Dynamic wind field forecast driven atmospheric pollution contribution assessment method and device

Abstract

The invention discloses a dynamic wind field forecast-driven atmospheric pollution contribution assessment method and device, and belongs to the technical field of urban low-altitude economic environmental meteorological safeguard, and the method comprises the steps: obtaining mesoscale background meteorological input data of a target region and a subcity-level resolution ground surface aerodynamic impedance map; generating a sub-city scale static near-surface foundation wind field based on the impedance map; background meteorological data and the static wind field are coupled and updated, and a near-surface dynamic change wind field and a pollutant concentration field with minute resolution and hectometer scale are obtained; carrying out data assimilation by combining meteorological and pollutant concentration observation data of a target area to obtain a three-dimensional wind field analysis field; and simulating pollutant smoke plume space distribution based on the analysis field, and quantitatively calculating pollutant conveying flux and setting concentration contribution of a pollution source to a specified receptor point location. According to the method, the precision and timeliness of pollution influence assessment are improved, and the problems of inaccurate assessment and response lag in a complex city pattern in the existing method are solved.

Description

Technical Field

[0001] This invention relates to a method and apparatus for assessing the contribution of atmospheric pollution driven by dynamic wind field forecasting, belonging to the field of environmental meteorological support technology for urban low-altitude economy. Background Technology

[0002] With the continuous acceleration of urbanization and rapid economic development, the amount of pollutants emitted from industrial production, transportation and residential life has also increased, leading to increasingly serious urban near-surface air pollution problems.

[0003] Currently, many pollution control methods use mesoscale air quality models, which have low resolution and struggle to accurately simulate the spatial heterogeneity inherent in the complex topography and building layouts of cities. This makes it difficult to accurately assess the distribution of horizontal wind speed and pollutant concentrations in specific areas at a 100-meter scale. Meanwhile, while high-precision computational fluid dynamics (CFD) and large eddy simulation (LES) methods offer high accuracy, their computational costs are extremely high and time-consuming, making them unsuitable for large-scale, real-time pollution impact assessments and early warning forecasts, and hindering the provision of rapid and effective decision support in emergency situations. Furthermore, existing methods often fail to identify critical pollutant transport pathways and cannot accurately track and analyze the pollution contributions of different sources to the observed area, affecting the targetedness and timeliness of pollution control measures and failing to meet the needs of practical applications.

[0004] In the prior art, there are already methods capable of generating near-surface static baseline wind fields at sub-urban scales. For example, Chinese Patent Publication No. CN120335059A (Application No.: 202510512504.X) discloses a method and system for refined forecasting of urban low-altitude dynamic wind fields and air quality. This method simulates near-surface wind fields by constructing a surface aerodynamic impedance map, providing a technical basis for this invention. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and device for assessing the contribution of air pollution driven by dynamic wind field forecasting, which solves the problems that existing pollution prevention and control methods have poor accuracy and efficiency in assessing pollution impact, and poor targeting and timeliness in pollution prevention and control.

[0006] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0007] In a first aspect, the present invention provides a method for assessing the contribution of air pollution driven by dynamic wind field forecasting, comprising:

[0008] Acquire hourly resolution mesoscale background meteorological input data for the target area, as well as a sub-city resolution surface aerodynamic impedance map of the target area;

[0009] A sub-city-scale static near-surface basic wind field is generated based on the aforementioned surface aerodynamic impedance map;

[0010] The mesoscale background meteorological input data is coupled with the static near-surface basic wind field and dynamically updated to obtain the near-surface dynamic wind field and pollutant concentration field with minute resolution and 100-meter scale in the target area;

[0011] Meteorological and pollutant concentration observation data of the target area are acquired, and the data are assimilated by combining the dynamically changing wind field and pollutant concentration field to obtain the assimilated three-dimensional wind field analysis field.

[0012] Based on the assimilated three-dimensional wind field analysis field, the spatial distribution of pollutant plumes in the target area is simulated, and the pollutant transport flux and the contribution of pollutant concentration from a given pollution source to a specified receptor site are quantitatively calculated.

[0013] Furthermore, acquiring hourly resolution mesoscale background meteorological input data for the target area includes:

[0014] Under real-world meteorological scenarios, background wind speed, wind direction, and atmospheric pollutant transport concentrations for the target area are obtained from mesoscale meteorological and atmospheric chemistry forecasting models.

[0015] The average wind direction and speed and the average pollutant concentration of the upwind grid at each time point in the target area are extracted and calculated to form mesoscale background meteorological input data.

[0016] Furthermore, the method for obtaining a sub-city-level resolution surface aerodynamic impedance map of the target area includes:

[0017] The geographic information data of the target area is obtained, including water body distribution, urban canopy parameters, building distribution, vegetation cover, topographic elevation, and latitude and longitude information of emission sources and locations.

[0018] A three-dimensional geographic map of the target area is constructed based on the geographic information data, and an initial aerodynamic impedance map is generated based on the three-dimensional geographic map.

[0019] Add a buffer zone around the target area in the initial aerodynamic impedance map;

[0020] Calculate the average aerodynamic impedance value of all grid points within the target area, and set this average value as the aerodynamic impedance value of the buffer zone. The calculation formula is as follows:

[0021] ;

[0022] in, This represents the average aerodynamic impedance value of all grid points within the target area simulation range, where m represents the total number of rows of grid points, n represents the total number of columns of grid points, and m×n represents the total number of grid points. Indicates the first line, number The aerodynamic impedance values ​​of the grid points, where the row index is... The range of values ​​for is 1≤ ≤m, column index The range of values ​​for is 1≤ ≤n;

[0023] By combining the aerodynamic impedance values ​​of the target area and the buffer zone, a sub-city-level resolution surface aerodynamic impedance map with buffer zones is generated.

