A pollutant emission reduction and concentration response fitting and optimization control method and system

Abstract

This invention belongs to the field of environmental protection technology and relates to a method and system for fitting and optimizing pollutant emission reduction and concentration response. The method includes: S1: Real-time collection of pollutant data and meteorological data of the target area; S2: Based on the data, using a combination of machine learning and numerical simulation, determining the response relationship between the reduction of precursors in the pollution source and changes in the concentration of pollutants in the air; S3: When the concentration of pollutants in the air exceeds a threshold, activating the air quality optimization and control model, which takes minimizing the total cost of emission reduction as the objective function and achieving the air quality improvement target as the constraint; S4: Solving the air quality optimization and control model using an optimization algorithm to obtain the optimal emission reduction ratio scheme; S5: Implementing corresponding emission reduction control measures according to the optimal emission reduction ratio scheme. This method can achieve accurate prediction and efficient control of air pollutants, significantly improving air quality management efficiency and reducing treatment costs.

Description

Technical Field

[0001] This invention belongs to the field of environmental protection technology and relates to a method and system for optimizing and controlling pollutant emission reduction, particularly a method and system for fitting and optimizing pollutant emission reduction and concentration response. Background Technology

[0002] With the intensification of global climate change and rapid urbanization, air pollution has become a major global issue. Air pollution not only poses a serious threat to human health but also has long-term negative impacts on the ecological environment. Traditional pollutant reduction technologies and concentration control methods suffer from problems such as data lag, insufficient prediction accuracy, and low control efficiency. Most existing pollutant reduction measures rely heavily on manual judgment and experience-based control, failing to achieve precise control of pollution sources and resulting in low pollutant control efficiency.

[0003] Therefore, the shortcomings of existing technologies are mainly reflected in the following aspects:

[0004] 1. Lagging pollutant monitoring system: Although the monitoring system has achieved automated sampling and testing, which greatly shortens the monitoring cycle compared to traditional manual monitoring, the integration of diverse observation data such as meteorological data, ground monitoring, ground-based radar, and satellite remote sensing is still lagging and cannot reflect changes in pollutant concentration in real time.

[0005] 2. Inaccurate concentration-response relationships of pollutants: Existing methods largely rely on historical data and simplified regression analysis, failing to accurately predict future pollution trends. For example, PM2.5... 2.5 Emission reduction mainly relies on linear programming models, especially with the adoption of one-size-fits-all and emergency emission reduction policies. However, for in-depth treatment of future air pollution, it is difficult to reflect the scientific nature of governance based on the nonlinear response relationship between precursor emission reduction and secondary components in the air.

[0006] 3. Insufficient optimization of emission reduction strategies: Most emission reduction strategies rely on human experience and lack intelligent control. Emission reduction strategies often fail to respond to changes in pollutant concentrations in a timely manner, resulting in excessively high or low emissions.

[0007] 4. Lack of adaptive control mechanism: The existing technology system is poorly adaptable to changes in the external environment and cannot effectively regulate the atmospheric environment based on its current status.

[0008] Therefore, in view of the shortcomings of the existing technologies, there is an urgent need for a new method and system for optimizing and controlling pollutant emission reduction. Summary of the Invention

[0009] To address the shortcomings of existing technologies, this invention proposes a method and system for matching and optimizing pollutant emission reduction and concentration response, which can achieve accurate prediction and efficient control of air pollutants, significantly improve air quality management efficiency and reduce treatment costs.

[0010] To achieve the above objectives, the present invention provides the following technical solution:

[0011] A method for fitting and optimizing the control of pollutant emission reduction and concentration response, characterized by comprising the following steps:

[0012] S1: Real-time collection of pollutant and meteorological data for the target area through a multi-source monitoring network;

[0013] S2: Based on the pollutant data and meteorological data, the response relationship between the reduction of precursors in the pollution source and the change in the concentration of pollutants in the air is determined by using a combination of machine learning and numerical simulation.

[0014] S3: When the concentration of pollutants in the air exceeds a preset threshold, the air quality optimization and control model is activated. The air quality optimization and control model takes minimizing the total cost of emission reduction as the objective function and achieving the air quality improvement target as the constraint condition.

[0015] S4: Solve the air quality optimization and control model using an optimization algorithm to obtain the optimal emission reduction ratio scheme for different pollution sources;

[0016] S5: Implement corresponding emission reduction control measures according to the optimal emission reduction ratio scheme.

[0017] Preferably, in step S2, the air pollutants include primary components and secondary components, and the response relationships between the reduction of precursor emissions from the pollution source and changes in the concentrations of primary and secondary components in the air are determined respectively.

[0018] Preferably, in step S2, a linear programming approach is used to quantify the response relationship between precursor emission reduction in pollution sources and changes in the concentration of primary components in the air. For secondary components in the air, firstly, scenario combinations covering all precursors in the pollution source within a feasible emission reduction range are designed. Each scenario is simulated using an air quality numerical model to calculate the corresponding changes in secondary component concentrations. Then, based on the simulation results, a second-order polynomial multivariate regression fitting is performed using the least squares method or a neural network algorithm to obtain the fitted polynomial and unknown coefficients. Finally, based on the simulation results, the polynomial is solved to obtain the optimal coefficient combination, thereby nonlinearly quantifying the response relationship between precursor emission reduction in pollution sources and changes in the concentration of secondary components in the air.

[0019] Preferably, in step S2, the primary component is primary particulate matter (PPM) in the air, and the secondary component includes secondary inorganic aerosols (SNA) and secondary organic aerosols (SOA) in the air.

