Response curved surface model construction method and device for atmospheric pollutant concentration analysis

Abstract

The invention discloses a response curved surface model construction method and device for atmospheric pollutant concentration analysis, and the method comprises the steps: carrying out the precursor emission simulation of a research region based on a Lagrange diffusion model, and generating precursor emission concentration data; based on the precursor emission concentration data, establishing a response relationship between the precursor concentration and the precursor emission change by using a statistical method; based on multi-scenario simulation of a three-dimensional air quality model, in combination with a polynomial response curved surface method, establishing a response relationship between atmospheric pollutant concentration and precursor concentration change; and coupling the response relationship of the precursor concentration to the precursor emission change with the response relationship of the atmospheric pollutant concentration to the precursor concentration change to obtain a response curved surface model of the atmospheric pollutant concentration to the precursor emission change. According to the method, the modeling cost and time of the response curved surface model can be reduced, and the influence of the small-scale region emission source change on the atmospheric pollutant concentration can be quickly quantified.

Description

Technical Field

[0001] The present invention relates to the field of atmospheric environment technology, and in particular to a response surface model construction method and device for atmospheric pollutant concentration analysis. Background Art

[0002] This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. No statement herein is admitted to be prior art by virtue of its inclusion in this section.

[0003] Air pollution is a major environmental problem. 2.5 As the main atmospheric pollutants, PM2.5 and O3 pose a huge threat to human health. In order to achieve continuous improvement in air quality, a series of atmospheric pollution reduction measures have been adopted to evaluate the impact of atmospheric pollution and atmospheric pollution reduction measures on PM2.5. 2.5 The impact of / O3 concentration is crucial for the formulation of pollution prevention and control measures. The three-dimensional air quality model can predict the PM2.5 in various regions under different emission scenarios. 2.5 / O3 concentration, but its calculation process is complex, computationally intensive and time-consuming, and has certain limitations when there are many emission scenarios. In order to efficiently evaluate the impact of massive emission reduction measures on air quality, existing studies have used statistical methods to simplify the three-dimensional air quality model (such as CMAQ, CAMx, WRF-Chem and other models) into a response surface model (RSM) based on multi-sampling scenario simulation. Compared with the three-dimensional air quality model, the response surface model can achieve PM2.5 reduction while saving more than 90% of the computing time and cost. 2.5 The prediction of the response of / O3 concentration to precursor emissions is an important tool to support the formulation of air pollution reduction plans.

[0004] The existing response surface model construction method uses deep learning technology to control the number of sampling scenarios of the three-dimensional air quality model to about twice the number of emission reduction targets. That is, two sampling scenarios are required to establish a response surface model for a target area, thereby achieving PM 2.5 / O3 concentrations respond quickly to changes in average emissions at large scales such as provincial / regional levels. However, when this method is applied to the construction of smaller-scale response surface models, it is necessary to produce two separate emission change scenarios for each small-scale area such as a park / facility / factory production process. There is a limitation that the number of three-dimensional air quality model scenarios reaches thousands or tens of thousands, which significantly increases the computing time and cost. Therefore, there is an urgent need to develop a new response surface model construction method to achieve a rapid response of atmospheric pollutant concentrations to changes in precursor emissions at small-scale regional levels such as parks / facility / production processes with low computing time and low computing cost, to solve the limitations of response surface models in fine-scale applications, and to meet the needs of air quality management for refined prevention and control. Summary of the Invention

[0005] An embodiment of the present invention provides a method for constructing a response surface model for analyzing atmospheric pollutant concentrations, which is used to reduce the cost and time of response surface modeling and quickly quantify the impact of changes in small-scale regional emission sources on atmospheric pollutant concentrations. The method includes:

[0006] Conducting precursor emission simulation in the study area based on a Lagrangian diffusion model to generate precursor emission concentration data; the precursor emission concentration data includes precursor concentrations after emission from different precursor emission sources;

[0007] Based on the precursor emission concentration data, a statistical method is used to establish the response relationship between the precursor concentration and the change of precursor emission;

[0008] Conducting a precursor emission scenario simulation for the study area based on a three-dimensional air quality model to generate scenario simulation result data; the scenario simulation result data includes the concentration of atmospheric pollutants after the emission of different precursors;

[0009] Based on the polynomial response surface method and using the scenario simulation results data, the response relationship between the concentration of atmospheric pollutants and the change of precursor concentration was established;

[0010] The response relationship of the precursor concentration to the change of the precursor emission is coupled with the response relationship of the atmospheric pollutant concentration to the change of the precursor concentration to obtain a response surface model of the atmospheric pollutant concentration to the change of the precursor emission.

[0011] An embodiment of the present invention further provides a response surface model construction device for atmospheric pollutant concentration analysis, the device comprising:

[0012] A diffusion model processing module is used to simulate precursor emissions in the study area based on a Lagrangian diffusion model to generate precursor emission concentration data; the precursor emission concentration data includes precursor concentrations after emission from different precursor emission sources; based on the precursor emission concentration data, a response relationship between precursor concentration and changes in precursor emissions is established using statistical methods;

[0013] A three-dimensional air quality model processing module is used to simulate precursor emission scenarios in a study area based on the three-dimensional air quality model and generate scenario simulation result data; the scenario simulation result data includes the concentration of atmospheric pollutants after the emission of different precursors; and based on the polynomial response surface method, the scenario simulation result data is used to establish a response relationship between the concentration of atmospheric pollutants and the change of the precursor concentration;

[0014] The coupling module is used to couple the response relationship of the precursor concentration to the change of the precursor emission with the response relationship of the atmospheric pollutant concentration to the change of the precursor concentration, so as to obtain a response surface model of the atmospheric pollutant concentration to the change of the precursor emission.

[0015] An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the response surface model construction method for atmospheric pollutant concentration analysis is implemented.

[0016] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the response surface model construction method for atmospheric pollutant concentration analysis is implemented.

[0017] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the above-mentioned response surface model construction method for atmospheric pollutant concentration analysis.

[0018] In the embodiment of the present invention, the Lagrangian diffusion model technology, the three-dimensional air quality model technology and the polynomial response surface technology are combined to establish a high-resolution response surface model. Compared with the technical solutions in the prior art, the embodiment of the present invention effectively reduces the direct application scope of the complex three-dimensional air quality model. Under the premise of ensuring the accuracy of the simulation, the computing time and resource cost required for the construction of the response surface model are greatly reduced, thereby significantly improving the modeling efficiency of the response surface model. The high-resolution response surface model in the embodiment of the present invention can quickly quantify the impact of changes in emission sources in small-scale areas such as parks, specific facilities, and specific production processes on PM 2.5 / O3 and other atmospheric pollutant concentrations. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0020] Figure 1 Schematic diagram of a process for constructing a response surface model for analyzing atmospheric pollutant concentrations in an embodiment of the present invention;

[0021] Figure 2 This is a specific example diagram of a method for constructing a response surface model for analyzing atmospheric pollutant concentrations in an embodiment of the present invention;

[0022] Figure 3 FIG2 is another specific example of a method for constructing a response surface model for analyzing atmospheric pollutant concentrations according to an embodiment of the present invention;

[0023] Figure 4 Schematic diagram of the process for establishing a response relationship between precursor concentration and precursor emission changes in an embodiment of the present invention;

[0024] Figure 5 FIG2 is another specific example of a method for constructing a response surface model for analyzing atmospheric pollutant concentrations according to an embodiment of the present invention;

[0025] Figure 6 Schematic diagram of a response surface model construction device for atmospheric pollutant concentration analysis in an embodiment of the present invention. DETAILED DESCRIPTION

[0026] To make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0027] In order to clearly describe the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, words such as "first" and "second" are used to distinguish between identical or similar items with basically the same functions and effects. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order.