[0024] Furthermore, the coupling and dynamic updating of the mesoscale background meteorological input data with the static near-surface baseline wind field includes:

[0025] Obtain the background wind speed from the mesoscale background meteorological input data;

[0026] Based on calm wind conditions and historical wind speeds, an attenuation coefficient is used. The background wind speed is corrected to obtain the corrected wind speed. The correction is based on the following formula: ;

[0027] in, This is the corrected wind speed; For a moment wind speed, For a moment wind speed;

[0028] The continuous wind direction in the mesoscale background meteorological input data is discretized into a finite number of wind field types, and a mapping relationship between wind direction and wind field type is established:

[0029] ;

[0030] in, This is the wind field type index, where N is the total number of preset wind field types. This refers to the actual wind direction and angle. This is a rounding function;

[0031] Interpolate the static near-surface basic wind field to a target grid resolution that matches the target region;

[0032] The propagation speed of wind field changes is calculated based on the corrected wind speed, and a time label for wind field switching is determined for each grid point;

[0033] Based on the aforementioned time stamp, within the transitional region of wind field change, the old and new wind fields are gradually mixed, and the mixing formula is as follows:

[0034] ;

[0035] in, For the mixed wind field, This is the wind field after the wind field changes have been completed. The original wind field before the wind field change. β is the mixing weighting factor (dimensionless), representing the relative contribution of the new and old wind fields to the mixed wind field, with a value range of 0-1; t represents the current time. The moment when the wind field change reaches the current grid point. The preset time required for a complete transition;

[0036] By combining the aforementioned surface aerodynamic impedance map, the interpolated and adapted static near-surface basic wind field, and the pre-acquired upper-air observation data, a three-dimensional spatial wind field is generated using the vertical logarithmic profile principle and height interpolation method.

[0037] Furthermore, the data assimilation, which combines the dynamically changing wind field and pollutant concentration field, yields an assimilated three-dimensional wind field analysis field, including:

[0038] Acquire real-time radar circumferential scanning observation data;

[0039] The radar ring scan real-time observation data is spatially interpolated to generate a high-space point wind field consistent with the simulation grid;

[0040] The features of the near-surface aerodynamic impedance map are fused with the wind field at the high-space points layer by layer. The influence weight of the near-surface impedance value on the wind field is gradually increased from high to low in the vertical direction, and finally a three-dimensional wind field analysis field is formed after assimilation.

[0041] Furthermore, the quantitative calculation of pollutant transport flux includes:

[0042] Identify the designated pollution sources to be assessed and their emission intensity within the target area;

[0043] Based on the assimilated three-dimensional wind field analysis field, the diffusion of pollutants from the set pollution source is simulated to obtain the pollutant concentration field;

[0044] Calculate the pollutant transport flux through the boundary of the specified area. The formula is as follows:

[0045] ;

[0046] in, It is a two-dimensional planar target region, where (i,j) is a region belonging to. All grid points within, It represents the pollutant concentration at grid point (i,j); yes Wind speed vector at grid points It is perpendicular to The unit normal vector of the region's surface. This represents the dot product of the wind speed vector and the unit normal vector. yes The area of ​​a grid point.

[0047] Furthermore, the method for calculating the contribution of the pollution source to the pollutant concentration at the designated receptor site includes:

[0048] The emission intensity of the pollution sources to be evaluated within the target area is defined as unit intensity.

[0049] Based on the assimilated three-dimensional wind field analysis field, a simulation was performed to calculate the simulated pollutant concentration at the specified receptor site under the given intensity. and the concentration of pollutants near the designated pollution source. ;

[0050] Calculate the concentration contribution coefficient of the specified pollution source to the designated receptor site. The formula is: .

[0051] Secondly, the present invention provides an air pollution contribution assessment device driven by dynamic wind field forecasting, used to implement the air pollution contribution assessment method driven by dynamic wind field forecasting as described in any one of the preceding claims, comprising:

[0052] The acquisition module is used to acquire hourly resolution mesoscale background meteorological input data of the target area, as well as a sub-city resolution surface aerodynamic impedance map of the target area.

[0053] The generation module is used to generate a sub-city-scale static near-surface basic wind field based on the surface aerodynamic impedance map.

[0054] The coupling and dynamic update module is used to couple and dynamically update the mesoscale background meteorological input data with the static near-surface basic wind field to obtain the near-surface dynamic wind field and pollutant concentration field with minute resolution and hexagonal scale in the target area.

[0055] The assimilation module is used to acquire meteorological and pollutant concentration observation data of the target area, and assimilate the data by combining the dynamically changing wind field and pollutant concentration field to obtain the assimilated three-dimensional wind field analysis field.

[0056] The evaluation module is used to simulate the spatial distribution of pollutant plumes in the target area based on the assimilated three-dimensional wind field analysis field, and to quantitatively calculate the pollutant transport flux and the contribution of the pollutant source to the pollutant concentration at the specified receptor site.

[0057] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0058] Fourthly, the present invention provides an electronic device, comprising:

[0059] Memory, used to store computer programs / instructions;

[0060] A processor for executing the computer program / instructions to implement the steps of any of the methods described above.

[0061] Fifthly, the present invention provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of any of the methods described above.