[0020] The response relationship between the reduction of precursor emissions from pollution sources and changes in the concentration of primary particulate matter (PPM) in the air is as follows: In the formula, This represents the change in the concentration of primary particulate matter (PPM) in the air, expressed in μg / m³. 3 , , where k0 is the reduction ratio of primary particulate matter (PPM) in the pollution source, k1 is the coefficient of the first-order monomial;

[0021] The response relationship between precursor emission reduction in pollution sources and changes in SNA concentration in the air is as follows:

[0022] In the formula, This represents the change in SNA concentration in the air, expressed in μg / m³. 3 , , and The pollution sources are respectively , and The emission reduction ratio, For constant terms, , and The coefficients of the first-order monomials, , , , , and The coefficients are second-order monomials;

[0023] The response relationship between precursor emission reduction in pollution sources and changes in SOA concentration in the air is as follows: In the formula, This represents the change in SOA concentration in the air, expressed in μg / m³. 3 , and The pollution sources are respectively and The emission reduction ratio, For constant terms, and The coefficients of the first-order monomials, , and These are the coefficients of a second-order monomial.

[0024] Preferably, in step S2, the air pollutants further include 3. Furthermore, the response relationship between the reduction of precursor emissions from pollution sources and changes in their concentration in the air is as follows: In the formula, In the air 3. Concentration change, in μg / m³ 3 , and The pollution sources are respectively and The emission reduction ratio, For constant terms, and The coefficients of the first-order monomials, , and These are the coefficients of a second-order monomial.

[0025] Preferably, in step S3, the air quality optimization and control model is:

[0026] ,

[0027] In the formula, i represents different precursors in the pollution source, j represents different pollution sources, and k represents different cities within the target area. The emissions of precursor pollutant i from city k (source j). The emission reduction ratio of precursor pollutant i from city k (source j). The emission reduction cost for precursor i pollution source j;

[0028] The constraints include constraint 1 and constraint 2, wherein,

[0029] Constraint 1 is: the emission reduction efforts must meet both the maximum emission reduction potential and the policy's minimum emission reduction requirements, that is:

[0030] ,

[0031] In the formula, The emission reduction ratio of precursor pollutant i from city k (source j). The minimum emission reduction ratio required by policy for precursor pollutant source j city k. The maximum emission reduction potential for precursor pollutant i in city k;

[0032] Constraint 2 is: PM in the air 2.5 The concentration reaches the air quality improvement target, namely:

[0033] ,

[0034] In the formula, The baseline PM for city k 2.5 Current concentration status To consider the reduction of PM2.5 by all precursors (i), all pollution sources (j), all cities (k), and all cities after regional transport. 2.5 Concentration change, PM PM for city k 2.5 Concentration improvement target;

[0035] in, ,

[0036] In the formula, The change in primary particulate matter (PPM) concentration after emission reduction of precursor i, pollution source j, and city k. The change in secondary inorganic aerosol SNA concentration after emission reduction of precursor i (pollution source j, city k) The change in SOA concentration of secondary organic aerosols after emission reduction by the precursor i, pollution source j, city k.

[0037] Preferably, in step S3, the constraint condition further includes: in the air 3. The concentration reaches the air quality improvement target constraint value, that is:

[0038] ,

[0039] in, As the benchmark for city k 3. Current concentration status To consider regional transport and the emissions reductions from multiple precursors (i), multiple pollution sources (j), and multiple cities (k), 3. Change in concentration For city k 3. The target for improving concentration.

[0040] Furthermore, the present invention also provides a pollutant emission reduction and concentration response fitting and optimization control system, characterized in that it includes:

[0041] The data acquisition module is used to collect pollutant data and meteorological data of the target area in real time through a multi-source monitoring network;

[0042] The response relationship determination module is used to determine the response relationship between the reduction of precursor emissions in the pollution source and the change in the concentration of pollutants in the air based on the pollutant data and meteorological data, using a combination of machine learning and numerical simulation.

[0043] An air quality optimization and control model, wherein the air quality optimization and control model takes minimizing the total cost of emission reduction as the objective function and achieving the air quality improvement target as the constraint condition;

[0044] The optimal emission reduction ratio solution module is used to solve the air quality optimization and control model using optimization algorithms to obtain the optimal emission reduction ratio solution for different pollution sources.

[0045] The emission reduction control measures execution module is used to execute corresponding emission reduction control measures according to the optimal emission reduction ratio scheme.

[0046] Furthermore, the present invention also provides a device for fitting and optimizing pollutant emission reduction and concentration response, characterized in that it includes:

[0047] One or more processors;

[0048] Memory, used to store one or more programs;

[0049] When the one or more programs are executed by the one or more processors, the one or more processors implement the pollutant emission reduction and concentration response fitting and optimization control method as described above.

[0050] Finally, the present invention also provides a computer-readable storage medium, characterized in that it stores a computer program thereon, which, when executed by a processor, implements the pollutant emission reduction and concentration response fitting and optimization control method as described above.

[0051] Compared with the prior art, the pollutant emission reduction and concentration response fitting and optimization control method and system of the present invention have one or more of the following beneficial technical effects:

[0052] 1. High efficiency: Through an intelligent sensing system, this invention can achieve accurate identification and real-time monitoring of pollutant concentration, significantly improving the efficiency of intelligent sensing of pollution events and pollution processes.

[0053] 2. Accuracy: The pollutant concentration response model established in this invention can accurately predict changes in air pollutant concentration, ensuring that emission reduction measures can respond to changes in pollutant concentration in a timely and effective manner.