[0028] Figure 1 FIG. 1 is a flow chart of a method for constructing a response surface model for analyzing atmospheric pollutant concentrations according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0029] Step 101: simulate precursor emissions in a study area based on a Lagrangian diffusion model to generate precursor emission concentration data; the precursor emission concentration data includes precursor concentrations after emission from different precursor emission sources;

[0030] Step 102: Based on the precursor emission concentration data, a response relationship between the precursor concentration and the precursor emission change is established using a statistical method;

[0031] Step 103: Perform precursor emission scenario simulation on the study area based on the three-dimensional air quality model to generate scenario simulation result data; the scenario simulation result data includes the concentration of atmospheric pollutants after the emission of different precursors;

[0032] Step 104: Based on the polynomial response surface method, using the scenario simulation result data, establish a response relationship between the concentration of atmospheric pollutants and the change of the precursor concentration;

[0033] Step 105 : Couple the response relationship of the precursor concentration to the change in precursor emission with the response relationship of the atmospheric pollutant concentration to the change in precursor concentration to obtain a response surface model of the atmospheric pollutant concentration to the change in precursor emission.

[0034] The embodiment of the present invention proposes a method for coupling Lagrangian diffusion model and three-dimensional air quality model technology to construct PM 2.5 / O3 A method for high-resolution response relationship of atmospheric pollutant concentration to precursor emissions. First, based on the Lagrangian diffusion model with low computational cost and short time consumption, a high-resolution "precursor concentration-precursor emission" response relationship was constructed using statistical methods; secondly, based on the three-dimensional air quality model, a low-resolution "atmospheric pollutant concentration-precursor concentration" response relationship was constructed using polynomial response surface technology. By coupling these two response relationships, a high-resolution response surface model of atmospheric pollutant concentration to precursor emission changes was constructed, which can realize the rapid response prediction of atmospheric pollutants to precursor emission reduction. This high-resolution response surface model of atmospheric pollutant concentration to precursor emission changes not only significantly reduces the cost and time of modeling, but also can quickly quantify the impact of changes in small-scale regional emission sources on atmospheric pollutant concentrations, thereby providing a scientific basis for precise pollution prevention and control strategies.

[0035] Step 101: simulate precursor emission in the study area based on the Lagrangian diffusion model to generate precursor emission concentration data.

[0036] During implementation, several precursor emission sampling scenarios were designed, and the Lagrangian diffusion model was used to simulate the precursor emission sources in each fine grid unit area within the study area to generate high-precision precursor concentration data sets under several emission scenarios.

[0037] The Lagrangian diffusion model in the embodiment of the present invention is suitable for simulating SO2, NO2 on complex terrain and small scale. X , a PM 2.5 , VOCs, NH3 and other atmospheric precursors, such as the physical processes of transmission, diffusion, and deposition.

[0038] In one embodiment, a precursor emission simulation is performed on a study area based on a Lagrangian diffusion model to generate precursor emission concentration data, which may include: performing a precursor emission simulation on each grid divided in the study area based on the Lagrangian diffusion model to generate a precursor emission concentration dataset; the precursor emission concentration dataset reflects the precursor concentration of each grid at a set time after only a single unit grid emits and other grids have no emissions.

[0039] Figure 2 FIG is a specific example of a method for constructing a response surface model for analyzing atmospheric pollutant concentrations in an embodiment of the present invention. Figure 2 The figure shows the specific implementation steps for generating precursor emission concentration data based on the Lagrangian diffusion model, including:

[0040] Step 201: setting the simulation domain and data preprocessing of the Lagrangian diffusion model;

[0041] In a preferred embodiment, the Lagrangian diffusion model includes a meteorological module and a Lagrangian-Gaussian puff module. The meteorological module utilizes terrain elevation data and land use data to generate a temporally and spatially varying meteorological field. The Lagrangian-Gaussian puff module utilizes the meteorological field generated by the meteorological module to advect puffs released from precursor emission sources, simulating the diffusion and transformation processes along the transport path.

[0042] This embodiment uses the grid meteorological field data output by the mesoscale numerical weather forecast model (Weather Research & Forecasting Model, WRF) as the initial guess field of the meteorological module in the diffusion model. The terrain elevation data used by the meteorological module is GEBCO data (General Bathymetric Chart of the Oceans, ocean relief map data), with an accuracy of 15 arc seconds (about 500m); the land use data is high-resolution data of the study area of a certain year with a resolution of 30m. The geodetic reference system selects the World Geodetic System 1984 (WGS-84), and the projection coordinate system is the Universal Transverse Mercator (UTM) grid system. The Lagrangian diffusion model needs to divide the study area into several grid areas in the horizontal direction and several layers in the vertical direction. This embodiment sets the grid resolution to 100m×100m, dividing the study area into several 100m×100m small areas. The vertical layer simulation range is from the ground to an altitude of 3000m, divided into 10 unequally spaced layers, with the top heights of each layer being 20, 40, 80, 160, 300, 600, 1000, 1500, 2200, and 3000m, respectively. It should be clarified that the simulation domain setting and input data of the Lagrangian diffusion model should be set according to the specific study area and the pollutant emission reduction of interest, and are not limited in this embodiment.

[0043] Step 202: Setting key parameters of the Lagrangian diffusion model;

[0044] The core module of the Lagrangian diffusion model in the embodiment of the present invention is the Lagrangian Gaussian puff module. The Lagrangian Gaussian puff model uses the temporally and spatially varying meteorological field generated by the meteorological module to horizontally transport the puffs released from the emission source and simulates its diffusion and transformation processes along the transport path. Depending on the input data, the Lagrangian Gaussian puff module provides different diffusion calculation options. In this embodiment, the default diffusion scheme is selected, and the micrometeorological parameters output by the meteorological module are used to calculate the horizontal and vertical diffusion parameters based on the similarity theory. The pollution source emission precursors in the Lagrangian Gaussian puff module include SO2, NO X , a PM 2.5 , VOCs and NH3. The dry and wet deposition modules of precursors were enabled in the simulation. The dry and wet deposition modules are mainly used to simulate and calculate the deposition process of pollutant precursors in the atmosphere.

[0045] Regarding the emission source settings for precursors, these include one or any combination of the following pollution source types: area source, point source, and line source. For example, mobile line sources, point sources such as power plants and industry, and area sources such as industrial process sources and agriculture, with time allocation coefficients set for different pollution source emission sectors in different months. It should be clarified that the key parameters of the Lagrangian diffusion model should be set based on the specific research area and the pollutants of interest for emission reduction, and this embodiment of the present invention does not impose any restrictions.

[0046] Step 203: Design several precursor emission sampling scenarios to generate a high-precision precursor emission concentration dataset.

[0047] Figure 3 FIG is another specific example of a method for constructing a response surface model for analyzing atmospheric pollutant concentrations according to an embodiment of the present invention. Figure 3 As shown in the figure, precursor emission simulation is performed on each grid divided in the study area based on the Lagrangian diffusion model to generate a precursor emission concentration dataset, which may include:

[0048] Step 301: Configure a first baseline scenario and multiple precursor emission zero scenarios; the first baseline scenario indicates that the emission source intensity in each grid area is the actual emission source intensity in the baseline year, and the precursor emission zero scenario indicates that each grid area and each emission source department are set to zero precursor emissions;

[0049] Step 302: Input the first baseline scenario into the Lagrangian diffusion model to calculate the spatiotemporal distribution of pollutant concentrations to obtain a first precursor concentration matrix; the first precursor concentration matrix includes the precursor concentrations of each grid region in the baseline year;

[0050] Step 303: Input each precursor placement zero scenario into the Lagrangian diffusion model to calculate the spatiotemporal distribution of pollutant concentrations, thereby obtaining a plurality of second precursor concentration matrices; the second precursor concentration matrices include the precursor concentrations of each grid corresponding to the precursor placement zero scenario;

[0051] Step 304 : forming a precursor emission concentration dataset based on the first precursor concentration matrix and the plurality of second precursor concentration matrices; the precursor emission concentration dataset includes concentration information of different emission sources, different regional locations, and different time resolutions.

[0052] In the embodiment of the present invention, different pollution source types require specific data formats and input requirements when inputting into the Lagrangian diffusion model. The embodiment of the present invention takes the sampling scenario of non-point source precursors as an example to explain the specific implementation method in detail.

[0053] The plurality of precursor emission sampling scenarios include a first baseline scenario and a plurality of precursor emission zero scenarios.

