Dynamic decision method and device for ozone pollution prevention and control

Abstract

This invention discloses a dynamic decision-making method and device for ozone pollution control. This method and device realize a dynamic decision-making technical route for ozone pollution control, encompassing monitoring and early warning, ozone source analysis, decision evaluation, and compliance assessment and performance evaluation. It establishes a measure library based on emission inventories and continuously evaluates and optimizes the measure library through scenario simulations. By integrating local and regional inventories, it achieves refined and rapid source tracing of ozone and its precursors; it enables multi-dimensional comprehensive source analysis using models, observations, and inventories, accurately identifying key emission areas and critical polluting industries; and it facilitates rapid evaluation and dynamic optimization of ozone pollution compliance plans. Utilizing multiple methods such as three-dimensional monitoring, emission inventories, numerical simulations, and emergency emission reduction technologies, it conducts multi-dimensional analysis of ozone pollution sources, quantitatively analyzes the source contributions of local areas, surrounding regions, and polluting industries, and identifies the key causes of urban ozone pollution.

Description

TECHNICAL FIELD

[0001] The present application relates to air pollution forecasting and prevention technology, more specifically, the present application relates to a dynamic decision-making method and device for ozone pollution prevention. BACKGROUND

[0002] Ozone is one of the main components of urban photochemical smog, and is the oxidation product of primary pollutants through a series of photochemical reactions. Its production leads to the enhancement of atmospheric oxidation, and also causes PM 2.5 secondary fine particulate pollution. The formation and change of near-surface ozone are mainly related to the emission of precursor pollutants such as nitrogen oxides (NOx) and volatile organic compounds (VOCs) emitted by human activities, including emissions from automobile exhaust, industrial processes such as petroleum and chemical industry, material synthesis, fuel combustion in power plants and other industrial enterprises, and biomass burning.

[0003] With the rapid development of China's economy and the continuous acceleration of urbanization, the situation of complex pollution represented by fine particulate matter (PM 2.5 ) and ozone (O3) in the atmosphere is severe, and urban haze pollution and photochemical pollution have gradually become the focus of attention of the society. Urban ozone pollution has become one of the key factors restricting air quality improvement and standard compliance. However, as a typical secondary pollutant, the reaction process and generation mechanism of ozone are very complex, especially in different regions with different economic industrial structures and emission characteristics. The reaction mechanism and corresponding control strategy of ozone are still not systematically studied. SUMMARY

[0004] The present application innovatively provides a dynamic decision-making method and device for ozone pollution prevention, which uses a variety of means such as three-dimensional monitoring, emission inventory, numerical simulation and emergency emission reduction technology to analyze ozone pollution sources in multiple dimensions, quantitatively analyzes the source contribution of local, surrounding areas and pollution industries, and recognizes the key causes of urban ozone pollution.

[0005] To achieve the above technical purposes, in one aspect, the present application discloses a dynamic decision-making method for ozone pollution prevention and control. The dynamic decision-making method for ozone pollution prevention and control comprises: observing and analyzing ozone pollution and its precursors, and identifying target control cities and target control industries for ozone pollution prevention and control; determining the control starting time of the target control cities according to the numerical prediction of air quality; compiling an atmospheric pollutant emission source list for the target control cities and the target control industries; identifying emission areas and emission industries affecting ozone pollution in the target control cities according to the compiled atmospheric pollutant emission source list; establishing a prevention and control measure library for the statistical results of different emission industries in the atmospheric pollutant emission source list; taking corresponding prevention and control measures for each emission area and emission industry identified affecting ozone pollution in the target control cities according to the prevention and control measure library; performing air quality scenario simulation on the taken prevention and control measures, and evaluating the effect of the reduction amount of each pollutant obtained by each prevention and control measure on the air quality scenario simulation through the quantitative response relationship between each prevention and control measure and the activity level and emission factor of the related pollution source; adjusting the prevention and control measures according to the evaluation results of the effect of the reduction amount of each pollutant on the air quality scenario simulation, and combining the air quality standard to be reached.