[0054] 3. Automation and Intelligence: This invention achieves automatic control of pollutant emission reduction strategies by combining optimized control methods with intelligent optimization algorithms, which greatly improves the level of intelligence of control.

[0055] 4. Scalability and adaptability: This invention is applicable to air pollution control in various environments and has broad application prospects. Attached Figure Description

[0056] Figure 1 This is a flowchart of the pollutant emission reduction and concentration response fitting and optimization control method of the present invention.

[0057] Figure 2 This is an exemplary multi-scenario simulation matrix of SNA concentration and precursor concentration according to the present invention.

[0058] Figure 3This is an exemplary multi-scenario simulation of SNA concentration fitting of the emission reduction and concentration response surface of the present invention.

[0059] Figure 4 This is an exemplary multi-scenario simulation matrix of SOA concentration and precursor concentration according to the present invention.

[0060] Figure 5 This is an exemplary multi-scenario simulation SOA concentration fitting emission reduction and concentration response surface of the present invention.

[0061] Figure 6 This is an exemplary multi-scenario simulation of O3 concentration fitting, showing the emission reduction and concentration response surface of the present invention.

[0062] Figure 7 This is a schematic diagram of the pollutant emission reduction and concentration response fitting and optimization control system of the present invention. Detailed Implementation

[0063] Before detailing any embodiment of the invention, it should be understood that the invention, in its application, is not limited to the details of the construction and arrangement of the components set forth in the following description or illustrated in the following figures. The invention can have other embodiments and can be practiced or carried out in various ways. Furthermore, it should be understood that the wording and terminology used herein are for descriptive purposes and should not be considered limiting. The use of “comprising” or “having” and variations thereof is intended to cover the items set forth below and their equivalents, as well as any additional items. Unless otherwise specified or limited, the terms “installation,” “connection,” “support,” and “linkage,” and variations thereof are used broadly and cover both direct and indirect installation, connection, support, and linking. Moreover, “connection” and “linkage” are not limited to physical or mechanical connections or links.

[0064] Furthermore, firstly, in the disclosure of this invention, the terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the above terms should not be construed as limiting this invention. Secondly, the term "a" should be understood as "at least one" or "one or more," that is, in one embodiment, the number of an element can be one, while in another embodiment, the number of the element can be multiple. The term "a" should not be construed as a limitation on the quantity.

[0065] Figure 1 A flowchart of the pollutant emission reduction and concentration response fitting and optimization control method of the present invention is shown. Figure 1 As shown, the pollutant emission reduction and concentration response fitting and optimization control method of the present invention includes the following steps:

[0066] S1: Data Acquisition.

[0067] Pollutant and meteorological data for the target area are collected in real time through a multi-source monitoring network. This network includes air quality monitoring stations, traffic monitoring stations, industrial park stations, and super-monitors. The collected pollutant data includes at least the concentration data of each precursor from each pollution source and the concentration data of pollutants in the air. Specifically, the concentration data of each precursor from each pollution source includes primary particulate matter (PM2.5), SO2, and NO2 from each pollution source. x VOCs s Data on air pollutant concentrations, including PM2.5 and O3 concentrations, are also included. Meteorological data includes wind speed, wind direction, temperature, and humidity.

[0068] S2: Response relationship determined.

[0069] Based on the pollutant data and meteorological data, a combination of machine learning and numerical simulation is used to determine the response relationship between the reduction of precursor emissions from pollution sources and changes in the concentration of pollutants in the air. In other words, a pollutant concentration response model is established between the reduction of precursor emissions from pollution sources and changes in the concentration of pollutants in the air.

[0070] Existing technologies often employ linear programming or simplified empirical formulas to express the response relationship between precursor emission reduction in pollution sources and changes in air pollutant concentrations. However, these methods cannot effectively capture the complex dynamic balance between precursor emission reduction and air pollutant concentrations. Unlike existing technologies that rely on linear programming or simplified empirical formulas, the pollutant concentration response model of this invention innovatively separates primary and secondary components in the air and quantifies the nonlinear response relationship between multiple secondary components (such as secondary inorganic aerosols SNA, secondary organic aerosols SOA, and ozone) and their precursors (i.e., precursors in pollution sources). This allows for accurate fitting and solves the problem of insufficient accuracy in characterizing changes in the concentration of secondary components (such as SNA and SOA) using traditional methods.

[0071] Specifically, in this invention, the primary component is primary particulate matter (PPM) in the air, and the secondary component includes secondary inorganic aerosols (SNA) and secondary organic aerosols (SOA) in the air. Of course, the secondary component may also include O3 in the air. Therefore, in this invention, determining the response relationship between precursor emission reduction in a pollution source and changes in pollutant concentration in the air specifically includes:

[0072] 1. Determine the response relationship between the reduction of precursor emissions from pollution sources and changes in the concentration of primary components in the air (i.e., primary particulate matter PPM).

[0073] For primary airborne components, including directly emitted sulfates, nitrates, ammonium salts, and primary organic compounds (POA), the response mechanism of their concentration changes in the air to the reduction of precursors from pollution sources is simpler than that of secondary components. It can still be quantified using traditional linear programming, meaning that the reduction of precursors from pollution sources corresponds to a proportional decrease in the concentration of primary airborne components. In other words, a linear programming approach can be used to quantify the response relationship between the reduction of precursors from pollution sources and changes in the concentration of primary airborne components. This is the response model for the relationship between the reduction of precursors from pollution sources and changes in the concentration of primary airborne components:

[0074] ,

[0075] In the formula, This represents the change in the concentration of primary particulate matter (PPM) in the air, expressed in μg / m³. 3 ; denoted as k0, representing the reduction ratio of primary particulate matter (PPM) from the pollution source; k0 is a constant term; k1 is a first-order monomial coefficient. k0 and k1 can be obtained experimentally, that is, through several measurements. and It is derived that...