[0054] The first baseline scenario refers to a scenario where the emission source intensity for each grid cell is set to the actual emission source intensity in the base year, measured in tons / year / square kilometer. The baseline emission scenario is input into a Lagrangian diffusion model to calculate the spatiotemporal distribution of pollutant concentrations. This yields the simulated precursor concentration for each grid cell in the base year, which can be expressed as a precursor concentration matrix.

[0055] The multiple precursor emission zeroing scenarios are defined as scenarios for each emission source sector and each grid region. Given R non-point source emission source sectors, I grid rows, and J grid columns within the study area, the total number of precursor emission zeroing scenarios for each non-point source emission source sector is (R × I × J). The emission source intensity matrix for the precursor emission zeroing scenario is as follows:

[0056]

[0057] Where, E rn The emission source intensity matrix of the study area for the nth sampling scenario of the rth emission source sector; E_p ij It represents the emission source intensity of precursor p in the area where the grid in row i and column j is located, in tons / year / km2. When (i-1)×J+j=n, E_p ij = 1 ton / year / km2, while the emission source intensity of the precursor p in the remaining grid areas is 0; p represents the type of precursor. In the embodiment of the present invention, the precursor types include SO2, NO X , a PM 2.5 , VOCs and NH3.

[0058] The above precursors are placed in zero scenario and input into the Lagrangian diffusion model to calculate the spatiotemporal distribution of pollutant concentrations, and obtain several precursor concentration matrices, which are recorded as the second precursor concentration matrix. The second precursor concentration matrix is as follows:

[0059]

[0060] Where Q trnp Q represents the concentration matrix of the precursor p calculated by the Lagrangian diffusion model for the nth sampling zero scenario of the rth emission source department at time t; ij represents the simulated concentration of precursors in the area where the grid in row i and column j is located; p represents the type of precursor, including SO2, NO X , a PM 2.5 , VOCs and NH3.

[0061] Finally, through the post-processing program, the above precursor concentration matrix with hourly resolution is processed into precursor concentration matrices with different time resolutions, such as 24-hour average, monthly average and annual average.

[0062] Through the above steps, a dataset of precursor emission concentrations with kilometer-level resolution was generated in the study area, covering concentration information of different emission source departments, different regional locations, and different time resolutions. Based on this dataset, it is possible to quickly obtain the concentrations of SO2, NO2, and CO2 in each grid area at a certain time after a single grid emits and other grids have no emissions. X , a PM 2.5 , VOCs and NH3 concentrations, providing basic data support for the subsequent construction of a rapid response relationship between precursor concentrations and changes in precursor emissions.

[0063] Step 102: Based on the precursor emission concentration data, a response relationship between the precursor concentration and the change in the precursor emission is established using a statistical method.

[0064] During implementation, based on the high-precision precursor concentration data set generated by the Lagrangian diffusion model, statistical methods are used to establish a high-resolution response relationship between precursor concentration and changes in precursor emissions.

[0065] Figure 4 The figure is a flow chart illustrating the establishment of a response relationship between precursor concentration and precursor emission changes in an embodiment of the present invention. This embodiment takes the effect of annual precursor emission changes on the average annual precursor concentration as an example to detail the process of establishing a high-resolution rapid response relationship between precursor concentration and precursor emission changes. Specifically, the process includes:

[0066] Step 401: Select a linear regression model to establish a response relationship between precursor concentration and precursor emission changes;

[0067] The Lagrangian diffusion model focuses on the physical transport and diffusion of precursors, and does not or only rarely considers the chemical transformation between different precursors. Therefore, the present invention assumes that the relationship between precursor emissions and precursor concentration is essentially linear. The basic framework expression for the relationship between precursor concentration and precursor emissions is as follows:

[0068] Qreduce=K×Ereduce+M(Formula 2-1)

[0069] Where, the independent variable is Ereduce, which represents the change in precursor emissions; the dependent variable is Qreduce, which represents the change in precursor concentration; K and M are the regression model parameters to be confirmed.

[0070] To achieve a high-resolution response relationship between precursor concentration and changes in precursor emissions, it is necessary to establish a mathematical relationship between changes in precursor concentration at the receptor grid and changes in precursor emissions at the source grid within the study area. Based on atmospheric chemical transport theory, when precursor emissions at any source grid within the study area change, these changes will affect the precursor concentration distribution across the entire area through chemical reactions and physical transport processes. Therefore, changes in precursor concentration at a particular receptor grid are correlated not only with changes in its own emissions but also with changes in emissions from other source grids. The relationship between changes in precursor concentration at the receptor grid and changes in precursor emissions at the source grid can be modified based on Equation 2-1.

[0071] In one embodiment, based on the precursor emission concentration data, using statistical methods to establish a response relationship between the precursor concentration and the change in the precursor emission may include:

[0072] Based on the precursor emission concentration data, a statistical method was used to establish the response relationship between precursor concentration and changes in precursor emissions according to the following regression model:

[0073]

[0074] Among them, Qreduce ij Indicates the change in the precursor concentration in the area where the receptor grid in row i and column j is located; ab represents the change in precursor emissions in the area where the source grid in row a and column b is located; I represents the total number of grid rows in the study area, J represents the total number of grid columns in the study area, and the number of receptor grids and source grids is the same, both I×J; K and M are regression model parameters.

[0075] Step 402 : Based on the precursor concentration data sets generated by the several zero-sampling scenarios in step 101 , the parameter K of the linear regression model is determined.

[0076] Based on the precursor emission concentration dataset generated in step 101, we can directly obtain the average annual simulated concentration matrix of precursors for all receptor grids within the study area, assuming a single grid's source intensity of 1 ton / year / km² (assuming no emissions from other grids). That is, the response matrix of the single grid's emission intensity to the average annual precursor concentration for all receptor grids, obtained based on the precursor emission concentration dataset, is:

[0077]

[0078] Where, Qyear ab The matrix of the average annual concentration of precursors in all receptor grids corresponding to the source grid in row a and column b is 1 ton / year / km2. ab_ijIt represents the annual average concentration of the precursor in the receptor grid in row i and column j corresponding to the precursor emission source intensity of the region where the source grid in row a and column b is located, with the unit being micrograms per cubic meter.

[0079] The parameter K in the regression model means the change in precursor concentration in the region of the receptor grid in row i and column j caused by the change in precursor emission in the region of the source grid in row a and column b. Therefore, the parameter K is Qyear ab_ij , then the relationship between the change in the precursor concentration in the receptor grid and the change in the precursor emission in the source grid can be further quantified as:

[0080]

[0081] Where Qreduce ij Indicates the change in the precursor concentration in the area where the receptor grid in row i and column j is located; ab represents the change in precursor emissions in the area where the source grid in row a and column b is located; I represents the total number of grid rows in the study area, J represents the total number of grid columns in the study area, and the number of receptor grids and source grids is the same, both (I×J); K and M are regression model parameters to be confirmed.

[0082] Step 403 : Based on the first precursor concentration matrix generated in step 302 for the first baseline scenario, confirm the parameter M of the regression model.

[0083] The relationship between precursor emissions and precursor concentrations under the baseline scenario can be used to determine the parameter M of the regression model. ab When the precursor concentration change in the area where the receptor grid is located is the precursor concentration Qbase under the baseline scenario ij The specific expression is as follows:

[0084] Known:

[0085] Ereduce ab =Ebase ab (Formula 2-5)

[0086] Qreduce ij =Qbase ij (Formula 2-6)

[0087] Substituting Equation 2-5 and Equation 2-6 into Equation 2-4, we can obtain:

[0088]

[0089] Substituting Equation 2-7 into Equation 2-4, the relationship between the change in the concentration of the receptor grid precursor and the change in the emission of the source grid precursor can be further quantified as:

[0090]

[0091] Where Qreduce ij is the dependent variable, which represents the change in the precursor concentration in the area where the receptor grid in row i and column j is located; ab is the independent variable, indicating the change in precursor emissions in the region where the source grid in row a and column b is located; Qyear ab_ij , Ebase ab and Qbase ij All are constants, Qyear ab_ij Ebase represents the change in the average annual concentration of the precursor in the i-th row and j-th column of the receptor grid caused by the reduction of unit emissions of the precursor in the region where the source grid in the a-th row and b-th column is located. ab Indicates the base year emission of precursors in the region where the source grid in row a and column b is located; Qbase ij It represents the baseline concentration of the precursor in the area where the receptor grid in row i and column j is located; I represents the total number of grid rows in the study area, and J represents the total number of grid columns in the study area.