[0062] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0063] 1. This invention provides a method and apparatus for assessing the contribution of atmospheric pollution driven by dynamic wind field forecasting. By coupling with mesoscale environmental meteorological forecasts under specific meteorological scenarios or actual meteorological scenarios, it can dynamically simulate the spatiotemporal diffusion process of near-surface pollutants in real time, achieving minute-level resolution and accuracy at the hundred-meter level. Compared with traditional static assessment methods, this method not only improves the rapid and accurate prediction of pollutant diffusion paths, but also allows for observation of pollution diffusion in conjunction with actual weather conditions, providing real-time early warning services and offering more timely and scientific decision-making basis for urban environmental management.

[0064] 2. This invention can accurately identify the pollution impact area of ​​specific emission sources in a city and assess the pollution contribution of specific emission sources in the target area to specific locations, providing scientific advice for relevant ambient air quality governance and control, so as to implement corresponding pollution prevention and control methods. Compared with traditional methods, this application has higher accuracy in the quantitative assessment of pollution transport paths and pollution source contributions under complex urban surface patterns, significantly improving the pertinence and real-time nature of pollution prevention and control;

[0065] 3. This invention provides a universal solution for generating refined aerodynamic impedance maps for complex urban morphologies, which can improve the accuracy of low-altitude wind field simulation and further optimize the implementation of pollution prevention and control strategies. Compared with existing low-resolution models, this invention can better reflect the impact of urban microstructure on airflow when predicting the spatial distribution of pollutant diffusion and transport, thus having greater practical value in pollution control. Attached Figure Description

[0066] Figure 1 This is a flowchart illustrating the atmospheric pollution contribution assessment method driven by dynamic wind field forecasting according to an embodiment of the present invention.

[0067] Figure 2 This is a schematic diagram of a three-dimensional geographic map of a target area provided in an embodiment of the present invention;

[0068] Figure 3 This is a three-dimensional wind field diagram generated by data assimilation provided in an embodiment of the present invention.

[0069] Figure 4 This is a schematic diagram of the atmospheric pollutant concentration distribution map at near-surface sub-urban scale resolution under a complex urban terrain pattern provided by an embodiment of the present invention.

[0070] Figure 5 This is a schematic diagram illustrating the concentration contribution of a pollution source to a specific location according to an embodiment of the present invention.

[0071] Figure 6 This is a schematic diagram of the spatial distribution of ozone concentration field in Nanjing at different times, provided by an embodiment of the present invention.

[0072] Figure 7 This is a schematic diagram of pollutant concentration and wind speed sequences provided in an embodiment of the present invention;

[0073] Figure 8 This is a schematic diagram illustrating the generation of a surface aerodynamic impedance map of a target area at sub-city level resolution, provided by an embodiment of the present invention.

[0074] Figure 9 This is a schematic diagram of a surface aerodynamic impedance map of a target area with different buffer impedance values ​​at sub-city level resolution, provided by an embodiment of the present invention.

[0075] Figure 10 This is a target area wind speed color-filled map generated from a sub-city-level resolution surface aerodynamic impedance map of the target area, provided by an embodiment of the present invention. Detailed Implementation

[0076] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0077] Example 1: This example introduces a method for assessing the contribution of air pollution driven by dynamic wind field forecasting, including:

[0078] Acquire hourly resolution mesoscale background meteorological input data for the target area, as well as a sub-city resolution surface aerodynamic impedance map of the target area;

[0079] A sub-city-scale static near-surface basic wind field is generated based on the aforementioned surface aerodynamic impedance map;

[0080] The mesoscale background meteorological input data is coupled with the static near-surface basic wind field and dynamically updated to obtain the near-surface dynamic wind field and pollutant concentration field with minute resolution and 100-meter scale in the target area;

[0081] Meteorological and pollutant concentration observation data of the target area are acquired, and the data are assimilated by combining the dynamically changing wind field and pollutant concentration field to obtain the assimilated three-dimensional wind field analysis field.

[0082] Based on the assimilated three-dimensional wind field analysis field, the spatial distribution of pollutant plumes in the target area is simulated, and the pollutant transport flux and the contribution of pollutant concentration from a given pollution source to a specified receptor site are quantitatively calculated.

[0083] like Figure 1 As shown in this embodiment, the method for assessing the contribution of air pollution driven by dynamic wind field forecasting involves the following steps in its application:

[0084] Step 1: Obtain geographic information and emission source information for the target area.

[0085] like Figure 2 As shown, the geographic information data of the target area is obtained, including: water body distribution information, building distribution information, normalized vegetation index information, topographic elevation information, and urban canopy parameter information;

[0086] Obtain emission source information within the target area, including: latitude and longitude information of specific emission sources, and spatiotemporal distribution information of emission intensity.

[0087] Step 2: Obtain a sub-city-level resolution surface aerodynamic impedance map of the target area, and generate a sub-city-level near-surface static basic wind field using a conventional sub-city-scale near-surface static basic wind field generation method in this field.

[0088] In the step of "Obtaining a sub-city-level resolution surface aerodynamic impedance map of the target area", a buffer is added: based on the terrain complexity of the target area and the computing power requirements, a certain range is expanded around the target area, and the average value of the impedance values ​​of all grid points in the target area is taken as the aerodynamic impedance value of this range, thus obtaining an aerodynamic impedance map with a buffer. This improves the computational stability and well-posedness of the simulation scheme in the subsequent simulation process (that is, the numerical calculation results are closer to the real physical image, and the equations in the simulation process have a unique stable solution), thereby improving the accuracy of low-altitude wind field simulation and the universality of simulation under complex urban terrain.