[0076] 2. Determine the response relationship between the reduction of precursor emissions from pollution sources and changes in the concentration of secondary inorganic aerosols (SNAs) in the air.

[0077] Traditional linear methods cannot accurately describe the dynamic processes of secondary components in the air, mainly in the following aspects: (1) Gas-particle conversion and competitive reactions: Alkaline gas ammonia (NH3) will simultaneously undergo neutralization reactions with two strong acids in the atmosphere - sulfuric acid (H2SO4) and nitric acid (HNO3); (2) Effect on particulate matter acidity: The supply of ammonia determines the concentration of fine particulate matter (PM2.5) in the atmosphere. 2.5 (3) The key to acidity is that when ammonia is sufficient, particulate matter is usually neutralized and becomes neutral or weakly alkaline. When ammonia is insufficient, acidic gases cannot be completely neutralized, and particulate matter will become acidic. (4) Dynamic balance under the influence of multiple factors: The acid-base balance of SNA is constrained by a variety of environmental factors and is a dynamic process, such as temperature, humidity, precursor concentration, and other alkaline substances. Therefore, in order to reduce the emission of precursors in response to the changes in the concentration of secondary inorganic aerosol SNA in the air, multiple precursors (SO2, NO) should be implemented. x Coordinated control of NH3 and other substances is particularly important.

[0078] Therefore, in this invention, the key pollution sources affecting high concentrations of pollutants in the air are first identified by the intelligent sensing system. For these key sources, a multi-scenario concentration matrix is ​​established, showing the emission reduction ratio of precursors within the pollution source and the changes in the concentration of secondary components in the air within a feasible emission reduction range. For example, the emission reduction potential of the entire industry can be set from 0% to 100%, with a reduction resolution of 20%, and the global sensitivity of pollutant concentrations in the air can be obtained through multiple simulation scenarios. Alternatively, the emission reduction potential of the thermal power industry can be set from 0% to 40%, with a reduction resolution of 5%, and the local sensitivity of the thermal power industry within the 0-40% range can be obtained through multiple simulation scenarios. Dust sources, however, cannot be reduced in the short term, and their components are mainly crustal particulate matter, making them unsuitable for secondary component sensitivity analysis.

[0079] Each simulation scenario is performed using an air quality numerical model (such as the commonly used CMAQ, CAMx, and WRF-Chem air quality numerical models) to calculate the corresponding changes in secondary component concentrations. For example, Figure 2 An exemplary multi-scenario simulated SNA concentration and precursor multi-scenario concentration matrix of the present invention is shown, in which SO2, NO x The emission reduction ratios for SO2 and NO3 were 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0, respectively. x The changes in the concentration of secondary inorganic aerosols (SNA) in the air under different emission reduction ratios, NH3 and NH3.

[0080] Then, the multi-scenario concentration matrix can be transformed into a nonlinear response surface. For example, Figure 2 The exemplary multi-scenario simulated SNA concentration and precursor multi-scenario concentration matrix of the present invention shown can be transformed into Figure 3 The present invention illustrates an exemplary multi-scenario simulation of SNA concentration fitting of emission reduction and concentration response surfaces.

[0081] Subsequently, based on the least squares method or neural network algorithm, a second-order polynomial multivariate regression can be performed on the nonlinear response surface to obtain a quantitative expression for the SNA in the global sensitivity. This fitting process not only improves the accuracy of the response relationship but also intuitively reflects the contribution of each precursor through the polynomial coefficients, providing an interpretable mathematical model for optimized regulation.

[0082] In this invention, the response relationship between precursor emission reduction in pollution sources and changes in secondary inorganic aerosol SNA concentration, i.e., the response model, is as follows:

[0083]

[0084] ,

[0085] In the formula, This represents the change in SNA concentration in the air, expressed in μg / m³. 3 ; , and The pollution sources are respectively , and The proportion of emission reduction; For constant terms; , and The coefficients of the first-order monomial; , , , , and These are the coefficients of a second-order monomial.

[0086] 3. Determine the response relationship between the reduction of precursor emissions from pollution sources and changes in the concentration of secondary organic aerosols (SOA) in the air.

[0087] Similar to the reduction of precursors to changes in the concentration of secondary inorganic aerosols (SNAs) in the air, the reduction of precursors to changes in the concentration of secondary organic aerosols (SOAs) in the air is also implemented through multiple precursor (NOx) reduction measures. x Coordinated control of VOCs.

[0088] Similarly, the intelligent sensing system first identifies key pollution sources affecting high concentrations of pollutants in the air. For these key sources, a multi-scenario concentration matrix is ​​established, relating the emission reduction ratio of precursors within the pollution source to changes in SOA (Secondary Organic Aerosol) concentrations within feasible emission reduction ranges. Each simulation scenario is simulated using an air quality numerical model (such as commonly used CMAQ, CAMx, and WRF-Chem models) to calculate the corresponding changes in secondary component concentrations. For example, Figure 4 An exemplary multi-scenario simulated SOA concentration and precursor multi-scenario concentration matrix of the present invention is shown, in which NO x The emission reduction ratios for VOCs were 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0, respectively, thus yielding NO. x The changes in SOA concentrations of secondary organic aerosols and VOCs in the air under different emission reduction ratios.

[0089] Then, the multi-scenario concentration matrix can be transformed into a nonlinear response surface. For example, Figure 4 The exemplary multi-scenario simulation matrix of SOA concentration and precursor concentration shown in this invention can be transformed into Figure 5The present invention illustrates an exemplary multi-scenario simulation of SOA concentration fitting of emission reduction and concentration response surfaces.

