Chemical pollution source multi-scene diffusion calculation method

Abstract

The invention discloses a multi-scene diffusion calculation method for a chemical pollution source. The method comprises the following steps: (1) parameter input and verification: inputting pollution source parameters, meteorological parameters, calculation region parameters and pollutant types; performing numerical range, data type and logic consistency verification on the input parameters, filling default values when non-key parameters are missing, and prompting correction when the parameters are abnormal; (2) coordinate conversion and caching; (3) calculating diffusion parameters; (4) single-source concentration calculation; (5) multi-source concentration superposition; and (6) result output: outputting a calculation result in the forms of a data table, a scatter diagram and a JSON file. According to the method, multi-scene unified calculation can be provided, an instantaneous point source, a continuous point source, an instantaneous multi-point source and a moving line source are supported, dynamic weather, terrain and pollutant characteristic correction are introduced to improve precision, calculation logic is optimized, and emergency efficiency is ensured.

Description

Technical Field

[0001] This invention relates to the field of environmental science and engineering technology, and in particular to a multi-scenario diffusion calculation method for chemical pollution sources, applicable to scenarios such as emergency response to sudden pollution, pollution risk assessment of industrial parks, and environmental impact assessment of construction projects. Background Technology

[0002] Atmospheric pollutant diffusion simulation is a core technology for assessing pollution risks and developing emergency response measures. Current mainstream technologies are based on Gaussian diffusion theory, but they have the following key problems: (1) Insufficient adaptation to multi-source scenarios Existing models are mostly designed for single-point sources (such as the instantaneous point source model in HJ2.2-2018), which cannot handle multi-source collaborative diffusion scenarios such as multi-point leakage and multi-point explosion in industrial parks; some multi-point source models ignore the mutual interference of turbulence between point sources (the error reaches more than 25% when the distance is <50m), resulting in significant deviations between the concentration calculation results and the actual results.

[0003] (2) Poor adaptability to complex conditions Meteorological conditions: The assumption of "constant wind speed and stability" cannot reflect the dynamic changes in the actual atmosphere (such as the change from unstable to neutral during the day), with an error of 20%-30%. Terrain conditions: The effects of obstacles such as tall buildings and mountains have not been considered. The actual concentration in complex terrain may be more than 50% higher than the calculated value. Pollutant characteristics: Assuming the pollutants are inert substances and ignoring sedimentation (such as particulate matter) and chemical reactions (such as NOx to NO2), the actual concentration deviates from the calculated value by 15%-40%.

[0004] (3) It is difficult to balance computational efficiency and accuracy. Foreign high-precision models (such as CALPUFF) rely on a large amount of measured data and have low computational efficiency (15-20 seconds for 5000 grid points), which cannot meet the emergency response requirements for sudden pollution (requires <10 seconds); domestic simplified models are fast, but their accuracy is insufficient (error 30%-50%), making it difficult to support scientific decision-making.

[0005] Therefore, there is an urgent need for a diffusion computing method that covers multiple scenarios, adapts to complex conditions, and balances efficiency and accuracy. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a multi-scenario diffusion calculation method for chemical pollution sources that addresses the shortcomings of the existing technology. This method can provide unified calculation for multiple scenarios, supports instantaneous point sources, continuous point sources, instantaneous multi-point sources, and moving line sources, introduces dynamic meteorological, terrain, and pollutant characteristic corrections to improve accuracy, and optimizes the calculation logic to ensure emergency response efficiency.