[0089] Calculate the average aerodynamic impedance value of all grid points within the target area, and set this average value as the aerodynamic impedance value of the buffer zone. The calculation formula is as follows:

[0090] ;

[0091] in, This represents the average aerodynamic impedance value of all grid points within the target area simulation range, where m represents the total number of rows of grid points, n represents the total number of columns of grid points, and m×n represents the total number of grid points. Indicates the first line, number The aerodynamic impedance values ​​of the grid points, where the row index is... The range of values ​​for is 1≤ ≤m, column index The range of values ​​for is 1≤ ≤n.

[0092] Step 3: Obtain the mesoscale environmental meteorological forecast results for the target area under specific meteorological scenarios or real-time meteorological scenarios at hourly resolution. The mesoscale environmental meteorological forecast results include relevant meteorological elements such as wind speed, wind direction, and pollutant concentration at hourly resolution.

[0093] The acquisition of mesoscale environmental meteorological forecast results under the real-time meteorological scenario includes: obtaining the background wind speed, wind direction, and transported concentration of air pollutants in the target area for the next 1-7 days from the mesoscale meteorological and atmospheric chemistry forecast model (in this embodiment, it comes from the online coupled numerical model of atmospheric chemistry-meteorology, i.e., the WRF-Chem model); and obtaining the mesoscale background meteorological input file of the target area under the real-time meteorological scenario by extracting and calculating the average wind direction and wind speed and the average pollutant concentration of the upwind grid at each time in the target area.

[0094] The acquisition of mesoscale environmental meteorological forecast results for a specific meteorological scenario includes: customizing the background wind direction and speed of the upwind grid at each time. In this embodiment, a northerly wind with a wind speed of 2 m / s is used for 2 hours to obtain the mesoscale background meteorological input file for the specific meteorological scenario.

[0095] Step 4: Using the mesoscale environmental meteorological forecast results as background meteorological input data, a low-altitude wind field dynamic propagation simulation method based on the Lagrange diffusion idea is adopted to couple the static near-surface basic wind field at the sub-city scale to achieve dynamic updates, thereby obtaining the non-uniform dynamic changes of the near-surface wind field and pollutant concentration field at minute resolution and hundred-meter scale in the target area.

[0096] The wind field dynamic propagation simulation method based on the Lagrange diffusion concept includes: constructing a wind field type classification system, introducing a wind speed attenuation correction mechanism, establishing a wind field interpolation mapping relationship, calculating the propagation speed of wind field changes based on wind speed, determining the dynamic label of the wind field switching time at each grid point, realizing the gradual mixing and switching of old and new wind fields based on the time label, and expanding the two-dimensional planar wind field into a three-dimensional spatial wind field vertical expansion, thereby realizing the gradual spatial propagation simulation of wind field changes.

[0097] The construction of the wind field type classification system includes using a wind direction angle discretization method to divide continuous wind directions into a finite number of wind field types and establishing a mapping relationship between wind direction and wind field type:

[0098] ;

[0099] in, For wind field type index, This represents the total number of wind field types (16 in this example). This refers to the actual wind direction angle (0-360°). This is a rounding function. The discretization method divides the continuous wind direction space into 16 equally spaced sectors (the northerly wind direction angle ranges from 348.75 to 11.25°, and so on), with each sector corresponding to a characteristic wind field pattern.

[0100] The introduced wind speed attenuation correction mechanism includes, based on the physical characteristics of the atmospheric boundary layer, the wind speed correction function introduced in this invention:

[0101] ;

[0102] in: The attenuation coefficient is used to control the intrusion intensity of wind shear; in this embodiment, it is set to 0.5 based on local measured data. The conditional judgment implements a wind speed maintenance mechanism under calm conditions. Attenuation coefficient This demonstrates the energy dissipation process of wind energy transferring from high-altitude to low-altitude environments.

[0103] The establishment of the wind field interpolation mapping relationship includes: using a two-dimensional spline interpolation algorithm to uniformly interpolate the basic wind field data to the target grid resolution of the model, thereby achieving wind field spatial adaptation and solving the inconsistency between the basic wind field data and the model calculation grid in terms of spatial resolution and number of grid points.

[0104] The calculation of the propagation speed of wind field changes based on wind speed includes: the propagation speed of wind field changes is proportional to the background wind speed.

[0105] ;

[0106] in, For the propagation speed of wind field changes, Background wind speed, This is the propagation efficiency coefficient.

[0107] The propagation of wind field changes in space follows the advection diffusion principle under the Lagrange framework, but this embodiment innovatively discretizes the continuous propagation process into a switching process of wind field types.

[0108] The dynamic label for determining the wind field switching time of each grid point includes: introducing a time label matrix and a dynamic switching function, and gradually replacing the wind field type during the propagation process by setting a time label for wind field switching for each grid node.

[0109] The time tag matrix is ​​used to record the time or duration of wind field changes reaching the current node, and is combined with a dynamic switching function to update the wind speed component field, thereby realizing the propagation process of wind field type from the source region to the surrounding region.

[0110] The time-stamped transition between old and new wind fields includes a linear mixing method used in the transition region:

[0111] ;

[0112] in, For the mixed wind field, This is the wind field after the wind field changes have been completed. The original wind field before the wind field change. β is the mixing weighting factor (dimensionless), representing the relative contribution of the new and old wind fields to the mixed wind field, with a value range of 0-1; t represents the current time. The moment when the wind field change reaches the current grid point. The preset time required for a complete transition.