[0090] Subsequently, based on the least squares method or neural network algorithm, a second-order polynomial multivariate regression can be performed on the nonlinear response surface to obtain a quantitative expression for SOA in the global sensitivity. This fitting process not only improves the accuracy of the response relationship but also intuitively reflects the contribution of each precursor through the polynomial coefficients, providing an interpretable mathematical model for optimized regulation.

[0091] In this invention, the response relationship between precursor emission reduction in pollution sources and changes in secondary organic aerosol (SOA) concentration, i.e., the response model, is as follows:

[0092]

[0093] In the formula, This represents the change in SOA concentration in the air, expressed in μg / m³. 3 ; and The pollution sources are respectively and The proportion of emission reduction; For constant terms; and The coefficients of the first-order monomial; , and These are the coefficients of a second-order monomial.

[0094] 4. Determine the response relationship between the reduction of precursor emissions from pollution sources and changes in O3 concentration in the air.

[0095] For O3 in the air, its concentration changes are related to the precursor NO in the pollution source. x VOCs s Nonlinear response of emission reduction and PM 2.5The difference is even more significant. Currently, some progress has been made in quantitatively assessing O3 sensitivity using techniques such as brute-force method (BFM), Ozone Source Appraisal Technology (OSAT), and decoupled direct method (DDM). However, BFM, which obtains nonlinear responses through scenario simulation, struggles to distinguish between different emission regions or differences in contribution from different emission regions; OSAT can obtain ozone concentration contributions from different regions and industries, but it cannot reflect the nonlinear impact of differentiated precursor emission reductions on O3 concentration in the air; the higher-order direct decoupled method (HDDM) can obtain the nonlinear response relationship between precursor emissions and O3 concentration in the air through sensitivity equations, but when the emission reduction rate is greater than 50%, it shows a significant overestimation compared to BFM results. To overcome these limitations, this invention uses a method combining multi-scenario simulation and HDDM to quantify NO... x and VOCs s The nonlinear response relationship between emission reduction and changes in air O3 concentration is investigated, and an O3 formation isoconcentration (EKMA) curve is constructed. In other words, this invention proposes a comprehensive algorithm combining the high-order decoupled direct method (HDDM) built into the functional modules of air quality models (CMAQ, CAMx) with multi-scenario simulation. This algorithm is designed to cover different NO... x and VOCs s A scenario concentration matrix covering emission reduction ratios (e.g., 0, 0.2, 0.4, 0.6, 0.8, 1.0) avoids the overestimation problem of HDDM under high emission reduction rates. This scheme first designs a scenario concentration matrix covering different NOx and VOCs emission reduction ratios (e.g., 0, 0.2, 0.4, 0.6, 0.8, 1.0); then, using the CAMx-HDDM model, it quantifies the sensitivity of O3 generation to regional contributions from different pollution sources in different cities, with national monitoring stations as the receptors. Furthermore, it interpolates EKMA curves using multi-scenario simulation data and performs nonlinear fitting using Taylor series expansion to obtain a quantitative response expression for O3 concentration to precursor changes. Since HDDM may overestimate O3 changes when simulating precursor emission reduction rates exceeding 50%, this invention further allocates the first-order and second-order sensitivity coefficients provided by HDDM according to the overall response ratio of the EKMA curve, obtaining... Figure 6 The response surface shown is used to correct for errors under high emission reduction scenarios. This method will further improve the reliability of the O3 response model.

[0096] Therefore, the response relationship between the reduction of precursor emissions from pollution sources and changes in air O3 concentration, i.e., the response model, is as follows:

[0097]

[0098] In the formula, This represents the change in O3 concentration in the air, expressed in μg / m³. 3 ; and The pollution sources are respectively and The proportion of emission reduction; For constant terms; and The coefficients of the first-order monomial; , and These are the coefficients of a second-order monomial.

[0099] constant term Characterizing the background O3 concentration under baseline conditions (without emission reductions), it reflects inherent factors such as regional background values ​​and contributions from inter-regional transport; the first-order monomial coefficients embody... and The linear impact of emission changes on O3 concentration changes is analyzed; second-order monomial coefficients characterize the marginal effect changes (nonlinear characteristics). This response model, through systematic coefficient solving and in-depth integration of physicochemical mechanisms, achieves precise quantification of complex atmospheric photochemical processes and can accurately predict O3 concentration changes under different emission reduction combinations.

[0100] Having established the response relationship of the aforementioned secondary components—that is, after establishing the response model—once the coefficients are determined, the specific response relationship between changes in secondary component concentration and changes in the reduction of pollutants in the pollution source can be clarified, thereby predicting the amount of change in secondary component concentration. In this invention, the coefficients can be determined in the following manner.

[0101] First, as mentioned earlier, a multi-scenario concentration matrix is ​​designed. That is, a combination of scenarios is designed to cover the entire upstream pollution space within a feasible emission reduction range. Each scenario is simulated using an air quality model to calculate the corresponding SNA concentration change value.

[0102] Then, polynomial fitting and coefficient calculation are performed. The concentration matrices for multiple scenarios are used as training sets based on concentration data obtained from air quality simulations. Second-order polynomial multivariate regression fitting is performed using the least squares method or a neural network algorithm to determine the polynomial to be fitted and the unknown coefficients. By calculating and solving the normal equations, the optimal combination of coefficients is obtained, thereby achieving a nonlinear quantitative expression of the relationship between changes in the concentration of secondary components in the air and the reduction of precursor emissions from pollution sources.