[0006] Further, for the dynamic decision-making method for ozone pollution prevention and control, observing and analyzing ozone pollution and its precursors, and identifying target control cities and target control industries for ozone pollution prevention and control, comprises: using the data of the existing environmental air quality standard monitoring sites in the city, combining with the fixed-point sampling analysis, supplementing the observation data of ozone precursor components, analyzing the temporal and spatial distribution characteristics of ozone and its precursors, and the chemical components and reaction activity of the precursors, and identifying the target control cities and target control industries for ozone pollution prevention and control.

[0007] Further, for the dynamic decision-making method for ozone pollution prevention and control, compiling an atmospheric pollutant emission source list comprises fusing regional background list and local city list.

[0008] Further, for the dynamic decision-making method for ozone pollution prevention and control, identifying emission areas and emission industries affecting ozone pollution in the target control cities according to the compiled atmospheric pollutant emission source list comprises: quantitatively analyzing the ozone source contribution of different regions, different industry emission sources, and different time periods according to the compiled atmospheric pollutant emission source list, and identifying the emission areas and emission industries affecting ozone pollution in the target control cities.

[0009] Further, for the dynamic decision-making method for ozone pollution prevention and control, the air quality standard is an ozone pollution compliance planning.

[0010] Further, for the dynamic decision-making method of the ozone pollution prevention and control, the atmospheric pollutant emission source list is compiled, including: compiling the atmospheric pollutant emission source list based on the emission inventory research data carried out by the target control city, existing research results of atmospheric pollution sources, and relevant data of other cities.

[0011] Further, for the dynamic decision-making method of the ozone pollution prevention and control, the atmospheric pollutant emission source list is compiled, including: compiling the atmospheric pollutant emission source list based on the emission inventory research data carried out by the target control city, existing research results of atmospheric pollution sources, and relevant data of other cities.

[0012] Further, for the dynamic decision-making method of the ozone pollution prevention and control, the effect of air quality scenario simulation includes one or more of the following: simulation effect of pollution emergency emission reduction, emission source shutdown, and regional transport influence.

[0013] To achieve the above technical purposes, in another aspect, the present application discloses a dynamic decision-making device for ozone pollution prevention and control, which comprises: a control city and industry identification unit for observing and analyzing ozone pollution and its precursors, identifying the target control city and the target control industry for ozone pollution prevention and control; a start time determination unit for determining the control start time of the target control city according to the numerical prediction of air quality; a list compilation unit for compiling the atmospheric pollutant emission source list for the target control city and the target control industry; an emission source identification unit for identifying the emission regions and industries affecting the ozone pollution of the target control city according to the compiled atmospheric pollutant emission source list; a prevention and control measure library establishment unit for establishing a prevention and control measure library according to the statistical results of different emission industries in the atmospheric pollutant emission source list; a prevention and control measure formation unit for taking corresponding prevention and control measures for each identified emission region and industry affecting the ozone pollution of the target control city according to the prevention and control measure library; a prevention and control measure evaluation unit for air quality scenario simulation of the taken prevention and control measures, evaluating the effect of air quality scenario simulation by the reduction amount of each pollutant achieved by each prevention and control measure through the quantitative response relationship between the activity level and emission factor of the related pollution source and each prevention and control measure; and a prevention and control measure adjustment unit for adjusting the prevention and control measures according to the evaluation results of the effect of air quality scenario simulation by the reduction amount of each pollutant and the air quality standard to be achieved.

[0014] Further, for the dynamic decision-making device for ozone pollution prevention and control, the control city and industry identification unit is further used to utilize the data of the existing environmental air quality standard monitoring site of the city, supplement the observation data of ozone precursors by combining with fixed-point sampling analysis, analyze the temporal and spatial distribution characteristics of ozone and its precursors, and the chemical composition and reaction activity of the precursors, and identify the target control city and the target control industry for ozone pollution prevention and control.

[0015] Further, for the ozone pollution prevention and control dynamic decision device, the list compiling unit is further used for fusing a regional background list and a local city list.

[0016] To achieve the above technical purposes, in still another aspect, the present application discloses a computing device. The computing device comprises one or more processors, and a memory coupled to the one or more processors, the memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform the above method.

[0017] To achieve the above technical purposes, in still another aspect, the present application discloses a machine-readable storage medium. The machine-readable storage medium stores executable instructions, which, when executed, cause the machine to perform the above method.