[0113] The vertical extension of the two-dimensional planar wind field into a three-dimensional spatial wind field includes: based on the near-surface aerodynamic impedance map of the target area, the sub-city-scale static near-surface basic wind field, and observation data such as radar ring scan and radiosonde, the vertical logarithmic profile principle and height interpolation method are used to generate a high-space point wind field consistent with the model grid by spatial interpolation of the observation data, so that the upper-level wind field is spatially represented as a relatively uniform background wind field.

[0114] In the vertical direction, the influence weight of near-ground aerodynamic impedance is gradually reduced from high to low, and the non-uniform wind field near the ground and the uniform wind field at high altitude are merged layer by layer to form a three-dimensional wind field that changes continuously in the vertical direction. Finally, a three-dimensional wind field is obtained in which the spatial difference of the wind field is smaller and the uniformity is stronger at higher altitudes, and the spatial difference of the wind field is greater and the non-uniformity is more significant closer to the ground.

[0115] Step 5: Acquire meteorological and pollutant concentration observation data of the target area, perform data assimilation, and thus achieve accurate simulation and evaluation of the spatial distribution of pollutant plumes, pollutant transport flux, and the concentration contribution of set pollution sources to specific locations in the target area.

[0116] The acquisition of meteorological and pollutant concentration observation data in the target area includes: acquiring ground observation data, radiosonde observation data, and remote sensing observation data from meteorological satellites and radar in the target area.

[0117] The data assimilation process includes: generating a relatively uniform gridded wind field at altitudes above 500 meters, consistent with the simulation grid, based on real-time radar ring scan observation data through spatial interpolation; furthermore, combining the near-surface aerodynamic impedance map and the near-surface wind field, performing layer-by-layer interpolation and fusion of the wind field in descending order of altitude, and gradually increasing the influence weight of the near-surface impedance value on the lower-level wind field. This ultimately forms a vertically continuous three-dimensional wind field, where the upper-level wind field is consistent with the radar observation altitude, and the near-surface wind field fully reflects the characteristics of the near-surface aerodynamic impedance map. The resulting analytical field, balancing model forecasting and observational accuracy, serves as the initial field for the numerical model, thereby effectively improving the numerical model's ability to simulate and forecast wind fields.

[0118] Figure 3 The image is a schematic diagram of a three-dimensional wind field generated by data assimilation according to an embodiment of the present invention. The image shows the three-dimensional horizontal wind field results based on radar data: the distribution of wind speed and direction at different times and heights. Figure 3 The left figures show the three-layer horizontal wind vector fields after radar interpolation, corresponding to the high, middle and low altitude layers (layers 19, 12 and 7). The horizontal axis represents latitude and longitude, the vertical axis represents altitude, and different colors represent wind speed. Figure 3The right image shows a fine-scale wind field with a resolution of 200m, corresponding to the three-layer height in the left image, and the wind speed is distinguished by color. Figure 3 (a) Figure 3 (b) Figure 3 (c) represents the three-dimensional wind field at different times, with the times corresponding to 04:54, 05:00, and 05:06 in sequence.

[0119] The method for achieving accurate simulation and evaluation of the spatial distribution of pollutant plumes, pollutant transport flux, and the concentration contribution of a given pollution source to a specific location in a target area includes:

[0120] Identify specific emission sources in the target area and simulate the near-surface diffusion process of pollutants emitted from them to obtain the spatial distribution of pollutant plumes and pollutant transport flux within the area.

[0121] The pollutant transport flux The expression is:

[0122] ;

[0123] in, It is a two-dimensional planar target region, where (i,j) is a region belonging to. All grid points within, It represents the pollutant concentration at grid point (i,j); yes Wind speed vector at grid points It is perpendicular to The unit normal vector of the region's surface. This represents the dot product of the wind speed vector and the unit normal vector. yes The area of ​​a grid point.

[0124] The concentration contribution of the designated pollution source to a specific location includes: evaluating the concentration contribution coefficient of the designated pollution source to the specific location when the source emission of the designated pollution source in the target area is at a unit intensity.

[0125] The concentration contribution coefficient is:

[0126] ;

[0127] in It is the concentration contribution coefficient of a pollution source at a specific location within the target area. It simulates pollutant concentrations at specific points within the target area. It refers to the concentration of pollutants near a specific emission source.

[0128] In this case, a 43km x 43km area was selected as the target area. Geographic data of this target area was acquired using drones and remote sensing data. A 3D geographic map was generated based on this geographic data, as shown below. Figure 2 As shown.

[0129] The forecast results of the non-uniform dynamic changes in the near-surface wind field and pollutant concentration field of the target area obtained by the rapid simulation and early warning system contained in this technology are as follows: Figure 4 As shown, the red box indicates a magnified detail: the direction of the arrow indicates the wind direction (as shown in the magnified detail within the red box), the size of the arrow indicates the wind speed in m / s, and the color scale represents the logarithm of the pollutant concentration to the base 10. Figure 4 A schematic diagram of the pollution concentration field of designated pollution sources in the target area at the 2nd, 4th, 6th, and 10th hours, with wind direction NE and wind speed 1m / s.

[0130] The forecast results obtained by analyzing the pollution source emission intensity within the target area as a unit intensity are as follows: the pollution contribution of the pollution source to a specific location in the target area is as follows: Figure 5 As shown: This Figure 5 This is a pollution contribution coefficient rose diagram representing the impact of a specific emission source on different specific locations under different wind directions and speeds. In the polar coordinates, the angle and radius represent wind direction and wind speed (unit: m / s), respectively, and the color scale represents the pollution contribution coefficient. Figure 5 (a) and (b) represent two different monitoring points: the Fifth Bureau Compound (31.3541°N, 119.817°E) and Yiyuan (31.3683°N, 119.7888°E).