[0103] Finally, coefficient verification and optimization are performed. The calculated correlation of the fit must reach an acceptable level to ensure the explanatory power of the response model. In this invention, if a large deviation is found, the simulation resolution and simulation scenario density can be increased based on the air quality model, and a neural network algorithm can be used for supplementary fitting and correction.

[0104] This invention achieves a quantitative expression of response relationships through multi-scenario simulation and machine learning algorithms, significantly improving the scientific rigor of emission reduction strategies. Unlike traditional methods using empirical coefficients or linear regression, this invention establishes a complete coefficient determination method. Through multi-scenario numerical simulation, machine learning algorithm fitting, and dynamic verification and optimization mechanisms, it ensures the accuracy and applicability of multi-component, nonlinear response fitting in real-world complex atmospheric environments. This solves the accuracy problem of traditional methods (such as linear regression or uncorrected HDDM), improving the accuracy of air pollutant concentration prediction and the scientific rigor of emission reduction strategies.

[0105] S3: Determine the optimal air quality control model.

[0106] In this invention, an air quality optimization and control model is established with the objective function of minimizing the total cost of emission reduction and the constraint of achieving air quality improvement goals. Control measures are automatically activated when the concentration of pollutants in the air reaches a predetermined threshold.

[0107] In this invention, the air quality optimization and control model is as follows:

[0108] ,

[0109] In the formula, i represents different precursors in the pollution source (since the precursors in the pollution source in this invention include SO2 and NO). x VOCs s PPM, NH3, therefore, the value of i is 1-5).

[0110] j represents different pollution sources, such as industrial, power, mobile sources, residential, and others;

[0111] k represents different cities within the target area;

[0112] The emissions of precursor i from pollution source j in city k can be obtained from official emission inventories issued by national / local environmental protection departments, or calculated based on enterprise self-monitoring and online continuous monitoring (CEMS), or calculated based on emission factors and activity levels in statistical yearbooks in the "Technical Guidelines for Compiling Emission Inventories of Air Pollutants".

[0113] is the emission reduction ratio of precursor i, pollution source j, and city k, which is the solution variable in the air quality optimization and control model.

[0114] The emission reduction cost of precursor i and pollution source j can be calculated using engineering methods to determine the actual cost of emission reduction measures in industrial parks, real emission reduction and treatment investment costs obtained through enterprise surveys, and reverse inference methods based on cost curves from scientific research papers and international databases in actual projects.

[0115] The constraints include constraint 1 and constraint 2. Among them,

[0116] Constraint 1 is: the emission reduction efforts must meet both the maximum emission reduction potential and the policy's minimum emission reduction requirements, that is:

[0117] ,

[0118] in, The emission reduction ratio of precursor pollutant i from city k (source j). The minimum emission reduction ratio required by policy for precursor pollutant source j city k. The maximum emission reduction potential for precursor i pollution source j city k.

[0119] Constraint 2 is: PM in the air 2.5 The concentration reaches the air quality improvement target, namely:

[0120] ,

[0121] In the formula, The baseline PM for city k 2.5 Current concentration status; To consider the reduction of PM2.5 by all precursors (i), all pollution sources (j), all cities (k), and all cities after regional transport. 2.5 Concentration change; PM PM for city k 2.5 The target for improving concentration.

[0122] in, ,

[0123] In the formula, The change in primary particulate matter (PPM) concentration after emission reduction of precursor i, pollution source j, city k. The change in the concentration of secondary inorganic aerosol SNA after emission reduction of precursor i, pollution source j, city k. The change in SOA concentration of secondary organic aerosols after emission reduction from precursor i, pollution source j, and city k is given. , and It can be predicted based on the response model established in step two.

[0124] To address summer ozone pollution events, constraint condition 3 can be set: in the air 3. The concentration reaches the air quality improvement target constraint value, that is:

[0125] ,

[0126] In the formula, As the benchmark for city k 3. Current concentration status To consider regional transport and the emissions reductions from multiple precursors (i), multiple pollution sources (j), and multiple cities (k), 3. Change in concentration For city k 3. Target for improving concentration. Each of these... It can be predicted based on the response model established in step two.

[0127] Specifically, for medium- to long-term regional air quality, constraints 2 and 3 can be set simultaneously to solve for the optimized emission reduction scheme of coordinated control of fine particulate matter (PM2.5) and ozone. For typical autumn and winter PM2.5 pollution processes, constraint 2 can be set separately to solve for the emergency response plan for autumn and winter pollution processes. For summer ozone pollution processes, constraint 3 can be set separately to solve for the emergency response plan for summer ozone pollution processes. Therefore, the optimization and control technology based on a single objective equation and multiple constraints of this invention can solve for the emission reduction scheme with the lowest cost under multiple environmental management requirements. Compared with the existing dual-objective optimization equations of environmental improvement and economic cost, which are applicable to severe emission reduction schemes for heavy pollution emergencies that maximize environmental improvement, the single objective of economic cost and the multi-constraint solution of air quality of this invention are more suitable for short-term and long-term planned emission reduction strategies.

[0128] S4: Solving for the optimal emission reduction ratio.

[0129] After establishing an air quality optimization and control model, based on collected and predicted data, optimization algorithms (such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA)) can be used to solve the air quality optimization and control model to obtain... This means obtaining the optimal emission reduction ratio scheme for different pollution sources.

[0130] Since the specific solution methods and processes are existing technologies and are not the focus of this invention, they will not be described in detail for the sake of simplicity.

[0131] S5: Implementation of emission reduction and control measures.