[0018] The present application has the following beneficial effects:

[0019] The ozone pollution prevention and control dynamic decision method and device provided by the embodiment of the present application utilize a plurality of means such as stereoscopic monitoring, emission list, numerical simulation, and emergency emission reduction technology to perform multi-dimensional analysis on ozone pollution sources, quantitatively analyze the source contribution of local, surrounding areas, and pollution industries, and recognize the key causes of urban ozone pollution. Based on the emission list, a control measure library is established for other pollution sources such as industry, motor vehicle, and process, and an ozone pollution emergency emission reduction scheme is proposed according to the measure library to perform air quality scenario simulation, so as to obtain the emission reduction amount of volatile organic compounds (VOCs) and nitrogen oxides (NOx) that can effectively control the regional volatile organic compounds (VOCs) and nitrogen oxides (NOx), and assist in improving the environmental air quality.

[0020] Through the ozone pollution prevention and control dynamic decision technical route of "monitoring and early warning-ozone source analysis-decision evaluation-meeting the standard and performance", real-time analysis of atmospheric ozone sources and dynamic emission reduction of precursors are realized, and technical support and treatment scheme are provided for air quality management work. The measure library is established according to the emission list, and the measure library is continuously evaluated and optimized through the effect of scenario simulation. The local list and the regional list are fused to realize fine and rapid tracing of ozone and its precursors; multi-dimensional comprehensive source analysis of models, observations, and lists is realized to accurately determine the key emission areas and key pollution industries; rapid evaluation and dynamic optimization of ozone pollution meeting planning are realized. BRIEF DESCRIPTION OF DRAWINGS

[0021] In the drawings,

[0022] Figure 1 The flowchart of the ozone pollution prevention and control dynamic decision method provided by an embodiment of the present application is shown in the figure;

[0023] Figure 2A schematic diagram of a dynamic decision-making device for ozone pollution control provided in another embodiment of the present invention;

[0024] Figure 3 This is a structural block diagram of a computing device for dynamic decision processing in ozone pollution control according to an embodiment of the present invention. Detailed Implementation

[0025] The dynamic decision-making method and apparatus for ozone pollution prevention and control provided by the present invention will be explained and described in detail below with reference to the accompanying drawings.

[0026] Figure 1 This is a flowchart of a dynamic decision-making method for ozone pollution prevention and control provided in one embodiment of the present invention.

[0027] like Figure 1 As shown, in step S110, ozone pollution and its precursors are observed and analyzed to identify target cities and industries for ozone pollution control. As an optional implementation, data from existing urban ambient air quality monitoring stations, such as ozone and / or nitrogen oxides (NOx) detection data, can be combined with fixed-point sampling analysis to supplement ozone precursor component observation data. This allows for the analysis of the spatiotemporal distribution characteristics of ozone and its precursors, as well as the chemical composition and reactivity of the precursors, thus identifying target cities and industries for ozone pollution control. Data from various fixed-point observations at different times are aggregated to obtain the spatiotemporal distribution characteristics of ozone and its precursors. Based on historical data, such as the spatiotemporal distribution characteristics of ozone and its precursors, cities with ozone concentrations exceeding preset concentration values ​​can be selected as target cities for control. Target industries can be identified based on historical data, such as the chemical composition and reactivity of precursors, and industries corresponding to highly reactive precursors can be identified.

[0028] In step S120, the start time for ozone control measures in the target cities is determined based on air quality numerical forecasts. Air quality numerical forecasts can predict cities where ozone pollution is imminent, or the time when ozone pollution is expected in the target cities, allowing for advance control measures to be implemented in these cities.

[0029] At step S130, an atmospheric pollutant emission source list is compiled for the target control city and the target control industry. Compiling the atmospheric pollutant emission source list can include fusing a regional background list and a city local list, fusing lists of different resolutions into one, and providing input data for a pollution source contribution analysis. The atmospheric pollutant emission source list can be a high-resolution atmospheric pollutant emission source list. The atmospheric pollutant emission source list can be grid data on a map, and a high-resolution list has smaller grids and higher data resolution than a low-resolution list. The regional background list is generally across provinces and cities and has low resolution, and the city local list is local and has relatively high resolution.

