three-dimensional Gaussian diffusion model of the present invention that varies with time:

[0012]

[0013] Where C is the concentration of pollutants at any point in space (x, y, z); σ x , σ y , σ z are the diffusion parameters in the x, y, and z directions; u is the average wind speed; x is the distance from the spatial point to the source along the downwind direction; y is the distance from the spatial point to the source along the direction perpendicular to the wind direction; z is the height of the spatial point; H is the pollutant release height, and q is the source strength.

[0014] Furthermore, the meteorological factor is calculated based on the meteorological element information as follows:

[0015]

[0016] Where, P is the atmospheric pressure; P b is the saturated vapor pressure under the current state; T is the ambient temperature; is the air humidity; ρ0 is the initial air density.

[0017] Furthermore, the present invention also includes a precipitation factor of:

[0018]

[0019] β=1.2×10 -5 I 0.5

[0020] Among them, I is the rainfall intensity, β is the adsorption coefficient, u refers to the average wind speed of the environment, and x is the distance from the spatial point to the source along the downwind direction.

[0021] Furthermore, the Gaussian diffusion model of pollutants in the present invention is:

[0022]

[0023] Among them, Q(x)=q*R(x).

[0024] Furthermore, the present invention also includes step 4, using a pollutant Gaussian diffusion model taking into account meteorological data to perform diffusion simulation and visualize the results, and finally obtain a diffusion distribution map of atmospheric pollutants over time under the influence of different meteorological elements.

[0025] Furthermore, the meteorological data in step 1 of the present invention is obtained through the WRF model or the ERA5 dataset, and the preprocessing includes format conversion, time alignment, and spatial interpolation to ensure that the meteorological data can match the geographic and time coordinates in the model.

[0026] In a second aspect, the embodiment of the present application provides a pollutant diffusion model based on meteorological data, which is:

[0027]

[0028] Among them, σ x , σ y , σ z are diffusion parameters in the x, y, and z directions; u is the average wind speed; x is the distance from the spatial point to the source along the downwind direction; y is the distance from the spatial point to the source along the direction perpendicular to the wind direction; z is the height of the spatial point; H is the height of pollutant release; K 气象 is the meteorological factor;

[0029] Q(x)=q*R(x)

[0030] Among them, q is the source strength and R(x) is the precipitation factor.

[0031] Furthermore, the meteorological factor K of the present invention 气象 for:

[0032]

[0033] Where, P is the atmospheric pressure; P b is the saturated vapor pressure under the current state; T is the ambient temperature; is the air humidity; ρ0 is the initial air density.

[0034] Furthermore, the precipitation factor R(x) of the present invention is:

[0035]

[0036] β=1.2×10 -5 I 0.5

[0037] Among them, I is the rainfall intensity, β is the adsorption coefficient, u refers to the average wind speed of the environment, and x is the distance from the spatial point to the source along the downwind direction.

[0038] Beneficial effects:

[0039] The present invention first processes the meteorological data, then performs multi-step calculations by improving the Gaussian diffusion model, and then visualizes the diffusion simulation result data, and finally obtains the atmospheric pollutant diffusion distribution map over time under the influence of different meteorological elements. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 It is the pollutant diffusion simulation algorithm flow based on real-time meteorological data in the present invention;

[0042] Figure 2 It is the operation flow chart of the WRF model in the present invention;

[0043] Figure 3 It is a schematic diagram of obtaining meteorological element variables in the present invention;

[0044] Figure 4 It is a typical NetCDF file data structure;

[0045] Figure 5 is the three-dimensional coordinate system of the Gaussian diffusion model;

[0046] Figure 6 It is a calculation flow chart of Gaussian diffusion model in the present invention;

[0047] Figure 7 It is a schematic diagram of iterative superposition calculation of Gaussian diffusion model;

[0048] Figure 8 It is a flow chart of algorithm design for improving Gaussian diffusion model in the present invention;

[0049] Fig. 9 It is a schematic diagram of the results of the development of pollutant concentration over time in the present invention;

[0050] Fig.10 It is a schematic diagram showing the effect of different temperatures on the concentration of pollutants in the present invention;

[0051] Fig.11 It is a schematic diagram showing the effect of different air pressures on the concentration of pollutants in the present invention;

[0052] Fig.12 It is a schematic diagram showing the results of pollutant concentration being affected by different humidity in the present invention;

[0053] Fig.13 It is a schematic diagram of the results of the pollutant concentration being affected by different densities in the present invention;

[0054] Fig.14 It is a schematic diagram of the results of the pollutant concentration being affected by different precipitation levels in the present invention;

[0055] Fig.15It is a schematic diagram of the results of pollutant concentration being affected by different wind fields in the present invention. DETAILED DESCRIPTION

[0056] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0057] It should be noted that the following embodiments and features in the embodiments may be combined with each other in the absence of conflict; and, based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making any creative work are within the scope of protection of the present disclosure.