[0092] Step 404: Based on actual pollution prevention and control needs, a high-resolution response relationship between precursor concentration and precursor emission changes is constructed.

[0093] In a preferred embodiment, based on the precursor emission concentration data, a statistical method is used to establish a response relationship between the precursor concentration and the change in the precursor emission, including:

[0094] The change in output precursor concentration is predicted based on the precursor emission reduction ratio of the emission source sector according to the following formula:

[0095]

[0096] Where Qreduce p_ij is the dependent variable, which represents the predicted concentration change of the precursor p in the area where the receptor grid in row i and column j is located; K p_r_ab is an independent variable, indicating the emission reduction ratio of precursor p in the region where the source grid in row a and column b is located in the pollution prevention and control measures; Qyear p_r_ab_ij , Ebase p_r_ab and Qbase p_r_ij All are constants, Qyear p_r_ab_ij Ebase represents the change in the average annual concentration of the precursor in the i-th row and j-th column of the receptor grid caused by the reduction of unit emissions of the precursor p in the emission source sector r in the region where the source grid in the a-th row and b-column is located.p_r_ab Qbase represents the base year emissions of precursor p in the emission source sector r in the region where the source grid in row a and column b is located; p_r_ij represents the baseline concentration of precursor p for source sector r in the region where the receptor grid in row i and column j is located; R represents the total number of source sectors; I represents the total number of grid rows in the study area, and J represents the total number of grid columns in the study area. The number of source grids and receptor grids is the same. Given that the Lagrangian diffusion model requires relatively low computational resources, the Lagrangian diffusion model grid resolution setting in step 101 allows the study area to be infinitely subdivided to a certain extent, thereby achieving a high-resolution response relationship between changes in precursor concentrations at the receptor grids and changes in precursor emissions at the source grids.

[0097] The embodiment of the present invention constructs a response relationship between the change in the concentration of the target pollutant and the change in the emission of the precursor as shown in Formula 2-9. The Formula 2-9 is applicable to the analysis of the change in the concentration of the precursor under general circumstances. In addition, for special grids where nonlinear relationships may exist, such as highly polluted areas or complex terrain areas, local polynomial fitting or piecewise linear functions can be introduced on the basis of Formula 2-9, and the fitting parameters can be optimized by statistical methods such as the least squares method to minimize the error and ensure that the regression model established above can accurately reflect the impact of the change in precursor emissions on the concentration of the precursor. The response relationship between the high-resolution precursor concentration and the precursor emission constructed by the embodiment of the present invention can quickly predict the precursor concentration of each grid area in the study area when different emission reduction measures are taken.

[0098] Step 103: Conduct precursor emission scenario simulation for the study area based on the three-dimensional air quality model to generate scenario simulation result data.

[0099] In one embodiment, a precursor emission scenario is simulated for the study area based on a three-dimensional air quality model to generate scenario simulation result data, which may include: performing a precursor emission scenario simulation for each large-scale unit divided into the study area based on the three-dimensional air quality model to generate scenario simulation result data; the number of large-scale units is less than the number of grids.

[0100] During implementation, the study area is divided into H large-scale units, each of which is a “PM 2.5 / Response relationship of atmospheric pollutant concentration to changes in precursor concentration / The smallest unit of precursor emission change in the relationship. It is worth noting that in the polynomial response surface method, the smallest unit of precursor concentration change determines the number of scenarios simulated by the three-dimensional air quality model. Because three-dimensional air quality models describe in detail the physical and chemical evolution of atmospheric precursors, including atmospheric transport processes such as advection, turbulence, and convection, gas-phase chemical processes such as the nitrogen oxide cycle affecting O3 formation, aerosol chemical processes such as sulfur dioxide oxidation affecting sulfate formation, and physical processes such as dry and wet deposition, they are limited by high computational cost and time. Therefore, when dividing the smallest unit of precursor concentration change, it is necessary to balance computing resources and research needs. The total number of large-scale units after division should not be too large, and H is usually less than 50. The number of large-scale units is much smaller than the number of grids divided in the diffusion model.

[0101] In one embodiment, a precursor emission scenario simulation is performed on each large-scale unit divided into a study area based on a three-dimensional air quality model to generate scenario simulation result data, which may include:

[0102] Configuring precursor emission sampling scenarios; the precursor emission sampling scenarios include a second baseline scenario, a gaseous precursor emission control scenario, or multiple gaseous precursor emission zero scenarios; the second baseline scenario indicates that the precursor emission amount of each large-scale unit is the actual emission amount in the base year, the gaseous precursor emission control scenario is a control scenario in which the precursor emission amount of each large-scale unit is set to be several times the precursor emission amount of the second baseline scenario, and the gaseous precursor emission zero scenario is a scenario in which the precursor emission amount of each large-scale unit is set to zero;

[0103] The precursor emission sampling scenario is input into the three-dimensional air quality model to calculate the spatiotemporal distribution of pollutant concentrations and obtain scenario simulation result data.

[0104] Step 104: Based on the polynomial response surface method, using the scenario simulation result data, establish the response relationship between the concentration of atmospheric pollutants and the change of the precursor concentration.

[0105] During implementation, based on the polynomial response surface method, using the scenario simulation result data, establishing the response relationship of the atmospheric pollutant concentration to the change of the precursor concentration can include: based on the polynomial response surface method, using the scenario simulation result data, constructing a nonlinear polynomial expression of the atmospheric pollutant concentration and the precursor concentration, in which: the dependent variable is the atmospheric pollutant concentration in the area where each grid is located, and the independent variable is the change in multiple precursor concentrations in each large-scale unit; the nonlinear polynomial expression includes multiple polynomial coefficients to be determined; the polynomial coefficients to be determined are determined by a nonlinear regression fitting method or a deep learning method.

[0106] During implementation, a low-resolution PM2.5 model was constructed using a multi-scenario simulation of a three-dimensional air quality model and polynomial response surface technology. 2.5 / The response relationship between O3 concentration and precursor concentration changes.

[0107] PM 2.5 There is a highly nonlinear relationship between the concentration of / O3 and the concentration of precursors. 2.5 The mathematical abstract expression of the response relationship between / O3 concentration and precursor concentration change is as follows:

[0108] CONCreduce ij =

[0109] f(Qreduce_VOCs h ,Qreduce_PPM h ,Qreduce_NH3 h ,Qreduce_SO2 h ,Qreduce_NOX h )

[0110] (Formula 3-1)

[0111] Where: Qreduce_VOCs h 、Qreduce_PPM h 、Qreduce_NH3 h 、Qreduce_SO2 h 、Qreduce_NOX h are independent variables, representing the precursor VOCs, primary PM 2.5 , NH3, SO2 and NO X Concentration change; CONCreduce ij is the dependent variable, indicating the PM of the area where the grid in row i and column j is located. 2.5 / O3 predicted concentration change, i=1,2,...,I,j=1,2,...J,I represents the total number of grid rows in the study area, J represents the total number of grid columns in the study area, CONCreduce h is generated by Qreduce_VOCs h 、Qreduce_PPM h 、Qreduce_NH3 h 、Qreduce_SO2 h and Qreduce_NOX h The coefficients of the polynomial need to be determined by the multi-scenario simulation result data of the three-dimensional air quality model.

[0112] The coefficients of the polynomial in Formula 3-1 can be determined by two methods: nonlinear regression fitting method and deep learning method. You can choose one of the methods in the specific implementation process.