[0131] Furthermore, this technology has been commercialized for forecasting. In this embodiment, the dynamic changes in the near-surface non-uniform wind field and pollutant concentration field at minute resolution and 100-meter scale in the target area have been generated as product data and uploaded to the forecast release platform website. The platform pushes early warning information to users in need based on threshold rules and trend recognition results, realizing pollution process analysis and interactive services.

[0132] like Figure 6 , 7 The image shown is a schematic diagram of the air quality forecasting platform website provided according to this embodiment. Figure 6 This is a schematic diagram of the spatial distribution of ozone concentration field in Nanjing at different times, taking Nanjing as an example. Figure 7 This is a schematic diagram of pollutant concentration and wind speed sequences. This embodiment has been successfully implemented and verified in Nanjing and Yixing areas, and has established an interactive environmental meteorological forecast website publishing capability. Taking the illustrated platform interface as an example, the platform supports switching between real-time monitoring and forecast results by time, and provides a display of pollutant indicators and meteorological elements; among which, Figure 6The monitoring module on the right side of the central display platform can show real-time air quality levels, air quality index, and concentrations of major pollutants (including PM2.5). 2.5 PM 10 (The map displays the spatial distribution of pollutants, including nitrogen dioxide, sulfur dioxide, carbon monoxide, and ozone). The left side shows a map overlaid with near-surface wind fields and pollutant spatial distribution results, providing a visual representation of the direction of pollution transport and its impact range.

[0133] Figure 6 The color scale indicates the ozone concentration, the arrow indicates the wind direction, and the arrow color indicates the wind speed. Figure 6 (a) Figure 6 (b) Figure 6 (c) The spatial distribution of ozone and wind field conditions at 15:30, 17:00 and 19:00 on March 8, 2026 are shown respectively.

[0134] Figure 7 (a) is the time series of wind speed forecasts from the grid. Figure 7 (b) shows the time series of ozone concentration forecasts from the grid. The horizontal axis represents time, and the vertical axes represent wind speed (m / s) and ozone concentration (μg / m³), respectively. 3 ).

[0135] Through the above-mentioned coupled simulation and platform release, this embodiment can refine the background weather field provided by the mesoscale forecast into minute-level and hundred-meter-level dynamic wind field and pollutant concentration field outputs that meet the needs of refined early warning at the county scale, thereby improving the spatiotemporal resolution and operational availability of pollution transport identification, cross-border impact judgment and emergency decision support.

[0136] In a further embodiment, this embodiment also provides a universal scheme for generating high-precision low-altitude wind field simulations for complex urban morphologies, including:

[0137] Step 1: Acquire topographic and feature data of the target area and generate a surface aerodynamic impedance map of the target area at sub-city level resolution.

[0138] Acquire topographic and feature data for the target area, including: topographic elevation, building outlines and heights, land use types, normalized difference vegetation index (NDVI), and water body distribution, such as... Figure 8 As shown.

[0139] Step 2: Add a buffer zone around the generated sub-city-level resolution surface aerodynamic impedance map of the target area, determine the impedance value and range of the buffer zone, and improve the accuracy of low-altitude wind field simulation.

[0140] The impedance value of the buffer is determined based on experience and set as the average value of the impedance value of the target area.

[0141] The range of the buffer zone is determined by taking 50% of the length and width of the target area based on experience.

[0142] In specific implementation, this embodiment selects the same target area as in embodiment 1, and obtains topographic and feature data of the target area through geographic remote sensing and reading GIS (Geographic Information System) information, including topographic elevation, building outline and building height, land use type, normalized vegetation index and other factors that affect near-ground ventilation resistance.

[0143] The obtained topographic and feature data were overlaid and analyzed using ArcGIS software (a multi-source geographic information system) to obtain a sub-city-level resolution surface aerodynamic impedance map of the target area. MATLAB software was then used to add a buffer zone around the target area. Based on experience, the impedance value of the buffer zone was set to the average impedance value of the target area, and the size of the buffer zone was set to 50% of the length and width of the target area, resulting in an aerodynamic impedance map with buffer zones. Figure 9 , 10 As shown.

[0144] like Figure 9 As shown, this is a sub-city-level resolution surface aerodynamic impedance map provided by an embodiment of the present invention when the buffer impedance values ​​are 0.1, 0.2, 0.5 and the average value of the core area impedance, respectively.

[0145] like Figure 10 As shown, this is a color-coded map of core area wind speed with a northeasterly background wind direction, generated from surface aerodynamic impedance maps at sub-city resolution with different buffer impedance values, provided by an embodiment of the present invention. The color scales represent wind speed magnitudes. The average core area wind speeds corresponding to buffer impedance values ​​of 0.1, 0.2, 0.5, and the average core area impedance value are 0.279 m / s, 0.450 m / s, 0.636 m / s, and 1.012 m / s, respectively. The results indicate that the simulated wind field is sensitive to the buffer impedance value. Specifically, when the buffer impedance value is taken as the average impedance value of the target area, the simulated average wind speed in the core area is closer to the set background wind speed value. This may suggest that the buffer impedance value should be approximately close to the average impedance value of the core area; a value that is too small will affect the accuracy of the simulated wind field results and result in excessively low wind speeds.