[0007] The technical problem to be solved by this invention is achieved through the following technical solution. This invention is a multi-scenario diffusion calculation method for chemical pollution sources, which includes the following steps: (1) Parameter input and verification: Input pollution source parameters, meteorological parameters, calculation area parameters, and pollutant types; perform numerical range, data type, and logical consistency checks on the input parameters; fill in default values ​​when non-critical parameters are missing; and prompt for correction when parameters are abnormal. (2) Coordinate transformation and caching: Convert the latitude and longitude of each point source and the grid points in the computational region to UTM plane coordinates. If the computational region spans multiple UTM zones, divide the region into multiple UTM zones and process them separately. Cache the UTM coordinates of all point sources to avoid repeated conversion and calculation. (3) Calculation of diffusion parameters: A simplified diffusion parameter table, combined with the time t>0s after emission and atmospheric stability, obtains the crosswind diffusion parameter σ_y,i and vertical diffusion parameter σ_z,i for each point source through linear interpolation; the downwind diffusion parameter σ_x,i=1.5×σ_y,i; If the point source spacing d_ij < 2×(σ_y,i+σ_y,j), introduce the turbulence disturbance factor f_ij=0.1×(1−d_ij / (2×(σ_y,i+σ_y,j))), and correct the diffusion parameters σ'_y,i=σ_y,i×(1+f_ij) and σ'_y,j=σ_y,j×(1+f_ij); If dynamic meteorological time series data is input, the diffusion parameters for each time period are calculated. (4) Single-source concentration calculation: For each point source i, calculate the UTM relative distance d between the grid point and point source i = √[(x_utm,calc−x_utm,source,i)²+(y_utm,calc−y_utm,source,i)²] and the azimuth angle α = arctan2(x_utm,calc−x_utm,source,i,y_utm,calc−y_utm,source,i)×180 / π; based on wind... To determine the airflow direction (wind direction + 180°), calculate Δα = α − airflow direction; the downwind distance x_i of the grid point relative to point source i is x_i = d × cos(Δα), and the crosswind distance y_i is d × sin(Δα); substituting Q_i, σ_x,i, σ_y,i (or σ'_y,i), σ_z,i, x_i, y_i, u, and t into the Gaussian instantaneous point source concentration formula, calculate the concentration contribution C_i(x,y,t) of point source i at that grid point, as follows:

[0008] (5) Concentration superposition from multiple sources: The concentration contributions of each point source at the same grid point are summed to obtain the total concentration of the grid point C_total(x,y,t)=Σ(i=1 to n)C_i(x,y,t); (6) Output of results: Output the calculation results in the form of data tables, scatter plots, and JSON files.

[0009] The technical problem to be solved by the present invention can also be further realized through the following technical solutions. For the above-mentioned multi-scenario diffusion calculation method of chemical pollution sources, in step (1), the pollution source parameters include the number of point sources, the latitude and longitude of each point source and the total emissions of each point source, and the meteorological parameters include the average wind speed, wind direction, atmospheric stability and optional dynamic meteorological time series data.

[0010] The technical problem to be solved by the present invention can also be further realized through the following technical solutions. For the above-mentioned multi-scenario diffusion calculation method for chemical pollution sources, in step (2), the UTM coordinate transformation adopts the WGS84 ellipsoid parameters, and the plane coordinate error is less than 0.1m.

[0011] The technical problem to be solved by the present invention can also be further realized through the following technical solutions. For the above-mentioned multi-scenario diffusion calculation method for chemical pollution sources, in step (3), the diffusion parameter table of the simplified version of the "Technical Guidelines for Environmental Impact Assessment - Atmospheric Environment" is used.

[0012] The technical problem to be solved by the present invention can also be further realized by the following technical solution. For the above-mentioned multi-scenario diffusion calculation method of chemical pollution sources, in step (3), if there are terrain obstacles in the calculation area, DEM terrain height data is introduced to correct the diffusion parameters: σ'_y,i=σ_y,i×(1+k×(h−z) / H), where h is the height of the obstacle, z is the height of the grid point, H is the height of the pollution source, k=0.3~0.5 on the leeward side and k=-0.2~-0.3 on the windward side.

[0013] The technical problem to be solved by the present invention can also be further realized by the following technical solutions. For the above-mentioned multi-scenario diffusion calculation method of chemical pollution sources, in step (4), if the pollutant has sedimentation characteristics, the sedimentation velocity v_s is introduced, and the vertical direction exponent term is corrected to exp[−(z−v_st)² / (2σ_z,i²)]; if the pollutant has chemical reaction characteristics, the reaction rate constant k_chem is introduced, and the concentration contribution C_i is multiplied by the attenuation factor exp(−k_chemt).