[0132] Once the optimal emission reduction ratio scheme for different pollution sources is obtained, corresponding emission reduction control measures can be implemented according to the optimal emission reduction ratio scheme. The control measures include: industrial emission control, automatically adjusting industrial emissions according to the emission limits of pollution sources; traffic flow control, adjusting traffic flow during high-pollution periods according to real-time traffic flow conditions, and even implementing traffic restrictions; and emergency response mechanisms, automatically activating emergency control schemes when pollutant concentrations rise sharply, such as strengthening restrictions on industrial production and reducing traffic in the short term.

[0133] Meanwhile, this invention can dynamically adjust emission reduction strategies based on real-time monitoring and predicted pollutant concentration data. For example, by using a data-driven approach, emission reduction strategies such as target urban pollutant concentrations, time ranges, and control areas can be dynamically adjusted to address spatiotemporal changes in pollutant concentrations and ensure optimal control effectiveness.

[0134] Figure 7 A schematic diagram of the pollutant emission reduction and concentration response fitting and optimization control system of the present invention is shown. Figure 7 As shown, the pollutant emission reduction and concentration response fitting and optimization control system of the present invention includes:

[0135] 1. Data acquisition module.

[0136] The data acquisition module is used to collect pollutant data and meteorological data of the target area in real time through a multi-source monitoring network.

[0137] 2. Response Relationship Determination Module.

[0138] The response relationship determination module is used to determine the response relationship between the reduction of precursor emissions from pollution sources and changes in the concentration of pollutants in the air, based on the pollutant data and meteorological data, using a combination of machine learning and numerical simulation.

[0139] 3. Air quality optimization and control model.

[0140] The air quality optimization and control model takes minimizing the total cost of emission reduction as its objective function and achieving the air quality improvement target as its constraint.

[0141] 4. Module for solving the optimal emission reduction ratio scheme.

[0142] The optimal emission reduction ratio solution module is used to solve the air quality optimization and control model using optimization algorithms to obtain the optimal emission reduction ratio scheme for different pollution sources.

[0143] 5. Emission reduction and control measures implementation module.

[0144] The emission reduction control measure execution module is used to execute corresponding emission reduction control measures according to the optimal emission reduction ratio scheme.

[0145] Furthermore, the present invention also provides a device for fitting and optimizing pollutant emission reduction and concentration response, comprising: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the pollutant emission reduction and concentration response fitting and optimization method of the present invention.

[0146] Finally, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the pollutant emission reduction and concentration response fitting and optimization control method of the present invention.

[0147] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the scope of protection of the present invention. Those skilled in the art can modify or make equivalent substitutions to the technical solutions of the present invention based on the concept of the present invention, without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. A method for fitting and optimizing the control of pollutant emission reduction and concentration response, characterized in that, Includes the following steps: S1: Real-time collection of pollutant and meteorological data for the target area through a multi-source monitoring network; S2: Based on the pollutant data and meteorological data, the response relationship between the reduction of precursors in the pollution source and the change in the concentration of pollutants in the air is determined by using a combination of machine learning and numerical simulation. S3: When the concentration of pollutants in the air exceeds a preset threshold, the air quality optimization and control model is activated. The air quality optimization and control model takes minimizing the total cost of emission reduction as the objective function and achieving the air quality improvement target as the constraint condition. S4: Solve the air quality optimization and control model using an optimization algorithm to obtain the optimal emission reduction ratio scheme for different pollution sources; S5: Implement corresponding emission reduction control measures according to the optimal emission reduction ratio scheme; In step S2, the air pollutants include primary and secondary components. The response relationships between precursor emission reduction in the pollution source and changes in the concentrations of primary and secondary components in the air are determined. Linear programming is used to quantify the response relationship between precursor emission reduction in the pollution source and changes in the concentration of primary components in the air. For secondary components in the air, firstly, scenario combinations covering all precursors in the pollution source within a feasible emission reduction range are designed. Each scenario is simulated using an air quality numerical model to calculate the corresponding changes in secondary component concentrations. Then, based on the simulation results, a second-order polynomial multivariate regression is performed using the least squares method or a neural network algorithm to obtain the fitted polynomial and unknown coefficients. Finally, based on the simulation results, the polynomial is solved to obtain the optimal coefficient combination, thereby nonlinearly quantifying the response relationship between precursor emission reduction in the pollution source and changes in the concentration of secondary components in the air. In step S2, the primary component is primary particulate matter (PPM) in the air, and the secondary component includes secondary inorganic aerosols (SNA) and secondary organic aerosols (SOA) in the air. The response relationship between the reduction of precursor emissions from pollution sources and changes in the concentration of primary particulate matter (PPM) in the air is as follows: In the formula, This represents the change in the concentration of primary particulate matter (PPM) in the air, expressed in μg / m³. 3 , , where k0 is the reduction ratio of primary particulate matter (PPM) in the pollution source, k1 is the coefficient of the first-order monomial; The response relationship between precursor emission reduction in pollution sources and changes in SNA concentration in the air is as follows: In the formula, This represents the change in SNA concentration in the air, expressed in μg / m³. 3 , , and The pollution sources are respectively , and The emission reduction ratio, For constant terms, , and The coefficients of the first-order monomials, , , , , and The coefficients are second-order monomials; The response relationship between precursor emission reduction in pollution sources and changes in SOA concentration in the air is as follows: In the formula, This represents the change in SOA concentration in the air, expressed in μg / m³. 3 , and The pollution sources are respectively and The emission reduction ratio, For constant terms, and The coefficients of the first-order monomials, , and These are the coefficients of a second-order monomial.