[0146] This embodiment, by coupling with mesoscale environmental meteorological forecast results under specific meteorological scenarios or actual meteorological scenarios, can dynamically simulate the spatiotemporal diffusion process of pollutants in real time. Compared with traditional static assessment methods, this method not only improves the speed and accuracy of predicting pollutant diffusion paths, but also allows for observation of pollution diffusion in conjunction with actual weather conditions, providing real-time early warning services and offering more timely and scientific decision-making basis for urban environmental management.

[0147] This embodiment, by combining the intensity of atmospheric pollutant emission sources and the horizontal wind field distribution at the sub-urban scale, can accurately identify the pollution impact area of ​​specific emission sources in a city, and assess pollutant transport flux and the pollution contribution of specific emission sources in the target area to specific locations. This provides scientific advice for relevant ambient air quality governance and control, enabling the implementation of corresponding pollution prevention and control methods. Compared to traditional methods, this application has higher accuracy in the quantitative assessment of pollution transport paths and pollution source contributions under complex urban surface patterns, significantly improving the targeting and real-time nature of pollution prevention and control.

[0148] This embodiment, through the generation of a generalized scheme for aerodynamic impedance maps of complex urban morphologies, can improve the accuracy of low-altitude wind field simulation and further optimize the implementation of pollution prevention and control strategies. Compared with existing low-resolution models, this invention can better reflect the impact of urban microstructure on airflow when predicting the spatial distribution of pollutant diffusion and transport, thus having greater practical value in pollution control.

[0149] Example 2: This example provides an atmospheric pollution contribution assessment device driven by dynamic wind field forecasting, comprising:

[0150] The acquisition module is used to acquire hourly resolution mesoscale background meteorological input data of the target area, as well as a sub-city resolution surface aerodynamic impedance map of the target area.

[0151] The generation module is used to generate a sub-city-scale static near-surface basic wind field based on the surface aerodynamic impedance map.

[0152] The coupling and dynamic update module is used to couple and dynamically update the mesoscale background meteorological input data with the static near-surface basic wind field to obtain the near-surface dynamic wind field and pollutant concentration field with minute resolution and hexagonal scale in the target area.

[0153] The assimilation module is used to acquire meteorological and pollutant concentration observation data of the target area, and assimilate the data by combining the dynamically changing wind field and pollutant concentration field to obtain the assimilated three-dimensional wind field analysis field.

[0154] The evaluation module is used to simulate the spatial distribution of pollutant plumes in the target area based on the assimilated three-dimensional wind field analysis field, and to quantitatively calculate the pollutant transport flux and the contribution of the pollutant source to the pollutant concentration at the specified receptor site.

[0155] The specific functions of each module described above are explained in the relevant content of the method in Embodiment 1, and will not be repeated here.

[0156] Example 3: This example provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described in Example 1.

[0157] Example 4: This example provides an electronic device, including:

[0158] Memory, used to store computer programs / instructions;

[0159] A processor for executing the computer program / instructions to implement the steps of any of the methods described in Embodiment 1.

[0160] Example 5: This example provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the method described in any one of Examples 1.

[0161] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

[0162] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0163] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0164] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0165] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0166] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure and not to limit its protection scope. Although this disclosure has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading this disclosure, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the pending claims.

Claims

1. A method for assessing the contribution of air pollution driven by dynamic wind field forecasting, characterized in that, include: Acquire hourly resolution mesoscale background meteorological input data for the target area, as well as a sub-city resolution surface aerodynamic impedance map of the target area; A sub-city-scale static near-surface basic wind field is generated based on the aforementioned surface aerodynamic impedance map; The mesoscale background meteorological input data is coupled with the static near-surface basic wind field and dynamically updated to obtain the near-surface dynamic wind field and pollutant concentration field with minute resolution and 100-meter scale in the target area; Meteorological and pollutant concentration observation data of the target area are acquired, and the data are assimilated by combining the dynamically changing wind field and pollutant concentration field to obtain the assimilated three-dimensional wind field analysis field. Based on the assimilated three-dimensional wind field analysis field, the spatial distribution of pollutant plumes in the target area is simulated, and the pollutant transport flux and the contribution of pollutant concentration from a given pollution source to a specified receptor site are quantitatively calculated.

2. The method for assessing the contribution of air pollution driven by dynamic wind field forecasting according to claim 1, characterized in that, The acquisition of hourly resolution mesoscale background meteorological input data for the target area includes: Under real-world meteorological scenarios, background wind speed, wind direction, and atmospheric pollutant transport concentrations for the target area are obtained from mesoscale meteorological and atmospheric chemistry forecasting models. The average wind direction and speed and the average pollutant concentration of the upwind grid at each time point in the target area are extracted and calculated to form mesoscale background meteorological input data.

3. The method for assessing the contribution of air pollution driven by dynamic wind field forecasting according to claim 1, characterized in that, The method for obtaining a sub-city-level resolution surface aerodynamic impedance map of the target area includes: The geographic information data of the target area is obtained, including water body distribution, urban canopy parameters, building distribution, vegetation cover, topographic elevation, and latitude and longitude information of emission sources and locations. A three-dimensional geographic map of the target area is constructed based on the geographic information data, and an initial aerodynamic impedance map is generated based on the three-dimensional geographic map. Add a buffer zone around the target area in the initial aerodynamic impedance map; Calculate the average aerodynamic impedance value of all grid points within the target area, and set this average value as the aerodynamic impedance value of the buffer zone. The calculation formula is as follows: ； in, This represents the average aerodynamic impedance value of all grid points within the target area simulation range, where m represents the total number of rows of grid points, n represents the total number of columns of grid points, and m×n represents the total number of grid points. Indicates the first line, number The aerodynamic impedance values ​​of the grid points, where the row index is... The range of values ​​for is 1≤ ≤m, column index The range of values ​​for is 1≤ ≤n; By combining the aerodynamic impedance values ​​of the target area and the buffer zone, a sub-city-level resolution surface aerodynamic impedance map with buffer zones is generated.