[0014] The technical problem to be solved by the present invention can also be further realized through the following technical solutions. For the above-mentioned multi-scenario diffusion calculation method of chemical pollution sources, in step (6), the JSON file supports importing into GIS software for further spatial analysis.

[0015] The technical problem to be solved by the present invention can also be further realized by the following technical solutions. For the above-mentioned chemical pollution source multi-scenario diffusion calculation method, a chemical pollution source multi-scenario diffusion calculation system is provided. The system includes a parameter input module, a coordinate transformation module, a diffusion parameter calculation module, a concentration calculation and superposition module, and a result output module. Each module is used to execute the above-mentioned chemical pollution source multi-scenario diffusion calculation method steps (1)-(6).

[0016] Compared with existing technologies, this invention has high calculation accuracy (error ≤20%) and high efficiency (5000 grid points <6 seconds). It can output results in multiple formats, including tables, graphs, and JSON. It is suitable for emergency response to sudden pollution incidents, industrial park planning, and environmental impact assessments, and has significant application value and economic benefits. Specific advantages are as follows: 1. Improved accuracy: Ideal condition error ≤20%, complex scene error ≤25%, a reduction of more than 50% compared to existing models; 2. Efficiency optimization: Calculation time for 5000 grid points is less than 6 seconds, meeting emergency needs; 3. Comprehensive functions: Covers multiple sources, dynamic weather, and complex terrain, supports multiple output formats, and is suitable for emergency response, planning, environmental impact assessment and other scenarios. Attached Figure Description

[0017] Figure 1 This is a scatter plot of the spatial distribution of instantaneous point source diffusion concentration according to the present invention (Example 1); the horizontal axis is "relative eastward distance (meters)" and the vertical axis is "relative northward distance (meters)"; black dots represent concentrations >0.00001 μg / m³, and gray dots represent concentrations ≤0.00001 μg / m³; the figure shows that the concentration extends along the windward direction (eastward) and is symmetrically distributed across the windward direction; Figure 2 This is a detailed graphical representation of the instantaneous point source diffusion of the present invention (Example 1); the high concentration area (10-1000 μg / m³) near the pollution source is magnified, and the location of the concentration peak (10-50m eastward, about 120 μg / m³) is marked. Figure 3 This is a scatter plot of the instantaneous multi-point source diffusion concentration spatial distribution of the present invention (Example 2); it shows the concentration superposition effect of 4 point sources, with black dots forming "multiple small aggregation areas + one large aggregation area" (multi-source overlap area), and the peak concentration is about 280 μg / m³; Figure 4This is a diagram showing the spatial distribution of the instantaneous multi-point source diffusion of the present invention (Example 2); the horizontal axis is the "relative distance to the east (meters)" and the vertical axis is the "relative distance to the north (meters)", marking the locations of 4 point sources and their diffusion ranges (approximately 350m in the downwind direction and approximately 200m in the crosswind direction). Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] A method for calculating the instantaneous multi-point source diffusion of chemical pollution sources includes the following steps: S1: Parameter Input and Validation: Input pollution source parameters (number of point sources n≥1, latitude and longitude of each point source (WGS84 coordinate system, longitude -180~180°, latitude -80~84°), total emissions of each point source Q_i>0g, pollution source height ≥0m), meteorological parameters (average wind speed u>0m / s, wind direction (0°~360°), atmospheric stability (AF type), optional dynamic meteorological time series data), calculation area parameters (latitude and longitude of rectangle vertices 1-4, grid step size 0~1000m) and pollutant type; perform numerical range, data type and logical consistency checks on the input parameters, fill in default values ​​when non-critical parameters are missing (pollution source height defaults to 0m, grid step size defaults to 5m), and prompt for correction when parameters are abnormal; S2: Coordinate Transformation and Caching Convert the latitude and longitude of each point source and the grid points in the computational region to UTM plane coordinates. If the computational region spans multiple UTM zones, divide the region into multiple UTM zones and process them separately. Cache the UTM coordinates of all point sources to avoid repeated conversion and calculation. The UTM coordinate transformation uses WGS84 ellipsoid parameters, and the plane coordinate error is less than 0.1m. S3: Diffusion parameter calculation: Based on the simplified diffusion parameter table of the "Technical Guidelines for Environmental Impact Assessment - Atmospheric Environment" (HJ2.2-2018), and combined with the post-emission time t>0s and atmospheric stability, the crosswind diffusion parameter σ_y,i and vertical diffusion parameter σ_z,i of each point source are obtained by linear interpolation; the downwind diffusion parameter σ_x,i=1.5×σ_y,i; if the point source distance d_ij<2×(σ_y,i+σ_y,j), a turbulence interference factor f_ij=0.1×(1−d_ij / (2×(σ_y,i+σ_y,j))) is introduced to correct the diffusion parameters σ'_y,i=σ_y,i×(1+f_ij) and σ'_y,j=σ_y,j×(1+f_ij); if dynamic meteorological time series data is input, the diffusion parameters for each time period are calculated separately. If there are terrain obstacles in the calculation area, DEM terrain height data is introduced to correct the diffusion parameter: σ'_y,i=σ_y,i×(1+k×(h−z) / H), where h is the obstacle height, z is the grid point height, H is the pollution source height, k=0.3~0.5 on the leeward side, and k=-0.2~-0.3 on the windward side.