[0058] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0059] The present application embodiment provides a pollutant diffusion modeling method based on meteorological data, such as Figure 1 As shown, it includes the following steps:

[0060] Step 1: Meteorological data collection and preprocessing

[0061] Acquire meteorological model data and preprocess it. In this embodiment, meteorological data can be acquired through WRF model or ERA5 data set.

[0062] (1) Obtaining meteorological data using the WRF model

[0063] The WRF (Weather Research and Forecasting) model is used to simulate and provide atmospheric environmental parameters. The WRF model can simulate and predict a variety of physical and chemical processes in the atmosphere, including temperature, humidity, wind speed, wind direction, air pressure, and precipitation. Its unique cross-scale simulation capability can accurately capture weather phenomena from mesoscale to global scale. The WRF operation flow chart is as follows: Figure 2 As shown, the key steps can be summarized into the following three parts:

[0064] ① Data preparation and preprocessing: Obtain static geographic, meteorological reanalysis and observation data from WRF official resources, configure the computational domain and grid settings through the WPS toolset, generate terrain grids and intermediate data files suitable for the WRF model, and finally integrate them into a comprehensive background field.

[0065] ②Simulation calculation: configure key parameters such as simulation duration and vertical stratification, use real.exe to generate initial and boundary conditions, and then wrf.exe to start numerical calculations to simulate the dynamic changes of the atmospheric environment.

[0066] ③ Post-processing and visualization of results: Use NCL tools to extract key variables from simulation outputs, perform data processing and visualization, and help users intuitively understand simulation results.

[0067] (2) Obtaining meteorological data from the ERA5 dataset

[0068] ERA5 is the fifth generation of ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric reanalysis datasets of global climate from 1940 to the present. ERA5 provides hourly estimates of a large number of atmospheric, land and ocean climate variables. ERA5 combines model data with observational data from around the world, using data assimilation techniques to form a globally complete and consistent dataset.

[0069] ① Data download: The high-altitude data is obtained from the data set "ERA5 hourly data on pressure levels from 1940 to present". The download steps are as follows:

[0070] (a) When you log in to https: / / cds.climate.copernicus.eu / for the first time, you need to register an account;

[0071] (b) Enter the ERA5 pressure stratification data download page;

[0072] https: / / cds.climate.copernicus.eu / cdsapp#! / dataset / reanalysis-er a5-pressure-levels? tab=overview

[0073] (c) Click Download data to enter the data selection page. In the selection page, you can select meteorological data variables, including wind speed, wind direction, temperature, relative humidity, etc. The product type and variable selection page is as follows Figure 3 shown.

[0074] ②Data processing: For the downloaded data, further processing methods need to be applied to this algorithm. The specific steps are as follows:

[0075] (a) Region selection: determine the data latitude and longitude range according to requirements;

[0076] (b) Variable selection: Use CDO to select the required meteorological element variables from the ERA5 downloaded data file. The variable selection command based on CDO is as follows:

[0077] #cdo-selname,var1,var2 ifile.nc ofile.nc

[0078] (c) Horizontal interpolation: CDO is used to perform horizontal interpolation. For example, the variables of the meteorological data are interpolated from a horizontal grid of 0.25° × 0.25° to a horizontal grid of 0.1° × 0.1°.

[0079] First, create a target file that describes a 0.1°×0.1° horizontal grid with the following content:

[0080] #gridID 1

[0081] gridtype=lonlat

[0082] datatype=float

[0083] xsize=51

[0084] ysize=51

[0085] xname = longitude

[0086] yname = latitude

[0087] xfirst=115

[0088] xinc=0.10000

[0089] yfirst=23

[0090] yinc=0.100000

[0091] scanningMode=64

[0092] Then, run the following CDO command to perform horizontal interpolation:

[0093] #cdo remapbil,targetgrid.txt ifile.nc ofile.nc

[0094] Among them, ifile.nc is the data file with a horizontal resolution of 0.25°×0.25° after area selection, and ofile.nc is the output data file with a horizontal resolution of 0.1°×0.1°.