[0113] The specific implementation method of determining the polynomial coefficients by the nonlinear regression fitting method is as follows:

[0114] First, (23H+1) precursor emission sampling scenarios for the three-dimensional air quality model were designed (H represents the division of the study area into H large-scale units), including: 1 second baseline scenario, that is, the precursor emissions of each large-scale unit are the actual emissions in the baseline year; 20H gaseous precursor emission control scenarios, that is, using Hammersley sequence sampling to set 20 SO2, NO X The control scenario is that the emission of the four gaseous precursors, VOCs and NH3, is 0 to 2 times the emission of the second baseline scenario; H gaseous precursor emission zero scenarios, that is, the four gaseous precursors of each large-scale unit are set to 0; 2H primary PM 2.5 Control scenario, that is, set a PM for each large-scale unit 2.5 Emissions are 0 and PM 2.5 The emission is twice that of the second baseline scenario. The (23H+1) precursor emission sampling scenarios are input into the three-dimensional air quality model respectively to calculate the spatiotemporal distribution of pollutant concentrations, and the concentrations of different precursors in each large-scale unit and the PM concentrations in each fine grid area are obtained. 2.5 Finally, the multi-scenario dataset generated by the three-dimensional air quality model was used to fit the polynomial 3-1 to obtain the response equation coefficients in formula 3-1, thereby establishing a low-resolution PM 2.5 / The response relationship between O3 concentration and precursor concentration changes.

[0115] The specific implementation of the deep learning method to determine the polynomial coefficients is as follows:

[0116] First, (2H+3) precursor emission sampling scenarios were designed, including: 1 second baseline scenario, that is, the precursor emissions of each large-scale unit were the actual emissions in the base year; H+1 gaseous precursor emission zero scenario, that is, SO2, NO X , VOCs and NH3, the four gaseous precursors are all 0, and the four gaseous precursors of the entire study area are all 0; H+1 primary PM 2.5 The precursor row is placed in zero scenario, that is, each large-scale unit is set to a PM 2.5 Emissions are set to 0, and a PM 2.5The emission is 0. The above (2H+3) precursor emission sampling scenarios are input into the three-dimensional air quality model respectively, and the spatiotemporal distribution of pollutant concentration is calculated to obtain the PM of each large-scale unit. 2.5 / O3 concentration and “PM 2.5 Finally, the multi-scenario dataset was input into the existing polynomial response surface training model based on deep learning method to directly obtain the response equation coefficients in Equation 3-1, thereby establishing a low-resolution PM2.5 model in the study area. 2.5 / The response relationship between O3 concentration and precursor concentration changes.

[0117] Step 105 : Couple the response relationship of the precursor concentration to the change in precursor emission with the response relationship of the atmospheric pollutant concentration to the change in precursor concentration to obtain a response surface model of the atmospheric pollutant concentration to the change in precursor emission.

[0118] When implemented, the high-resolution response relationship between precursor concentration and precursor emission is combined with the low-resolution PM 2.5 The response relationship between / O3 concentration and precursor concentration is coupled to construct PM 2.5 A high-resolution response surface model of O / O concentration to changes in precursor emissions is used to screen refined precursor emission reduction strategies.

[0119] In one embodiment, the response relationship of the precursor concentration to the change of the precursor emission is coupled with the response relationship of the atmospheric pollutant concentration to the change of the precursor concentration to obtain a response surface model of the atmospheric pollutant concentration to the change of the precursor emission, including:

[0120] Replacing the precursor concentration change in the response relationship of the atmospheric pollutant concentration to the precursor concentration change with the precursor concentration in the response relationship of the precursor concentration to the precursor emission change to obtain the response relationship of the atmospheric pollutant concentration to the precursor emission change;

[0121] Based on the response relationship between atmospheric pollutant concentration and precursor emission changes, a response surface model of atmospheric pollutant concentration and precursor emission changes is formed.

[0122] The study area is divided into (I×J) fine grid areas in the Lagrangian diffusion model and into H large-scale units in the 3D air quality model. I×J is much larger than H, so each large-scale unit h includes several fine grid areas. The embodiment of the present invention assumes that within the same large-scale unit, the change in the concentration of the precursor has an impact on the PM 2.5The influence of / O3 concentration is consistent among all fine grid areas, that is, the response relationship of several fine grid areas within the same large-scale unit h follows the law of the average response of the large-scale unit h. Therefore, the PM of each grid area established in the three-dimensional air quality model processing is consistent with the influence of / O3 concentration on the fine grid areas. 2.5 The response of / O3 concentration to the change of precursor concentration in a large area unit h, equation 3-1, can be expanded to PM in each grid area. 2.5 The response relationship between the concentration of / O3 and the change of the precursor concentration in each grid area is shown in the equation 2-9. At the same time, the change of the precursor concentration in each grid area can be replaced by equation 2-9. Then, PM 2.5 The high-resolution response relationship of / O3 concentration to precursor emissions is shown below:

[0123]

[0124]

[0125] Where, CONCreduce ij is the dependent variable, which represents the PM in the area where the receptor grid in row i and column j is located. 2.5 / Change in O3 concentration; Qreduce_VOCs ij 、QPreduce_PPM ij 、Qreduce_NH3 ij 、Qreduce_SO2 ij and Qreduce_NOX ij Respectively represent the precursor VOCs, primary PM 2.5 , NH3, SO2 and NO X Concentration change; the change in precursor concentration is related to the change in precursor emissions, K_VOCs r_ab 、K_PPM r_ab 、K_NH3 r_ab 、K_SO2 r_ab and K_NOX r_ab They represent the VOCs precursors, primary PM2.5 and PM3 in the area where the source grid in row a and column b is located in the pollution prevention and control measures. 2.5 , NH3, SO2 and NO X Emission reduction ratio; Qyear_VOCs r_ab_ij 、Qyear_PPM r_ab_ij 、Qyear_NH3 r_ab_ij 、Qyear_SO2 r_ab_ij and Qyear_NOX r_ab_ij They represent the precursor VOCs, primary PM2.5 and VOCs in the emission source sector r in the region where the source grid in row a and column b is located. 2.5, NH3, SO2 and NO X The change in the average annual concentration of the precursor in the receptor grid in row i and column j caused by reducing unit emissions; Ebase_VOCs r_ab 、Ebase_PPM r_ab 、Ebase_NH3 r_ab 、Ebase_SO2 r_ab and Ebase_NOX r_ab f represents the precursor VOCs, primary PM2.5 and VOCs in the emission source sector r of the region where the source grid is located in the ath row and bth column. 2.5 , NH3, SO2 and NO X Base year emissions; Qbase_VOCs r_ij 、Qbase_PPM r_ij 、Qbase_NH3 r_ij 、Qbase_SO2 r_ij and Qbase_NOX r_ij They represent the precursor VOCs, primary PM2.5 and VOCs in the emission source sector r in the region where the source grid in row a and column b is located. 2.5 , NH3, SO2 and NO X The change in the average annual concentration of the precursor in the receptor grid in the i-th row and j-th column caused by the reduction of unit emissions; R represents the total number of emission source departments; I represents the total number of grid rows in the study area, and J represents the total number of grid columns in the study area. The number of source grids and receptor grids is the same. Given that the Lagrangian diffusion model has relatively low requirements for computing resources, the study area can be infinitely subdivided to a certain extent by setting the grid resolution of the Lagrangian diffusion model, thereby achieving a high-resolution response relationship between the change in the precursor concentration in the receptor grid and the change in the precursor emission in the source grid.

[0126] The above-mentioned "high-resolution PM 2.5 The "response relationship between O3 concentration and precursor emission changes" can abstractly express complex three-dimensional air quality models in mathematical form.

[0127] In one embodiment, after obtaining the response surface model of atmospheric pollutant concentration to precursor emission changes, the method of the embodiment of the present invention may also include: using the response surface model of atmospheric pollutant concentration to precursor emission changes to screen emission reduction measures whose air quality benefits exceed a set threshold from multiple emission reduction measures to be implemented.

[0128] To ensure the accuracy of the high-resolution response surface model, the embodiment of the present invention compares and verifies it with the simulation results of another three-dimensional air quality model, as follows:

[0129] This embodiment sets up 30 multi-grid emission sampling scenarios and 30 single-grid sampling scenarios for the verification of the high-resolution response surface model. The specific implementation methods of the 30 multi-grid emission reduction scenarios are as follows: First, the number of 30 groups of emission reduction grids is determined by a random sampling method, wherein the number of grids in each group ranges from 2 to the total number of grids in the study area. Among them, the emission reduction ratio of the randomly determined emission reduction grids is set by the Latin hypercube sampling method, specifically including: 10 emission reduction scenarios with an average emission rate of 0.5 and an emission rate variance of 0.4; 20 emission reduction scenarios with an average emission rate of 0.1 and an emission rate variance of 0.03. The specific implementation methods of the 30 single-grid emission reduction scenarios are as follows: First, 30 groups of different single emission reduction grids are determined by a random sampling method. Among them, the emission reduction ratio is set for the randomly determined emission reduction grid through the Latin hypercube sampling method, including: 10 emission reduction scenarios with an average emission rate of 0.5 and an emission rate variance of 0.4; 20 emission reduction scenarios with an average emission rate of 0.1 and an emission rate variance of 0.03.