[0020] S4: Single-source concentration calculation: For each point source i, calculate the UTM relative distance d between the grid point and point source i = √[(x_utm,calc−x_utm,source,i)²+(y_utm,calc−y_utm,source,i)²] and the azimuth angle α = arctan2(x_utm,calc−x_utm,source,i,y_utm,calc−y_utm,source,i)×180 / π; determine the air pressure based on the wind direction. Given the flow direction (wind direction + 180°), calculate Δα = α − airflow direction (converted to radians); the downwind distance x_i of the grid point relative to point source i is x_i = d × cos(Δα), and the crosswind distance y_i is d × sin(Δα); substitute Q_i, σ_x,i, σ_y,i (or σ'_y,i), σ_z,i, x_i, y_i, u, t into the Gaussian instantaneous point source concentration formula to calculate the concentration contribution C_i(x,y,t) of point source i at that grid point, as follows:

[0021] If the pollutant has sedimentation characteristics, a sedimentation velocity v_s is introduced, and the vertical exponential term is corrected to exp[−(z−v_st)² / (2σ_z,i²)]; if the pollutant has chemical reaction characteristics, a reaction rate constant k_chem is introduced, and the concentration contribution C_i is multiplied by the attenuation factor exp(−k_chemt). S5: Multi-source concentration superposition: The concentration contributions of each point source at the same grid point are summed to obtain the total concentration of the grid point C_total(x,y,t)=Σ(i=1 to n)C_i(x,y,t); S6: Output Results The calculation results are output in the form of a data table (including grid point longitude, latitude, and C_total), a scatter plot (C_total>0.00001μg / m³ is black, otherwise it is gray) and a JSON file (including pollution type, emission time, number of grid points, step size, and data of each grid point); Among them, JSON files can be imported into GIS software for further spatial analysis.

[0022] A multi-scenario diffusion calculation system for chemical pollution sources includes a parameter input module, a coordinate transformation module, a diffusion parameter calculation module, a concentration calculation and superposition module, and a result output module. Each module executes steps S1-S6 of the above-mentioned multi-scenario diffusion calculation method for chemical pollution sources.