[0095] (d) Vertical interpolation: The meteorological element variables after horizontal interpolation are vertically interpolated as needed, and the variables are interpolated from the isobaric surface to the isohyper surface. The following is an example

[0096] #hlevel=`seq-s","0 50 17000`:

[0097] Data format processing

[0098] NetCDF (Network Common Data Form) is a standard format designed for efficient storage and network exchange of multidimensional scientific data. The main feature of the NetCDF file format is that it can effectively manage and distribute array data, especially large-scale scientific data sets that need to be shared in a network environment. It was originally developed to meet the data storage needs of the meteorology field, and is now widely used in many fields such as earth science and environmental science. Figure 4 This is the data structure of a typical NetCDF file.

[0099] The suffix of NetCDF file is generally .nc or .nc4, and its structure mainly includes four core parts: groups, dimensions, variables and attributes.

[0100] Groups: Provides a way to organize variables. The NetCDF 4 standard supports a hierarchical group structure, allowing related variables and attributes to be grouped together, allowing for more flexible data management.

[0101] Dimensions: Before creating a variable, you must first define its associated dimensions, which specify the length of the variable in each dimension.

[0102] Variables: Variables are used to store actual data values. They are in the form of multidimensional arrays, and each variable is associated with a specific dimension.

[0103] Attributes: To make data meaningful and understandable, both variables and dimensions can have attributes attached to them. Attributes provide metadata information that is critical to interpreting the data.

[0104] In this algorithm, the meteorological element data obtained from the WRF simulation or ERA5 dataset are converted into a unified .nc format through the corresponding algorithm, and an interactive interface between the meteorological data and the improved Gaussian diffusion model is established to realize the real-time coupling calculation of the meteorological data module and the pollutant diffusion module, ensuring that the simulation results of the pollutant diffusion effect are updated in real time.

[0105] Step 2: Based on the Gaussian diffusion model of continuous point sources, a three-dimensional Gaussian diffusion model that changes with time is obtained;

[0106] The traditional Gaussian model is applicable to uniform atmospheric conditions and is based on the following assumptions: the wind speed inside the simulation space is uniform and stable; the release of pollutant sources is continuous and uniform; the mass of pollutants remains conserved during the overall diffusion process (without considering material transformations such as nuclide decay); in addition, the theory assumes that the concentration of pollutants during continuous point source diffusion conforms to the law of normal distribution.

[0107] To simulate the diffusion of pollutants under point source release using the above Gaussian model, we first need to establish a three-dimensional coordinate system, taking the vertical projection point of the pollutant release point to the ground as the coordinate origin, taking the z-axis along this point perpendicular to the ground, taking the main wind direction as the x-axis, and obtaining the corresponding direction of the y-axis through the right-hand rule. Establish a three-dimensional coordinate system in Gaussian space, as shown in the attached figure. Figure 5 shown.

[0108] Based on the above assumptions, the pollutant concentration distribution function at any point in the simulation area can be obtained as:

[0109]

[0110] Where: C is the concentration of pollutants at any point (x, y, z) in the target area, kg / m 3 ; A(x) is the function to be determined; σ y , σ z is the diffusion parameter in the y and z directions, m.

[0111] Considering the continuous emission of point sources and the law of mass conservation, on any cross section perpendicular to the x-axis, there exists:

[0112]

[0113] Where: q is the source strength, that is, the rate at which point sources emit pollutants; u is the average ambient wind speed, m / s.

[0114] Further considering the stability and uniformity of the wind field, the undetermined function can be obtained:

[0115] A(x)=q / 2πuσ y σ z (3)

[0116] In summary, the core formula of the Gaussian diffusion model of continuous point sources can be derived:

[0117]

[0118] The specific calculation steps of pollutant concentration are as follows: Figure 6 As shown:

[0119] The Gaussian diffusion model of continuous point sources in the above formula (4) is a traditional Gaussian model that only considers wind speed and wind direction. It is suitable for continuous leakage diffusion and uniform meteorological fields, and can solve the steady-state distribution characteristics of pollutant diffusion in this specific case. However, for the actual diffusion of pollutants in the atmosphere, the background field of meteorological elements will change over time, that is, the time effect should be considered in the Gaussian diffusion model to obtain the nuclear pollutant diffusion results that change over time.