[0030] As an optional implementation, the atmospheric pollutant emission source list can be compiled based on emission inventory research data of the target control city, existing atmospheric pollution source research results, and relevant data of other cities. On this basis, the atmospheric pollutant emission source list of the target control city can be compiled according to the emission inventory research data of the target control city and other cities, and the existing atmospheric pollution source research results. The emission inventory of other cities can be used as the above-mentioned regional background list.

[0031] As an optional implementation, after the atmospheric pollutant emission source list is compiled, the atmospheric pollutant emission source list can be updated whenever an update condition is met. Specifically, the atmospheric pollutant emission source list can be dynamically updated in terms of pollutant emission amount, spatio-temporal distribution, and chemical composition. The update condition can be an update period, or a manually triggered update instruction. The specific update condition is not limited in the embodiment. On this basis, the atmospheric pollutant emission source list can be dynamically updated to incorporate the latest atmospheric pollutant emission data in time and improve the accuracy of the atmospheric pollutant emission source list.

[0032] At step S140, an emission area and an emission industry affecting ozone pollution of the target control city are identified according to the compiled atmospheric pollutant emission source list. As an optional implementation, the emission area and the emission industry affecting ozone pollution of the target control city can be identified by quantitatively analyzing city ozone source contributions of different regions, different industry emission sources, and different time periods according to the compiled atmospheric pollutant emission source list. For example, the regions and industries can be selected according to the order of emission amount of volatile organic compounds (VOCs) in the atmospheric pollutant emission source list from large to small.

[0033] At step S150, a prevention and control measure library is established based on the statistical results of different emission industries in the atmospheric pollutant emission source inventory. Specifically, the prevention and control measure library can be established based on the high-resolution atmospheric pollutant emission source inventory, according to the statistical results of the emission source types such as industry, motor vehicle and / or process. The prevention and control measures can be established according to the statistical results of the atmospheric pollutant emission source inventory, which can represent the industrial sources of main pollutants, and then the prevention and control measure library can be developed according to the industrial experience, i.e., how to reduce emissions for each industry when pollution occurs. Among them, the prevention and control measures in the prevention and control measure library can be dynamically adjusted according to the local inventory and / or the emission reduction effect after the implementation of the prevention and control measures, so as to continuously optimize the prevention and control measure library.

[0034] At step S160, according to the prevention and control measure library, corresponding prevention and control measures are taken for each identified emission area and emission industry that affects the control of urban ozone pollution.

[0035] At step S170, air quality scenario simulation is performed on the taken prevention and control measures, and the effect of the air quality scenario simulation by the emission reduction amount of each pollutant achieved by each prevention and control measure is evaluated through the quantitative response relationship between each prevention and control measure and the activity level and emission factor of the related pollution source. As a specific example, according to the results of step S160, it can be determined what control measures should be taken for the ozone pollution area and the key emission industry, the formed emission reduction scheme is developed, and the effect of the air quality scenario simulation by the emission reduction amount of each pollutant is continuously evaluated through the quantitative response relationship between each measure and the activity level and emission factor of the related source.

[0036] As an optional implementation, the effect of the air quality scenario simulation includes one or more of the following: pollution emergency emission reduction, emission source shutdown, and simulation effect of regional transport influence. The corresponding prevention and control measures of pollution emergency emission reduction, emission source shutdown and / or regional transport influence can be determined to form a corresponding emission reduction scheme, and the prevention and control measures are simulated to obtain the corresponding simulation effect.

[0037] At step S180, according to the evaluation results of the effect of the air quality scenario simulation by the emission reduction amount of each pollutant, the prevention and control measures are adjusted in combination with the air quality standard to be reached. Among them, the air quality standard can be an ozone pollution compliance planning. As a specific example, a dynamic evaluation between the air quality scenario simulation and the compliance planning can be constructed, the feasibility of promoting air quality compliance according to the results of the prevention and control scheme is promoted, and at the same time, the compliance planning is brought into the control scheme and the emission reduction planning to carry out continuous tracking and evaluation, so as to realize the rapid evaluation and dynamic optimization of the overall ozone pollution compliance planning.