[0023] Specifically: ① Parameter input and verification (step S1) Pollution source parameters: Supports one or more point sources, latitude and longitude accurate to 6 decimal places (e.g., 115.904950°), emission >0g (default 100g). Meteorological parameters: Wind direction supports 8 directions (0° North, 45° Northeast, etc.), stability is divided into AF class (default D class neutral), and time series data supports one set every 10 seconds; Verification logic: Error is prompted when the longitude exceeds -180° to 180°; the height of the pollution source is automatically corrected to 0m when it is negative; and the extreme values ​​of latitude and longitude are automatically taken to form the calculation range when the coordinates of the rectangle vertices are disordered.

[0024] ② Coordinate transformation and caching (step S2) UTM Conversion: Using WGS84 ellipsoidal parameters, latitude and longitude are converted to UTM plane coordinates (e.g., pollution source latitude and longitude 115.904950°, 39.680665° are converted to UTM coordinates 499850.20m, 4392500.50m, zone number 50N). Cross-zone processing: If the longitude of the calculation area changes from 119° (zone 50) to 121° (zone 51), it is divided into two UTM zones for separate calculation to avoid the distance error from 0.5% to below 0.1%; Caching mechanism: Cache the UTM coordinates of all point sources to reduce repeated conversions and improve efficiency by 10%-12%.

[0025] ③ Calculation of diffusion parameters (step S3) Basic parameters: Based on the simplified table of HJ2.2-2018, such as D-type stability, at t=60s, σ_y=28m, σ_z=18m, σ_x=1.5×28=42m; Linear interpolation: If t = 90s (between 60s and 120s), σ_y = 28 + (σ_y, 120−28) × (90−60) / (120−60); Multi-source interference correction: If the distance between point sources i and j is d_ij = 40m, σ_y,i = 28m, σ_y,j = 28m, 2×(28+28) = 112m > 40m, f_ij = 0.1×(1−40 / 112) = 0.064, σ'_y,i=28×(1+0.064)=29.79m; Dynamic meteorology: If the time series data is "wind speed 3m / s from 0-30s, wind speed 4m / s from 31-60s", calculate σ_y in two segments and then superimpose the concentration.

[0026] ④ Single-source concentration calculation (step S4) Spatial distance: e.g., grid point UTM coordinates 499900.15m, 4392518.30m, point source iUTM coordinates 499850.20m, 4392500.50m. d = √[(499900.15−499850.20)²+(4392518.30−4392500.50)²] = 53.02m, α = 70.9°, airflow direction = 270°+180° = 90°, Δα = 70.9°−90° = −19.1° (-0.333rad), x_i = 53.02×cos(−0.333) = 49.95m, y_i = 53.02×sin(−0.333) = −17.34m; Concentration formula: Substituting Q_i=100g, σ_x=42m, σ_y=28m, σ_z=18m, u=3m / s, t=60s, we get Ci=1.86μg / m³. Characteristic correction: If the pollutant is PM2.5 (v_s=0.001m / s), the vertical term is exp[−(z−0.001×60)² / (2×18²)]; if it is SO2 (k_chem=0.001s⁻¹), multiply by exp(−0.001×60)=0.941.

[0027] ⑤ Multi-source superposition and output (steps S5-S6) Superposition: The concentration contributions of the four point sources are 1.86, 1.52, 1.28, and 0.95 μg / m³, respectively, and the total concentration = 1.86 + 1.52 + 1.28 + 0.95 = 5.61 μg / m³; Output: The table displays "longitude 115.905500, latitude 39.680800, concentration 5.61μg / m³", and the point is black in the scatter plot. The JSON file contains information such as grid step size 5m and number of grid points 7200.

[0028] A multi-scenario diffusion calculation system for chemical pollution sources, the core technical solution of which includes: (1) Parameter processing module Input parameters are categorized as follows: pollution source parameters (number of point sources, latitude and longitude, emissions, etc.), meteorological parameters (wind speed, wind direction, stability, time series data supported), computational area parameters (rectangle vertices, grid step size), and pollutant characteristic parameters (sedimentation velocity, reaction rate). Intelligent verification: Automatically verifies parameter range (e.g., longitude -180~180°) and data type, and fills in default values ​​when non-critical parameters are missing (e.g., pollution source height defaults to 0m).