[0120] Generally speaking, the smoke released by a point source starts from the origin o and migrates continuously with the wind direction, and diffuses in the y and z directions at the same time. According to the material balance, the following three-dimensional Gaussian diffusion formula that changes with time can be further derived:

[0121]

[0122] Where C is the concentration of pollutants at any point in space (x, y, z), kg / m 3 ; σ x , σ y , σ z are the diffusion parameters in the x, y, and z directions; u is the average wind speed; x is the distance from the spatial point to the source along the downwind direction; y is the distance from the spatial point to the source along the direction perpendicular to the wind direction; z is the height of the spatial point; and H is the pollutant release height.

[0123] The above diffusion model is suitable for leakage in a short period of time. If it is to be used for continuous point source release diffusion, the model needs to be further improved. Specifically, the leakage process can be divided in time and considered as multiple short-term leakage sources according to the time interval. The pollutant concentration distribution in the area generated by continuous emission is the algebraic superposition of the concentration distribution generated by multiple instantaneous sources, as shown in the attached figure. Figure 7 As shown. That is, assuming that the diffusion time of pollutant leakage is t, and n instantaneous leakage sources are divided with a time interval Δt (consistent with the time resolution of the meteorological element background field), the concentration distribution of the smoke cloud generated by the i-th leakage source diffused along the wind direction to the time t can be calculated by the following formula:

[0124]

[0125] By superimposing the concentration of pollutants generated by each instantaneous leakage source, the concentration distribution of pollutants generated by the continuous emission pollution source can be obtained, which can be expressed as the following formula:

[0126]

[0127]

[0128] The above Gaussian model assumes that wind direction and wind speed are constant parameters, but in reality, wind speed and wind direction change randomly, that is, environmental factors change. Therefore, coordinate correction is required during the superposition calculation iteration process. Take the reference coordinate system xoy as a reference. When the wind direction and wind speed change, the instantaneous coordinate system x′oy′ is established with the new downwind direction as the x-axis. The spatial position between the two coordinate systems needs to be rotated and translated. The coordinate transformation expression is:

[0129] y′=[yy m (n)]cosβ(n)-[xx m (n)]sinβ(n) (8)

[0130]

[0131] Where: (x, y) is the coordinate of the calculation point in the xoy coordinate system; (x′, y′) is the coordinate of the calculation point in the x′oy′ coordinate system; x m (n), y m (n) are the horizontal and vertical coordinates of the initial position of the nth group of smoke clouds, and can be set to (0, 0) if the origin remains unchanged; β(n) is the angle between the wind direction in the nth time step and the x-axis of the reference coordinate system.

[0132] Step 3: Calculate meteorological factors based on meteorological element information, and obtain a pollutant Gaussian diffusion model taking into account meteorological data based on the meteorological factors;

[0133] In actual situations, other elements in the atmosphere such as humidity, density, air pressure, temperature and precipitation will have a significant impact on the diffusion of pollutants.

[0134] Precipitation is an atmospheric element that has an important impact on the diffusion of pollutants. During precipitation, raindrops will interact with pollutants in the atmosphere and transfer pollutants from the gas phase to the liquid phase through the wet deposition process. This process will deeply affect the concentration of pollutants in the atmosphere. Considering the impact of rainfall on the concentration of diffused gases, the Gaussian model is further improved. The improved point source Gaussian diffusion model is as follows:

[0135]

[0136] in,

[0137]

[0138] β=1.2×10 -5 I 0.5 (12)

[0139] Where I is the rainfall intensity, in mm·h -1 ; β is the adsorption coefficient; unit is m·s -1 .

[0140] In addition to rain erosion, other atmospheric factors will also affect the diffusion of pollutants. Changes in humidity will affect the stability of the atmosphere and the turbulent mixing ability, thereby affecting the diffusion of pollutants. The density of the atmosphere will also affect the diffusion of pollutants. In a denser atmosphere, the diffusion rate of pollutants may slow down because collisions between molecules are more frequent, hindering the diffusion of pollutants. Changes in temperature will affect the stability of the atmosphere, thereby affecting the diffusion ability of pollutants. Air pressure indirectly affects the diffusion of pollutants by affecting air density, weather systems and other meteorological conditions. Further incorporating conventional meteorological elements such as temperature, humidity, air pressure and atmospheric density into the Gaussian model, as follows

[0141] C(x,y,z,t) 气象 =C(x,y,z,t)*K 气象 (13) Among them, K 气象 Meteorological factors are as follows:

[0142]

[0143] Where P is the atmospheric pressure, in kPa; P b is the saturated vapor pressure in the current state, in kPa; T is the ambient temperature, in °C; is the air humidity, in %, and is calculated by substituting the decimal; ρ0 is the initial air density, in kg / m 3 .