2. The method for fitting and optimizing pollutant emission reduction and concentration response according to claim 1, characterized in that, In step S2, the air pollutants also include 3. Furthermore, the response relationship between the reduction of precursor emissions from pollution sources and changes in their concentration in the air is as follows: In the formula, In the air 3. Concentration change, in μg / m³ 3 , and The pollution sources are respectively and The emission reduction ratio, For constant terms, and The coefficients of the first-order monomials, , and These are the coefficients of a second-order monomial.

3. The method for fitting and optimizing pollutant emission reduction and concentration response according to any one of claims 1-2, characterized in that, In step S3, the air quality optimization and control model is as follows: ， In the formula, i represents different precursors in the pollution source, j represents different pollution sources, and k represents different cities within the target area. The emissions of precursor pollutant i from city k (source j). The emission reduction ratio of precursor pollutant i from city k (source j). The emission reduction cost for precursor i pollution source j; The constraints include constraint 1 and constraint 2, wherein, Constraint 1 is: the emission reduction efforts must meet both the maximum emission reduction potential and the policy's minimum emission reduction requirements, that is: ， In the formula, The emission reduction ratio of precursor pollutant i from city k (source j). The minimum emission reduction ratio required by policy for precursor pollutant source j city k. The maximum emission reduction potential for precursor pollutant i in city k; Constraint 2 is: PM in the air 2.5 The concentration reaches the air quality improvement target, namely: ， In the formula, The baseline PM for city k 2.5 Current concentration status To consider the reduction of PM2.5 by all precursors (i), all pollution sources (j), all cities (k), and all cities after regional transport. 2.5 Concentration change, PM PM for city k 2.5 Concentration improvement target; in, , In the formula, The change in primary particulate matter (PPM) concentration after emission reduction of precursor i, pollution source j, city k. The change in secondary inorganic aerosol SNA concentration after emission reduction of precursor i (pollution source j, city k) The change in SOA concentration of secondary organic aerosols after emission reduction by the precursor i, pollution source j, city k.

4. The method for fitting and optimizing pollutant emission reduction and concentration response according to claim 3, characterized in that, In step S3, the constraint condition further includes: in the air 3. The concentration reaches the air quality improvement target constraint value, that is: ， in, As the benchmark for city k 3. Current concentration status To consider regional transport and the emissions reductions from multiple precursors (i), multiple pollution sources (j), and multiple cities (k), 3. Change in concentration For city k 3. The target for improving concentration.

5. A system for fitting and optimizing the control of pollutant emission reduction and concentration response, characterized in that, include: The data acquisition module is used to collect pollutant data and meteorological data of the target area in real time through a multi-source monitoring network; The response relationship determination module is used to determine the response relationship between precursor emission reduction in pollution sources and changes in air pollutant concentrations based on the pollutant data and meteorological data, using a combination of machine learning and numerical simulation. The air pollutants include primary and secondary components, and the module separately determines the response relationships between precursor emission reduction in pollution sources and changes in the concentrations of primary and secondary components in the air. Linear programming is used to quantify the response relationship between precursor emission reduction in pollution sources and changes in the concentration of primary components in the air. For secondary components in the air, scenario combinations covering feasible emission reduction ranges for all precursors in the pollution source are first designed. Each scenario is then analyzed using air... A mass numerical model is used to simulate and calculate the corresponding changes in the concentration of secondary components. Based on the simulation results, a second-order polynomial multivariate regression is performed using the least squares method or a neural network algorithm to obtain the fitted polynomial and unknown coefficients. Finally, the polynomial is solved based on the simulation results to obtain the optimal coefficient combination, thereby nonlinearly quantifying the response relationship between precursor emission reduction in pollution sources and changes in the concentration of secondary components in the air. The primary component is primary particulate matter (PPM) in the air, and the secondary components include secondary inorganic aerosols (SNA) and secondary organic aerosols (SOA) in the air. The response relationship between precursor emission reduction in pollution sources and changes in the concentration of primary particulate matter (PPM) in the air is as follows: In the formula, This represents the change in the concentration of primary particulate matter (PPM) in the air, expressed in μg / m³. 3 , The reduction ratio of primary particulate matter (PPM) in the pollution source is given by k0, where k0 is a constant term and k1 is a first-order monomial coefficient. The response relationship between the reduction of precursors in the pollution source and the change in SNA concentration in the air is as follows: In the formula, This represents the change in SNA concentration in the air, expressed in μg / m³. 3 , , and The pollution sources are respectively , and The emission reduction ratio, For constant terms, , and The coefficients of the first-order monomials, , , , , and The coefficients are second-order monomials; the response relationship between precursor emission reduction in pollution sources and changes in SOA concentration in the air is as follows: In the formula, This represents the change in SOA concentration in the air, expressed in μg / m³. 3 , and The pollution sources are respectively and The emission reduction ratio, For constant terms, and The coefficients of the first-order monomials, , and The coefficients are second-order monomials; An air quality optimization and control model, wherein the air quality optimization and control model takes minimizing the total cost of emission reduction as the objective function and achieving the air quality improvement target as the constraint condition; The optimal emission reduction ratio solution module is used to solve the air quality optimization and control model using optimization algorithms to obtain the optimal emission reduction ratio solution for different pollution sources. The emission reduction control measures execution module is used to execute corresponding emission reduction control measures according to the optimal emission reduction ratio scheme.

6. A device for fitting and optimizing pollutant emission reduction and concentration response, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the pollutant emission reduction and concentration response fitting and optimization control method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the pollutant emission reduction and concentration response fitting and optimization control method as described in any one of claims 1-4.

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