[0029] (2) Coordinate transformation module UTM conversion: Converts latitude and longitude (WGS84) to UTM plane coordinates, eliminating the effects of Earth's curvature; Cross-band processing: When calculating a region that spans multiple UTM bands, it is divided into multiple bands for separate calculation to avoid distance errors (the error is reduced from 0.5% to below 0.1%). Coordinate caching: Caches the UTM coordinates of point sources, reducing redundant calculations and improving efficiency by 10%-12%.

[0030] (3) Diffusion parameter calculation module Basic parameters: Based on the simplified table of HJ2.2-2018, σ_y and σ_z are obtained by linear interpolation in combination with time and stability, and σ_x = 1.5 × σ_y; Multi-source interference correction: When the point source spacing d_ij < 2×(σ_y,i+σ_y,j), an interference factor f_ij=0.1×(1−d_ij / (2×(σ_y,i+σ_y,j))) is introduced to correct σ_y; Terrain correction: DEM data is introduced, and the correction formula is σ'_y=σ_y×(1+k×(h−z) / H) (k=0.3~0.5 on the leeward side, k=-0.2~-0.3 on the windward side). Dynamic meteorological correction: When inputting time-series meteorological data, the diffusion parameters are calculated in different time periods, and the concentrations of each time period are superimposed.

[0031] (4) Concentration Calculation and Overlay Module Single-source concentration: The concentration contribution of a single point source is calculated based on the Gaussian formula, with corrections introduced for sedimentation (exp[−(z−v_st)^2 / (2σ_z²)]) and reaction (exp(−k_chemt)). Multi-source superposition: Total concentration C_total=ΣC_i (i=1 to n, n is the number of point sources), reflecting the synergistic effect of multiple sources.

[0032] (5) Result Output Module Multiple output formats: data tables (longitude, latitude, C_total), scatter plots (concentration > 0.00001 μg / m³ is black, otherwise gray), and JSON files (supports GIS import).

[0033] refer to Figure 1-2 Example 1: Instantaneous point source diffusion calculation ① Input parameters Pollution source parameters: Longitude 115.904950°, Latitude 39.680665°, Altitude 0m, Emission 100g; Meteorological parameters: wind speed 3.0 m / s, wind direction 270° (west), stability class D, time after emission 60 s; Calculation region parameters: Vertex 1 (115.904240°, 39.682250°), Vertex 2 (115.909893°, 39.682250°), Vertex 3 (115.909893°, 39.679180°), Vertex 4 (115.904240°, 39.679180°), grid step size 5m; Contaminant type: Dimethyl methylphosphonate (inert substance, no sedimentation / reaction).

[0034] ② Calculation process S1: All verification parameters meet the requirements and are not missing; S2: Pollution source UTM coordinates 499850.20m, 4392500.50m (50N), grid point (115.905000°, 39.680700°) UTM coordinates 499855.10m, 4392505.30m; S3: Class D stability, t=60s, σ_y=28m, σ_z=18m, σ_x=42m; S4: d = √[(499855.10−499850.20)²+(4392505.30−4392500.50)²] = 6.86m, α = 45.5°, Δα = 45.5°−90° = −44.5° (-0.777rad), x = 6.86×cos(−0.777) = 4.87m, y = 6.86×sin(−0.777) = −4.80m; Substituting into the formula, we get C = 0.043μg / m³; S5: Single-point source with no superposition, C_total=0.043μg / m³; S6: Outputs a table, black scatter plots, and a JSON file.

[0035] ③ Result verification Concentration distribution: extends along the wind (eastward), symmetrical in the crosswind direction, with the peak area 10-50m eastward (approximately 120μg / m³), which conforms to physical laws; Accuracy: Compared with the HJ2.2-2018 standard model, the error is 15%, which meets the requirements.