[0130] By rationally designing the distribution characteristics of emission reduction scenarios through the above scientific sampling method, it is possible to effectively cover the combination patterns of different emission reduction intensities and different emission reduction areas. The above 30 multi-grid emission reduction scenarios and 30 single-grid emission reduction scenarios were input into the high-resolution response surface model and the three-dimensional air quality model for comparison, and the PM values of each grid area under the corresponding scenario were obtained. 2.5 / O3 simulated concentration.

[0131] The verification results show that the results of the high-resolution response surface model and the three-dimensional air quality model are close, indicating that the embodiment of the present invention successfully constructed the PM 2.5 A high-resolution prediction model for the response of O / O concentration to precursor emissions.

[0132] Figure 5 FIG is another specific example of a method for constructing a response surface model for analyzing atmospheric pollutant concentrations according to an embodiment of the present invention. Figure 5 As shown in FIG, using the response surface model of atmospheric pollutant concentration to precursor emission changes, the emission reduction measures whose air quality benefits exceed the set threshold are screened from multiple emission reduction measures to be implemented, which may include:

[0133] Step 501: Acquire emission reduction measures data to be implemented by multiple emission source departments;

[0134] Step 502: Calculate the precursor emission reduction ratio corresponding to each emission reduction measure for each emission source sector using the emission factor method;

[0135] Step 503: Input the precursor emission reduction ratio corresponding to each emission reduction measure of each emission source department into the response surface model of atmospheric pollutant concentration versus precursor emission change, and calculate and output the change in atmospheric pollutant concentration corresponding to each emission reduction measure;

[0136] Step 504: Based on the change in atmospheric pollutant concentration corresponding to each emission reduction measure, filter the emission reduction measures whose air quality benefits exceed the set threshold.

[0137] During implementation, based on the massive emission reduction measures provided by relevant enterprises, the emission factor method is used to calculate the atmospheric precursor emission reduction ratio corresponding to each emission reduction measure for each emission source department. The emission reduction ratio of each pollutant in each department is input into the response equation 4-1, which can quickly obtain the PM2.5 corresponding to the massive emission reduction measures. 2.5 / O3 concentration changes, thereby screening out emission reduction measures with the greatest air quality benefits. The following are some specific scenarios for application:

[0138] The method of the embodiment of the present invention can be used in the specific scenario of the elimination analysis of high-polluting enterprises in a city. First, the contribution ratio of each enterprise to the precursor emission in a city is calculated according to the emission factor method. Then, the high-resolution response surface model of the city is constructed using the method of the embodiment of the present invention, so as to quickly calculate the contribution ratio of each enterprise to PM. 2.5 Through this process, it is possible to identify the contribution of a city to PM 2.5 / O3 concentration has a significant impact on the main enterprises, and then clearly identify the high-polluting enterprises that need to be eliminated first in the prevention and control of air pollution.

[0139] The method of the embodiment of the present invention can be used in the specific scenario of green transformation analysis of the park. First, the contribution ratio of each production process to the precursor emission is calculated according to the emission factor method. Then, the high-resolution response surface model of the park is constructed using the method of the embodiment of the present invention, so as to quickly calculate the contribution ratio of each production process to PM in the park. 2.5 Through this process, the contribution of PM 2.5 / O3 concentration has a significant impact on the main production processes, thereby clarifying the key production links that require priority attention and improvement during the green transformation process.

[0140] The embodiments of the present invention have the following beneficial technical effects:

[0141] (1) The present invention combines Lagrangian diffusion modeling, three-dimensional air quality modeling, and polynomial response surface analysis to propose a novel high-resolution response surface model construction method. This method effectively reduces the direct application scope of complex three-dimensional air quality models and significantly reduces the computational time and resource costs required for response surface model construction while ensuring simulation accuracy, thereby significantly improving the modeling efficiency of response surface models.

[0142] (2) The embodiment of the present invention constructs a high-resolution PM 2.5 The response surface model of / O3 concentration to changes in precursor emissions can quickly quantify the impact of changes in precursor emissions in small-scale areas such as parks, specific facilities, and specific production processes on PM 2.5 This high-resolution response surface model provides a scientific basis for the formulation and implementation of refined pollution prevention and control measures such as "one city, one policy" and "one enterprise, one policy", contributing to more precise and effective air pollution control.

[0143] The present invention also provides a device for constructing a response surface model for analyzing atmospheric pollutant concentrations, as described in the following embodiments. Because the principles underlying the device are similar to those of the method for constructing a response surface model for analyzing atmospheric pollutant concentrations, the implementation of the device can be referenced to the implementation of the method for constructing a response surface model for analyzing atmospheric pollutant concentrations, and any repetitions will not be repeated.

[0144] Figure 6 Schematic diagram of a response surface model construction device for analyzing atmospheric pollutant concentrations according to an embodiment of the present invention. Figure 6 As shown, the apparatus 600 includes:

[0145] The diffusion model processing module 601 is used to simulate precursor emissions in the study area based on the Lagrangian diffusion model to generate precursor emission concentration data; the precursor emission concentration data includes the concentration of precursors after emission from different precursor emission sources; based on the precursor emission concentration data, a response relationship between the precursor concentration and the change in precursor emissions is established using statistical methods;

[0146] The three-dimensional air quality model processing module 602 is configured to perform a precursor emission scenario simulation for the study area based on the three-dimensional air quality model and generate scenario simulation result data; the scenario simulation result data includes the atmospheric pollutant concentrations after the emission of different precursors; and establish a response relationship between atmospheric pollutant concentrations and changes in precursor concentrations using the scenario simulation result data based on the polynomial response surface methodology.

[0147] The coupling module 603 is used to couple the response relationship of the precursor concentration to the precursor emission change with the response relationship of the atmospheric pollutant concentration to the precursor concentration change to obtain a response surface model of the atmospheric pollutant concentration to the precursor emission change.

[0148] In one embodiment, the diffusion model processing module 601 is specifically used to: perform precursor emission simulation on each grid divided in the study area based on the Lagrangian diffusion model to generate a precursor emission concentration data set; the precursor emission concentration data set reflects the precursor concentration of each grid at a set time after only a single unit grid emits and other grids have no emissions.

[0149] In one embodiment, the Lagrangian diffusion model includes a meteorological module and a Lagrangian Gaussian puff module;

[0150] The meteorological module is used to generate temporally and spatially varying meteorological fields using terrain elevation data and land use data;

[0151] The Lagrangian-Gaussian Puff Module is used to advect puffs released from precursor emission sources using the meteorological field generated by the Meteorological Module, simulating the diffusion and transformation processes along the transport path.

[0152] In one embodiment, the diffusion model processing module 601 is specifically configured to:

[0153] Configure a first baseline scenario and multiple precursor emission zero scenarios; the first baseline scenario indicates that the emission source intensity in each grid area is the actual emission source intensity in the base year, and the precursor emission zero scenario indicates that each grid area and each emission source department are set to zero precursor emissions;

[0154] The first baseline scenario is input into the Lagrangian diffusion model to calculate the spatiotemporal distribution of pollutant concentrations and obtain a first precursor concentration matrix; the first precursor concentration matrix includes the precursor concentrations in each grid area in the baseline year;

[0155] Inputting each precursor placement zero scenario into the Lagrangian diffusion model to calculate the spatiotemporal distribution of pollutant concentrations, thereby obtaining a plurality of second precursor concentration matrices; the second precursor concentration matrices include precursor concentrations of each grid corresponding to the precursor placement zero scenario;

[0156] A precursor emission concentration data set is formed based on the first precursor concentration matrix and the plurality of second precursor concentration matrices; the precursor emission concentration data set includes concentration information of different emission sources, different regional locations and different time resolutions.