[0144] Saturated vapor pressure P b It is usually directly related to temperature and can be directly solved by empirical formulas. The most commonly used simplified model is as follows:

[0145]

[0146] Substituting all meteorological element parameters, the mathematical expression of the Gaussian plume diffusion model that takes multiple meteorological elements into consideration can be obtained, as shown below:

[0147]

[0148] So far, an improved Gaussian diffusion model that includes conventional meteorological elements such as precipitation, temperature, humidity, density, and air pressure has been established, further improving its applicability and accuracy in simulating mesoscale pollutant diffusion.

[0149] Step 4: Use the pollutant Gaussian diffusion model that takes into account meteorological data to perform diffusion simulation and visualize the results, and finally obtain the atmospheric pollutant diffusion distribution map over time under the influence of different meteorological factors.

[0150] Attached Figure 8 To improve the algorithm design flow chart of the Gaussian diffusion model, the pollution source module collects source parameters such as the release rate of pollutants in real time and transmits them to the simulation calculation module. The meteorological parameter module obtains meteorological parameters such as wind speed, wind direction, temperature, humidity, etc. in real time and transmits them to the simulation calculation module. The simulation calculation module processes the source data, meteorological data, and custom parameters such as the calculation area size, and substitutes them into the model for concentration distribution calculation. Finally, the concentration distribution cloud map of pollutants in the atmosphere is obtained through visualization. The specific steps are as follows:

[0151] 1. Pollution source setting

[0152] Set the basic information of the pollutant emission source, such as location, release height, and emission speed. These information will be used as input parameters of the model.

[0153] 2. Meteorological element settings

[0154] ① Data source selection: Determine whether to use the WRF model or the ERA5 dataset as the source of meteorological data.

[0155] ②Data acquisition: Obtain the required meteorological data through WRF model simulation or download from the ERA5 official website, including wind speed, wind direction, temperature, humidity, air pressure and other parameters.

[0156] ③ Data preprocessing: The acquired data needs to be preprocessed, including format conversion, time alignment, spatial interpolation, etc., to ensure that the meteorological data can match the geographic and time coordinates in the model. During data processing, the appropriate time and spatial resolution can be selected according to research needs.

[0157] 3. Simulate regional settings

[0158] ① Determine the simulation area: clarify the geographical area of ​​the simulation, which is usually determined based on the research objectives, the location of pollution sources, and sensitive areas of concern. The simulation area should be large enough to include all important areas where pollutants may spread, while also taking into account the limitations of computing resources.

[0159] ② Set the grid resolution: The grid resolution refers to the size of each cell in the grid, which determines the spatial accuracy of the algorithm model. The choice of resolution should be based on research needs, data availability, and computing resources.

[0160] ③ Grid division: According to the determined simulation area and grid resolution, the entire area is divided into a series of regular grid cells.

[0161] ④ Initialize grid data: After the grid division is completed, each grid unit needs to be initialized, including setting the initial concentration value (usually 0, indicating no pollutants), recording the grid coordinate information, etc.

[0162] 4. Diffusion formula calculation

[0163] ① Time step setting: Set an appropriate time step (such as every hour) based on research requirements and the temporal resolution of the data.

[0164] ② Concentration calculation: According to Gaussian diffusion theory, the pollutant concentration at any point downwind can be calculated using the improved Gaussian diffusion model formula, which involves the standard deviation in each direction (i.e., diffusion coefficient), and these parameters are related to the location of the grid point. In each time step, the current meteorological data and pollution source data are used to calculate the concentration distribution of pollutants using the Gaussian diffusion formula.

[0165] ③ Iterative calculation: Since meteorological conditions such as wind speed and wind direction will change over time, it is necessary to continuously update meteorological data during the calculation process and adjust the parameters in the Gaussian diffusion formula accordingly. That is, perform a separate calculation for each time step (such as every hour), then advance the time one step forward, repeat the above process until the simulation ends, and superimpose the results.

[0166] 5. Result visualization steps

[0167] ①Solution of simulation results: The pollutant concentration data at each time step calculated by the Gaussian diffusion model are sorted into a unified format, including the spatial coordinates (x, y, z) and the corresponding concentration value C.

[0168] ② Draw a concentration distribution map: Use contour maps to show the spatial distribution of pollutant concentrations. The depth of color or the size of the symbol can indicate the concentration, and the specific value is shown in the legend color scale.