[0038] Figure 2The structural schematic diagram of the dynamic decision device for ozone pollution prevention and control provided for another embodiment of the present application is shown in FIG. 2. As shown in FIG. 2, the dynamic decision device for ozone pollution prevention and control 200 provided for this embodiment comprises a target city and industry identification unit 210, a start time determination unit 220, a list compilation unit 230, an emission source identification unit 240, a prevention and control measure library establishment unit 250, a prevention and control measure formation unit 260, a prevention and control measure evaluation unit 270, and a prevention and control measure adjustment unit 280. Figure 2

[0039] The target city and industry identification unit 210 is used for observing and analyzing ozone pollution and its precursors, and identifying the target city and industry for ozone pollution prevention and control. The operation of the target city and industry identification unit 210 can refer to the operation of step S110 described above with reference to FIG. 1. Figure 1

[0040] The start time determination unit 220 is used for determining the control start time of the target city according to the air quality numerical prediction. The operation of the start time determination unit 220 can refer to the operation of step S120 described above with reference to FIG. 1. Figure 1

[0041] The list compilation unit 230 is used for compiling the atmospheric pollutant emission source list for the target city and industry. The operation of the list compilation unit 230 can refer to the operation of step S130 described above with reference to FIG. 1. Figure 1

[0042] The emission source identification unit 240 is used for identifying the emission region and industry affecting the ozone pollution of the target city according to the compiled atmospheric pollutant emission source list. The operation of the emission source identification unit 230 can refer to the operation of step S140 described above with reference to FIG. 1. Figure 1

[0043] The prevention and control measure library establishment unit 250 is used for establishing the prevention and control measure library according to the statistical results of different emission industries in the atmospheric pollutant emission source list. The operation of the prevention and control measure library establishment unit 250 can refer to the operation of step S150 described above with reference to FIG. 1. Figure 1

[0044] The prevention and control measure formation unit 260 is used for taking corresponding prevention and control measures for each identified emission region and industry affecting the ozone pollution of the target city according to the prevention and control measure library. The operation of the prevention and control measure formation unit 260 can refer to the operation of step S160 described above with reference to FIG. 1. Figure 1

[0045] ​​​​​​​The prevention and control measure evaluation unit 270 is configured to simulate the air quality scenario by the prevention and control measures, and evaluate the effect of the reduction of each pollutant by each prevention and control measure on the air quality scenario simulation by the quantitative response relationship between each prevention and control measure and the activity level and emission factor of the related pollution source. The operation of the prevention and control measure evaluation unit 270 can refer to the operation of the prevention and control measure evaluation unit 270 described above with reference to Figure 1 The operation of step S170 is described above.

[0046] The prevention and control measure adjustment unit 280 is configured to adjust the prevention and control measures according to the evaluation result of the effect of the reduction of each pollutant on the air quality scenario simulation, in combination with the air quality standard to be achieved. The air quality standard can be the ozone pollution compliance planning. The operation of the prevention and control measure adjustment unit 280 can refer to the operation of the prevention and control measure adjustment unit 280 described above with reference to Figure 1 The operation of step S180 is described above.

[0047] As an optional implementation, the management and control city and industry identification unit 210 can be further configured to analyze the spatial and temporal distribution characteristics of ozone and its precursors, and the chemical composition and reaction activity of the precursors by using the data of the existing environmental air quality standard monitoring sites of the city, in combination with the fixed-point sampling analysis, supplementing the observation data of the ozone precursor components, to identify the target management and control city and the target management and control industry of the ozone pollution prevention and control.

[0048] As an optional implementation, the inventory compiling unit 230 can be further configured to fuse the regional background inventory and the local inventory of the city.

[0049] As an optional implementation, the emission source identification unit 240 can be further configured to quantitatively analyze the ozone source contribution of different regions, different industries, and different time periods of the city according to the compiled atmospheric pollutant emission source inventory, to identify the emission region and the emission industry affecting the ozone pollution of the target management and control city.

[0050] As an optional implementation, the inventory compiling unit 230 can be further configured to compile the atmospheric pollutant emission source inventory based on the emission inventory research data of the target management and control city, the existing atmospheric pollution source research results, and the related data of other cities.

[0051] As an optional implementation, the inventory compiling unit 230 can be further configured to dynamically update the emission amount, the spatial and temporal distribution, and the chemical composition of each pollutant in the atmospheric pollutant emission source inventory.