[0157] In one embodiment, the diffusion model processing module 601 is specifically configured to:

[0158] Based on the precursor emission concentration data, a statistical method was used to establish the response relationship between precursor concentration and changes in precursor emissions according to the following regression model:

[0159]

[0160] Among them, Qreduce ij Indicates the change in the precursor concentration in the area where the receptor grid in row i and column j is located; ab represents the change in precursor emissions in the area where the source grid in row a and column b is located; I represents the total number of grid rows in the study area, J represents the total number of grid columns in the study area, and the number of receptor grids and source grids is the same, both I×J; K and M are regression model parameters.

[0161] In one embodiment, the regression model parameter M is determined using a first precursor concentration matrix, and the regression model parameter K is determined using a plurality of second precursor concentration matrices.

[0162] In one embodiment, the diffusion model processing module 601 is specifically configured to:

[0163] The change in output precursor concentration is predicted based on the precursor emission reduction ratio of the emission source sector according to the following formula:

[0164]

[0165] Where Qreduce p_ij is the dependent variable, which represents the predicted concentration change of the precursor p in the area where the receptor grid in row i and column j is located; K p_r_ab is an independent variable, indicating the emission reduction ratio of precursor p in the region where the source grid in row a and column b is located in the pollution prevention and control measures; Qyear p_r_ab_ij , Ebase p_r_ab and Qbase p_r_ij All are constants, Qyear p_r_ab_ij Ebase represents the change in the average annual concentration of the precursor in the i-th row and j-th column of the receptor grid caused by the reduction of unit emissions of the precursor p in the emission source sector r in the region where the source grid in the a-th row and b-column is located. p_r_ab Qbase represents the base year emissions of precursor p in the emission source sector r in the region where the source grid in row a and column b is located; p_r_ij It represents the baseline concentration of precursor p in the emission source sector r in the area where the receptor grid in the i-th row and j-th column is located; R represents the total number of emission source sectors; I represents the total number of grid rows in the study area, and J represents the total number of grid columns in the study area. The number of source grids and receptor grids is the same.

[0166] In one embodiment, the precursor emission source includes one or any combination of the following pollution source types:

[0167] Area source, point source, line source.

[0168] In one embodiment, the three-dimensional air quality model processing module 602 is specifically configured to:

[0169] Based on the three-dimensional air quality model, precursor emission scenarios are simulated for each large-scale unit divided into the study area to generate scenario simulation result data; the number of large-scale units is less than the number of grids.

[0170] In one embodiment, the three-dimensional air quality model processing module 602 is specifically configured to:

[0171] Configuring precursor emission sampling scenarios; the precursor emission sampling scenarios include a second baseline scenario, a gaseous precursor emission control scenario, or multiple gaseous precursor emission zero scenarios; the second baseline scenario indicates that the precursor emission amount of each large-scale unit is the actual emission amount in the base year, the gaseous precursor emission control scenario is a control scenario in which the precursor emission amount of each large-scale unit is set to be several times the precursor emission amount of the second baseline scenario, and the gaseous precursor emission zero scenario is a scenario in which the precursor emission amount of each large-scale unit is set to zero;

[0172] The precursor emission sampling scenario is input into the three-dimensional air quality model to calculate the spatiotemporal distribution of pollutant concentrations and obtain scenario simulation result data.

[0173] In one embodiment, the three-dimensional air quality model processing module 602 is specifically used to: based on the polynomial response surface method, use the scenario simulation result data to construct a nonlinear polynomial expression of the atmospheric pollutant concentration and the precursor concentration, in which: the dependent variable is the atmospheric pollutant concentration in the area where each grid is located, and the independent variable is the change in the concentration of multiple precursors in each large-scale unit; the nonlinear polynomial expression includes multiple polynomial coefficients to be determined; the polynomial coefficients to be determined are determined by a nonlinear regression fitting method or a deep learning method.

[0174] In one embodiment, the coupling module 603 is specifically configured to:

[0175] Replacing the precursor concentration change in the response relationship of the atmospheric pollutant concentration to the precursor concentration change with the precursor concentration in the response relationship of the precursor concentration to the precursor emission change to obtain the response relationship of the atmospheric pollutant concentration to the precursor emission change;

[0176] Based on the response relationship between atmospheric pollutant concentration and precursor emission changes, a response surface model of atmospheric pollutant concentration and precursor emission changes is formed.

[0177] In one embodiment, the apparatus 600 further includes:

[0178] The emission reduction measures screening and analysis module is used to use the response surface model of atmospheric pollutant concentration to precursor emission changes obtained in the coupling module 603 to screen emission reduction measures whose air quality benefits exceed a set threshold from multiple emission reduction measures to be implemented.

[0179] In one embodiment, the emission reduction measures screening and analysis module is specifically used to:

[0180] Obtain data on emission reduction measures to be implemented in multiple emission source sectors;

[0181] Calculate the precursor emission reduction ratio corresponding to each emission reduction measure for each emission source sector using the emission factor method;

[0182] Input the precursor emission reduction ratio corresponding to each emission reduction measure of each emission source department into the response surface model of atmospheric pollutant concentration to precursor emission change, and calculate and output the change in atmospheric pollutant concentration corresponding to each emission reduction measure;

[0183] Based on the change in atmospheric pollutant concentration corresponding to each emission reduction measure, emission reduction measures whose air quality benefits exceed the set threshold are screened.

[0184] An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the response surface model construction method for atmospheric pollutant concentration analysis is implemented.

[0185] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the response surface model construction method for atmospheric pollutant concentration analysis is implemented.

[0186] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the above-mentioned response surface model construction method for atmospheric pollutant concentration analysis.

[0187] In the embodiment of the present invention, the Lagrangian diffusion model technology, the three-dimensional air quality model technology and the polynomial response surface technology are combined to establish a high-resolution response surface model. Compared with the technical solutions in the prior art, the embodiment of the present invention effectively reduces the direct application scope of the complex three-dimensional air quality model. Under the premise of ensuring the accuracy of the simulation, the computing time and resource cost required for the construction of the response surface model are greatly reduced, thereby significantly improving the modeling efficiency of the response surface model. The high-resolution response surface model in the embodiment of the present invention can quickly quantify the impact of changes in emission sources in small-scale areas such as parks, specific facilities, and specific production processes on PM 2.5 / O3 and other atmospheric pollutant concentrations.

[0188] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0189] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0190] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0191] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0192] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A response surface model construction method for atmospheric pollutant concentration analysis, characterized in that: include: Based on the Lagrangian diffusion model, the precursor emission simulation was carried out in the study area to generate the precursor emission concentration data; The precursor emission concentration data includes the precursor concentration after emission from different precursor emission sources; Based on the precursor emission concentration data, a statistical method is used to establish the response relationship between the precursor concentration and the change of precursor emission; Conducting a precursor emission scenario simulation for the study area based on a three-dimensional air quality model to generate scenario simulation result data; the scenario simulation result data includes the concentration of atmospheric pollutants after the emission of different precursors; Based on the polynomial response surface method and using the scenario simulation results data, the response relationship between the concentration of atmospheric pollutants and the change of precursor concentration was established; The response relationship of the precursor concentration to the change of the precursor emission is coupled with the response relationship of the atmospheric pollutant concentration to the change of the precursor concentration to obtain a response surface model of the atmospheric pollutant concentration to the change of the precursor emission.

2. The method according to claim 1, wherein The precursor emission simulation of the study area is carried out based on the Lagrangian diffusion model to generate precursor emission concentration data, including: Based on the Lagrangian diffusion model, precursor emission simulation is performed on each grid divided in the study area to generate a precursor emission concentration dataset; the precursor emission concentration dataset reflects the precursor concentration of each grid at a set time after only a single unit grid emits and other grids have no emissions.

3. The method according to claim 2, wherein The Lagrangian diffusion model includes a meteorological module and a Lagrangian Gaussian puff module; The meteorological module is used to generate temporally and spatially varying meteorological fields using terrain elevation data and land use data; The Lagrangian-Gaussian Puff Module is used to advect puffs released from precursor emission sources using the meteorological field generated by the Meteorological Module, simulating the diffusion and transformation processes along the transport path.