[0036] refer to Figure 3-4 Example 2: Instantaneous Multi-Point Source Diffusion Calculation ① Input parameters Pollution source parameters: There are 4 point sources. The parameters of the main point source are the same as in Example 1: eastward point source (100m east of the main point source, 100g), northeastward point source (80m northeast of the main point source, 100g), and northward point source (120m north of the main point source, 100g). Other parameters: Same as in Example 1.

[0037] ② Calculation process S1-S2: The UTM coordinates of the four point sources are (499850.20, 4392500.50). (499950.20, 4392500.50), (499906.07, 4392556.07), (499850.20, 4392620.50); S3: 4 point sources σ_y=28m, distance between the eastward point source and the main point source 100m<2×(28+28)=112m, f_ij=0.1×(1−100 / 112)=0.011, σ'_y = 28 × 1.011 = 28.31 m; S4: The concentration contributions of the grid point (115.905500°, 39.680800°) and the four point sources are 1.86, 1.52, 1.28, and 0.95 μg / m³, respectively. S5: C_total=1.86+1.52+1.28+0.95=5.61μg / m³; S6: Output results, scatter plot shows the "large cluster area" of multiple sources superimposed.

[0038] ③ Result verification Superposition effect: The concentration in the multi-source overlap region (280 μg / m³) is higher than the single-source peak value (120 μg / m³), which is consistent with physical laws; Efficiency: Calculation time for 7200 grid points is 5.2 seconds, which meets emergency needs.

[0039] The advantages of this invention are as follows: 1. Application Prospects: (1) Environmental emergency response Scenario: Chemical storage tank leaks and explosions can simulate the pollution spread range and peak concentration within 5 minutes, providing decision support for personnel evacuation (such as identifying high-risk areas above 50μg / m³) and emergency response (such as sealing off the source location); Target users: emergency management departments and environmental monitoring agencies, with an estimated coverage rate of 80% for units at the municipal level and above nationwide.

[0040] (2) Industrial park planning Scenario: Optimize the layout of pollution sources in industrial parks, simulate the pollution risk of different point source distances (e.g., 50m / 100m), and avoid high concentration areas caused by the superposition of multiple sources; Target users: Industrial park management committees and planning and design units, with an estimated coverage rate of 60% for key industrial parks in China (approximately 1,500).

[0041] (3) Environmental impact assessment Scenario: Environmental impact assessment for construction projects (such as chemical plants and oil depots), replacing traditional simplified models and improving the scientific rigor of environmental impact assessment reports; Target users: Environmental assessment agencies and environmental approval departments. It is estimated that the usage rate of domestic environmental assessment agencies (approximately 2,000) will reach 70%.

[0042] 2. Economic Benefit Forecast: (1) Direct economic benefits Software sales: Based on a price of 50,000 yuan per software package (including annual maintenance), it is estimated that 500 packages will be sold within 3 years, generating direct revenue of 25 million yuan; Customized services: Providing customized models for large industrial parks (such as those incorporating localized terrain data), with a single project costing 200,000 to 500,000 yuan. It is estimated that 30 projects will be completed within 3 years, generating 9 million yuan in revenue.

[0043] (2) Indirect economic benefits Reduced emergency response costs: Reduced casualties and property damage from sudden pollution incidents, with a reduction of 1 million yuan per incident, resulting in 50 fewer incidents nationwide each year and an annual indirect benefit of 50 million yuan. Benefits of planning optimization: Avoiding the later rectification costs caused by unreasonable layout of pollution sources (rectification cost of about 10 million yuan per park), reducing the number of parks requiring rectification by 30 within 3 years, and generating a benefit of 300 million yuan.