4. The method for assessing the contribution of air pollution driven by dynamic wind field forecasting according to claim 1, characterized in that, The process of coupling and dynamically updating the mesoscale background meteorological input data with the static near-surface baseline wind field includes: Obtain the background wind speed from the mesoscale background meteorological input data; Based on calm wind conditions and historical wind speeds, an attenuation coefficient is used. The background wind speed is corrected to obtain the corrected wind speed. The correction is based on the following formula: ; in, This is the corrected wind speed; For a moment wind speed, For a moment wind speed; The continuous wind direction in the mesoscale background meteorological input data is discretized into a finite number of wind field types, and a mapping relationship between wind direction and wind field type is established: ； in, This is the wind field type index, where N is the total number of preset wind field types. This refers to the actual wind direction and angle. This is a rounding function; Interpolate the static near-surface basic wind field to a target grid resolution that matches the target region; The propagation speed of wind field changes is calculated based on the corrected wind speed, and a time label for wind field switching is determined for each grid point; Based on the aforementioned time stamp, within the transitional region of wind field change, the old and new wind fields are gradually mixed, and the mixing formula is as follows: ； in, For the mixed wind field, This is the wind field after the wind field changes have been completed. The original wind field before the wind field change; β is the mixing weighting factor, representing the relative contribution of the new and old wind fields to the mixed wind field, with a value range of 0-1; t represents the current time. The moment when the wind field change reaches the current grid point. The preset time required for a complete transition; By combining the aforementioned surface aerodynamic impedance map, the interpolated and adapted static near-surface basic wind field, and the pre-acquired upper-air observation data, a three-dimensional spatial wind field is generated using the vertical logarithmic profile principle and height interpolation method.

5. The method for assessing the contribution of air pollution driven by dynamic wind field forecasting according to claim 1, characterized in that, The data assimilation, combining the dynamically changing wind field and pollutant concentration field, yields an assimilated three-dimensional wind field analysis field, including: Acquire real-time radar circumferential scanning observation data; The radar ring scan real-time observation data is spatially interpolated to generate a high-space point wind field consistent with the simulation grid; The features of the near-surface aerodynamic impedance map are fused with the wind field at the high-space points layer by layer. The influence weight of the near-surface impedance value on the wind field is gradually increased from high to low in the vertical direction, and finally a three-dimensional wind field analysis field is formed after assimilation.

6. The method for assessing the contribution of air pollution driven by dynamic wind field forecasting according to claim 1, characterized in that, The quantitative calculation of pollutant transport flux includes: Identify the designated pollution sources to be assessed and their emission intensity within the target area; Based on the assimilated three-dimensional wind field analysis field, the diffusion of pollutants from the set pollution source is simulated to obtain the pollutant concentration field; Calculate the pollutant transport flux through the boundary of the specified area. The formula is as follows: ； in, It is a two-dimensional planar target region, where (i,j) is a region belonging to. All grid points within, It represents the pollutant concentration at grid point (i,j); yes Wind speed vector at grid points It is perpendicular to The unit normal vector of the region's surface. This represents the dot product of the wind speed vector and the unit normal vector. yes The area of ​​a grid point.

7. The method for assessing the contribution of air pollution driven by dynamic wind field forecasting according to claim 1, characterized in that, The method for calculating the contribution of a pollution source to the pollutant concentration at a specified receptor site includes: The emission intensity of the pollution sources to be evaluated within the target area is defined as unit intensity. Based on the assimilated three-dimensional wind field analysis field, a simulation was performed to calculate the simulated pollutant concentration at the specified receptor site under the given intensity. and the concentration of pollutants near the designated pollution source. ; Calculate the concentration contribution coefficient of the specified pollution source to the designated receptor site. The formula is: .

8. A device for assessing the contribution of air pollution driven by dynamic wind field forecasting, used to implement the method for assessing the contribution of air pollution driven by dynamic wind field forecasting as described in any one of claims 1-7, characterized in that, include: The acquisition module is used to acquire hourly resolution mesoscale background meteorological input data of the target area, as well as a sub-city resolution surface aerodynamic impedance map of the target area. The generation module is used to generate a sub-city-scale static near-surface basic wind field based on the surface aerodynamic impedance map. The coupling and dynamic update module is used to couple and dynamically update the mesoscale background meteorological input data with the static near-surface basic wind field to obtain the near-surface dynamic wind field and pollutant concentration field with minute resolution and hexagonal scale in the target area. The assimilation module is used to acquire meteorological and pollutant concentration observation data of the target area, and assimilate the data by combining the dynamically changing wind field and pollutant concentration field to obtain the assimilated three-dimensional wind field analysis field. The evaluation module is used to simulate the spatial distribution of pollutant plumes in the target area based on the assimilated three-dimensional wind field analysis field, and to quantitatively calculate the pollutant transport flux and the contribution of the pollutant source to the pollutant concentration at the specified receptor site.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When executed by a processor, the computer program implements the steps of the method described in any one of claims 1-7.

10. An electronic device, characterized in that, include: Memory, used to store computer programs / instructions; A processor for executing the computer program / instructions to implement the steps of the method according to any one of claims 1-7.

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