[0169] Combined with the visualization results, the temporal and spatial variation characteristics of pollutant diffusion are analyzed and discussed, and the possible influencing factors and potential risks are analyzed to provide data reference for relevant decision-making.

[0170] Attached Figures 9 to 15 The above content proves the effectiveness of the modified Gaussian algorithm, that is, the present invention can generate corresponding pollutant diffusion results in combination with the meteorological conditions input in real time.

[0171] The present application embodiment provides a pollutant diffusion model based on meteorological data.

[0172]

[0173] Among them, σ x , σ y , σ z are diffusion parameters in the x, y, and z directions; u is the average wind speed; x is the distance from the spatial point to the source along the downwind direction; y is the distance from the spatial point to the source along the direction perpendicular to the wind direction; z is the height of the spatial point; H is the height of pollutant release; K 气象 is the meteorological factor;

[0174] Q(x)=q*R(x)

[0175] Among them, q is the source strength and R(x) is the precipitation factor.

[0176] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A pollutant diffusion modeling method based on meteorological data, characterized in that: The specific process is: Step 1: Acquisition and preprocessing of meteorological data; Step 2: Based on the Gaussian diffusion model of continuous point sources, a three-dimensional Gaussian diffusion model that changes with time is obtained; Step 3: Calculate meteorological factors based on meteorological element information, and obtain a pollutant Gaussian diffusion model that takes meteorological data into account based on the meteorological factors.

2. The pollutant diffusion modeling method based on meteorological data according to claim 1 is characterized in that: Three-dimensional Gaussian diffusion model with time variation: Where C is the concentration of pollutants at any point in space (x, y, z); σ x , σ y , σ z are the diffusion parameters in the x, y, and z directions; u is the average wind speed; x is the distance from the spatial point to the source along the downwind direction; y is the distance from the spatial point to the source along the direction perpendicular to the wind direction; z is the height of the spatial point; H is the pollutant release height, and q is the source strength.

3. The pollutant diffusion modeling method based on meteorological data according to claim 2 is characterized in that: Based on the meteorological element information, the meteorological factor is calculated as: Where, P is the atmospheric pressure; P b is the saturated vapor pressure under the current state; T is the ambient temperature; is the air humidity; ρ0 is the initial air density.

4. The pollutant diffusion modeling method based on meteorological data according to claim 3 is characterized in that: Also included is the precipitation factor: β＝1.2×10 -5 I 0.5 Among them, I is the rainfall intensity, β is the adsorption coefficient, u refers to the average wind speed of the environment, and x is the distance from the spatial point to the source along the downwind direction.

5. The pollutant diffusion modeling method based on meteorological data according to claim 4 is characterized in that: The pollutant Gaussian diffusion model is: Among them, Q(x)=q*R(x).

6. The pollutant diffusion modeling method based on meteorological data according to claim 1 is characterized in that: It also includes step 4, using the pollutant Gaussian diffusion model considering meteorological data to perform diffusion simulation and visualize the results, and finally obtain the atmospheric pollutant diffusion distribution map over time under the influence of different meteorological elements.

7. The pollutant diffusion modeling method based on meteorological data according to claim 1 is characterized in that: The meteorological data in step 1 are obtained through the WRF model or ERA5 dataset. The preprocessing includes format conversion, time alignment, and spatial interpolation to ensure that the meteorological data can match the geographic and time coordinates in the model.

8. A pollutant diffusion model based on meteorological data, characterized in that: The model is: Among them, σ x , σ y , σ z are diffusion parameters in the x, y, and z directions; u is the average wind speed; x is the distance from the spatial point to the source along the downwind direction; y is the distance from the spatial point to the source along the direction perpendicular to the wind direction; z is the height of the spatial point; H is the height of pollutant release; K 气象 is the meteorological factor; Q(x)=q*R(x) Among them, q is the source strength and R(x) is the precipitation factor.

9. The pollutant diffusion model based on meteorological data according to claim 8, characterized in that: The meteorological factor K 气象 for: Where, P is the atmospheric pressure; P b is the saturated vapor pressure under the current state; T is the ambient temperature; is the air humidity; ρ0 is the initial air density.

10. The pollutant diffusion model based on meteorological data according to claim 8, characterized in that: The precipitation factor R(x) is: β＝1.2×10 -5 I 0.5 Among them, I is the rainfall intensity, β is the adsorption coefficient, u refers to the average wind speed of the environment, and x is the distance from the spatial point to the source along the downwind direction.

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