Claims

1. A method for multi-scenario dispersion computation of a chemical pollution source, characterized in that: The method comprises the following steps: (1) Parameter input and verification: Input the pollution source parameters, meteorological parameters, calculation region parameters and pollutant types; perform numerical range, data type and logical consistency verification on the input parameters, fill in default values when non-critical parameters are missing, and prompt correction when parameters are abnormal; (2) Coordinate conversion and caching: Convert the latitude and longitude of each point source and the grid points of the calculation region into UTM plane coordinates. If the calculation region crosses the UTM zone, the region is divided into multiple UTM zones for processing. Cache the UTM coordinates of all point sources to avoid repeated conversion and calculation; (3) Diffusion parameter calculation: Simplify the diffusion parameter table, combine the time t>0s after emission and the atmospheric stability, and obtain the horizontal wind direction diffusion parameter σ_y,i and the vertical diffusion parameter σ_z,i of each point source through linear interpolation; the downwind diffusion parameter σ_x,i=1.5×σ_y,i; If the distance between point sources d_ij<2×(σ_y,i+σ_y,j), introduce the turbulence interference factor f_ij=0.1×(1−d_ij / (2×(σ_y,i+σ_y,j))), modify the diffusion parameters σ’_y,i=σ_y,i×(1+f_ij), σ’_y,j=σ_y,j×(1+f_ij); If the input dynamic meteorological time series data is input, calculate the diffusion parameters of each period; (4) Single-source concentration calculation: For each point source i, calculate the UTM relative distance d=√[(x_utm,calc−x_utm,source,i)²+(y_utm,calc−y_utm,source,i)²] and the azimuth angle α=arctan2(x_utm,calc−x_utm,source,i,y_utm,calc−y_utm,source,i)×180 / π between the grid point and the point source i; determine the airflow direction according to the wind direction (wind direction + 180°), calculate Δα=α−airflow direction; the downwind distance x_i of the grid point relative to the point source i is d×cos(Δα), and the horizontal wind direction distance y_i is d×sin(Δα); substitute Q_i, σ_x,i, σ_y,i (or σ’_y,i), σ_z,i, x_i, y_i, u, t into the Gaussian instantaneous point source concentration formula to calculate the concentration contribution C_i(x,y,t) of the point source i at the grid point, the formula is:

2. (5) Multi-source concentration superposition: Sum the concentration contributions of each point source at the same grid point to obtain the total concentration C_total(x,y,t)=Σ(i=1 to n)C_i(x,y,t) of the grid point; (6) Result output: Output the calculation results in the form of data table, scatter plot and JSON file.

3. The method of claim 1, wherein: In step (1), the pollution source parameters include the number of point sources, the latitude and longitude of each point source, and the total emission of each point source. The meteorological parameters include average wind speed, wind direction, atmospheric stability and optional dynamic meteorological time series data.

4. The method of claim 1, wherein: In step (2), the UTM coordinate conversion uses WGS84 ellipsoid parameters, and the plane coordinate error is less than 0.1m.

5. The method of claim 1, wherein: In step (3), the diffusion parameter table of the simplified version of the "Technical Guidelines for Environmental Impact Assessment of Atmospheric Environment" is used as the basis.

6. The method according to claim 1 or 4, wherein: In step (3), if there are terrain obstacles in the calculation area, the DEM terrain height data is introduced to correct the diffusion parameters: σ'_y,i=σ_y,i×(1+k×(h−z) / H), wherein h is the height of the obstacle, z is the height of the grid point, H is the height of the pollution source, k=0.3~0.5 on the leeward side, and k=-0.2~-0.3 on the windward side.

7. The method of claim 1, wherein: In step (4), if the pollutant has sedimentation characteristics, the settling velocity v_s is introduced to correct the vertical direction index term to exp[−(z−v_st)² / (2σ_z,i²)]; if the pollutant has chemical reaction characteristics, the reaction rate constant k_chem is introduced to multiply the concentration contribution C_i by the decay factor exp(−k_chemt).

8. The method of claim 1, wherein: In step (6), the JSON file supports import into GIS software for further spatial analysis.

9. A chemical pollution source multi-scenario dispersion computing system, characterized by: The system includes a parameter input module, a coordinate conversion module, a diffusion parameter calculation module, a concentration calculation and superposition module, and a result output module, each of which is used to execute steps (1)-(6) of the chemical pollution source multi-scenario diffusion calculation method of claims 1-7.

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