4. The method according to claim 2, wherein Based on the Lagrangian diffusion model, precursor emission simulations were performed on each grid divided within the study area to generate a precursor emission concentration dataset, including: Configure a first baseline scenario and multiple precursor emission zero scenarios; the first baseline scenario indicates that the emission source intensity in each grid area is the actual emission source intensity in the base year, and the precursor emission zero scenario indicates that each grid area and each emission source department are set to zero precursor emissions; The first baseline scenario is input into the Lagrangian diffusion model to calculate the spatiotemporal distribution of pollutant concentrations and obtain a first precursor concentration matrix; the first precursor concentration matrix includes the precursor concentrations in each grid area in the baseline year; Inputting each precursor placement zero scenario into the Lagrangian diffusion model to calculate the spatiotemporal distribution of pollutant concentrations, thereby obtaining a plurality of second precursor concentration matrices; the second precursor concentration matrices include precursor concentrations of each grid corresponding to the precursor placement zero scenario; A precursor emission concentration data set is formed based on the first precursor concentration matrix and the plurality of second precursor concentration matrices; the precursor emission concentration data set includes concentration information of different emission sources, different regional locations and different time resolutions.

5. The method according to claim 4, wherein Based on the precursor emission concentration data, statistical methods are used to establish the response relationship between precursor concentration and changes in precursor emissions, including: Based on the precursor emission concentration data, a statistical method was used to establish the response relationship between precursor concentration and changes in precursor emissions according to the following regression model: Among them, Qreduce ij Indicates the change in the precursor concentration in the area where the receptor grid in row i and column j is located; ab represents the change in precursor emissions in the area where the source grid in row a and column b is located; I represents the total number of grid rows in the study area, J represents the total number of grid columns in the study area, and the number of receptor grids and source grids is the same, both I×J; K and M are regression model parameters.

6. The method according to claim 5, wherein The regression model parameter M is determined using the first precursor concentration matrix, and the regression model parameter K is determined using the plurality of second precursor concentration matrices.

7. The method according to claim 5, wherein Based on the precursor emission concentration data, statistical methods are used to establish the response relationship between precursor concentration and changes in precursor emissions, including: The change in output precursor concentration is predicted based on the precursor emission reduction ratio of the emission source sector according to the following formula: Where Qreduce p_ij is the dependent variable, which represents the predicted concentration change of the precursor p in the area where the receptor grid in row i and column j is located; K p_r_ab is an independent variable, indicating the emission reduction ratio of precursor p in the region where the source grid in row a and column b is located in the pollution prevention and control measures; Qyear p_r_ab_ij , Ebase p_r_ab and Qbase p_r_ij All are constants, Qyear p_r_ab_ij Ebase represents the change in the average annual concentration of the precursor in the i-th row and j-th column of the receptor grid caused by the reduction of unit emissions of the precursor p in the emission source sector r in the region where the source grid in the a-th row and b-column is located. p_r_ab Qbase represents the base year emissions of precursor p in the emission source sector r in the region where the source grid in row a and column b is located; p_r_ij It represents the baseline concentration of precursor p in the emission source sector r in the area where the receptor grid in the i-th row and j-th column is located; R represents the total number of emission source sectors; I represents the total number of grid rows in the study area, and J represents the total number of grid columns in the study area. The number of source grids and receptor grids is the same.

8. The method according to any one of claims 1 to 7, characterized in that: The precursor emission sources include one or any combination of the following pollution source types: Area source, point source, line source.

9. The method according to claim 7, wherein: Based on the three-dimensional air quality model, the precursor emission scenario of the study area is simulated to generate scenario simulation result data, including: Based on the three-dimensional air quality model, precursor emission scenarios are simulated for each large-scale unit divided into the study area to generate scenario simulation result data; the number of large-scale units is less than the number of grids.

10. The method according to claim 9, wherein Based on the three-dimensional air quality model, precursor emission scenarios are simulated for each large-scale unit divided into the study area, and scenario simulation result data is generated, including: Configuring precursor emission sampling scenarios; the precursor emission sampling scenarios include a second baseline scenario, a gaseous precursor emission control scenario, or multiple gaseous precursor emission zero scenarios; the second baseline scenario indicates that the precursor emission amount of each large-scale unit is the actual emission amount in the base year, the gaseous precursor emission control scenario is a control scenario in which the precursor emission amount of each large-scale unit is set to be several times the precursor emission amount of the second baseline scenario, and the gaseous precursor emission zero scenario is a scenario in which the precursor emission amount of each large-scale unit is set to zero; The precursor emission sampling scenario is input into the three-dimensional air quality model to calculate the spatiotemporal distribution of pollutant concentrations and obtain scenario simulation result data.

11. The method according to claim 10, wherein Based on the polynomial response surface methodology and using scenario simulation results, the response relationship between atmospheric pollutant concentrations and changes in precursor concentrations was established, including: Based on the polynomial response surface method and using the scenario simulation result data, a nonlinear polynomial expression of atmospheric pollutant concentration and precursor concentration is constructed. In the nonlinear polynomial expression: the dependent variable is the atmospheric pollutant concentration in the area where each grid is located, and the independent variable is the change in the concentration of multiple precursors in each large-scale unit; the nonlinear polynomial expression includes multiple polynomial coefficients to be determined; the polynomial coefficients to be determined are determined by nonlinear regression fitting method or deep learning method.

12. The method according to claim 1, wherein The response relationship of the precursor concentration to the change of the precursor emission is coupled with the response relationship of the atmospheric pollutant concentration to the change of the precursor concentration to obtain a response surface model of the atmospheric pollutant concentration to the change of the precursor emission, including: Replacing the precursor concentration change in the response relationship of the atmospheric pollutant concentration to the precursor concentration change with the precursor concentration in the response relationship of the precursor concentration to the precursor emission change to obtain the response relationship of the atmospheric pollutant concentration to the precursor emission change; Based on the response relationship between atmospheric pollutant concentration and precursor emission changes, a response surface model of atmospheric pollutant concentration and precursor emission changes is formed.

13. The method according to claim 1, wherein After obtaining the response surface model of atmospheric pollutant concentration to precursor emission changes, it also includes: Using the response surface model of atmospheric pollutant concentration to precursor emission changes, emission reduction measures whose air quality benefits exceed the set threshold are screened from multiple emission reduction measures to be implemented.

14. The method according to claim 13, wherein Using the response surface model of atmospheric pollutant concentrations to changes in precursor emissions, we screened out emission reduction measures whose air quality benefits exceeded the set threshold from a number of emission reduction measures to be implemented, including: Obtain data on emission reduction measures to be implemented in multiple emission source sectors; Calculate the precursor emission reduction ratio corresponding to each emission reduction measure for each emission source sector using the emission factor method; Input the precursor emission reduction ratio corresponding to each emission reduction measure of each emission source department into the response surface model of atmospheric pollutant concentration to precursor emission change, and calculate and output the change in atmospheric pollutant concentration corresponding to each emission reduction measure; Based on the change in atmospheric pollutant concentration corresponding to each emission reduction measure, emission reduction measures whose air quality benefits exceed the set threshold are screened.

15. A response surface model construction device for atmospheric pollutant concentration analysis, characterized in that: include: Diffusion model processing module, used to simulate precursor emissions in the study area based on the Lagrangian diffusion model and generate precursor emission concentration data; The precursor emission concentration data includes precursor concentrations after emission from different precursor emission sources; based on the precursor emission concentration data, a response relationship between the precursor concentration and the change in precursor emission is established using a statistical method; A three-dimensional air quality model processing module is used to simulate precursor emission scenarios in a study area based on the three-dimensional air quality model and generate scenario simulation result data; the scenario simulation result data includes the concentration of atmospheric pollutants after the emission of different precursors; and based on the polynomial response surface method, the scenario simulation result data is used to establish a response relationship between the concentration of atmospheric pollutants and the change of the precursor concentration; The coupling module is used to couple the response relationship of the precursor concentration to the change of the precursor emission with the response relationship of the atmospheric pollutant concentration to the change of the precursor concentration, so as to obtain a response surface model of the atmospheric pollutant concentration to the change of the precursor emission.

16. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 14 is implemented.

17. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 14 is implemented.

18. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 14 is implemented.

Images