[0052] As an optional implementation, the effect of the air quality scenario simulation can include one or more of the following: the simulation effect of the pollution emergency reduction, the emission source shutdown, and the regional transportation influence.

[0053] The ozone pollution prevention and treatment dynamic decision method and device provided by the embodiment of the present application utilizes various means such as stereoscopic monitoring, emission inventory, numerical simulation and emergency emission reduction technology to perform multi-dimensional analysis on the ozone pollution sources, quantitatively analyzes the source contribution of local, surrounding areas and pollution industries, and recognizes the key causes of urban ozone pollution. Based on the emission inventory, the control measure library is established for other pollution sources such as industry, motor vehicle and process, and the ozone pollution emergency emission reduction scheme is proposed according to the measure library to perform air quality scenario simulation, so that the emission reduction amount of volatile organic compounds (VOCs) and nitrogen oxides (NOx) that can effectively control the regional ozone pollution is obtained, and the improvement of the environmental air quality is assisted.

[0054] The ozone pollution prevention and treatment dynamic decision method and device provided by the embodiment of the present application realizes the ozone pollution prevention and treatment dynamic decision technical route of “monitoring and early warning-ozone source analysis-decision evaluation-meeting the standard and performance”. The measure library is established according to the emission inventory, and the measure library is continuously evaluated and optimized through the effect of scenario simulation. The local inventory and the regional inventory are fused to realize the fine and rapid tracing of ozone and its precursors; the multi-dimensional comprehensive source analysis of the model, observation and inventory is realized to accurately determine the key emission areas and key pollution industries; the rapid evaluation and dynamic optimization of the ozone pollution meeting the standard planning are realized.

[0055] Figure 3 The structural block diagram of the computing device for ozone pollution prevention and treatment dynamic decision processing according to the embodiment of the present application.

[0056] As shown in Figure 3 , the computing device 300 can include at least one processor 310, a memory 320, a memory 330, a communication interface 340 and an internal bus 350, and the at least one processor 310, the memory 320, the memory 330 and the communication interface 340 are connected together via the bus 350. The at least one processor 310 executes at least one computer readable instruction (i.e., the above-mentioned element implemented in the form of software) stored or encoded in the computer readable storage medium (i.e., the memory 320).

[0057] In one embodiment, the computer executable instructions stored in the memory 320, when executed, cause the at least one processor 310 to perform: observing and analyzing ozone pollution and its precursors, identifying target control cities and target control industries for ozone pollution prevention and control; determining a control start time of the target control cities according to an air quality numerical prediction; compiling an atmospheric pollutant emission source list for the target control cities and the target control industries; establishing a prevention and control measure library according to the statistical results of different emission industries in the high-resolution atmospheric pollutant emission source list; taking corresponding prevention and control measures for each identified emission area and emission industry affecting the ozone pollution of the target control cities according to the prevention and control measure library; performing air quality scenario simulation on the taken prevention and control measures, evaluating the effect of the air quality scenario simulation by the emission reduction amount of each pollutant achieved by each prevention and control measure on the activity level and emission factor of the related pollution source; and adjusting the prevention and control measures according to the evaluation results of the effect of the air quality scenario simulation by the emission reduction amount of each pollutant, combined with the air quality standard to be reached.

[0058] It should be understood that the computer executable instructions stored in the memory 320, when executed, cause the at least one processor 310 to perform the various operations and functions described above in connection with various embodiments of the present disclosure. Figures 1-2

[0059] In the present disclosure, the computing device 300 can include, but is not limited to, a personal computer, a server computer, a workstation, a desktop computer, a laptop computer, a notebook computer, a mobile computing device, a smart phone, a tablet computer, a cellular phone, a personal digital assistant (PDA), a handheld device, a messaging device, a wearable computing device, a consumer electronic device, and the like.

[0060] According to one embodiment, a program product, such as a non-transitory machine-readable medium, is provided. The non-transitory machine-readable medium can have instructions (i.e., the above-described elements implemented in software) thereon that, when executed by a machine, cause the machine to perform the various operations and functions described above in connection with various embodiments of the present disclosure. Figures 1-2

[0061] In particular, a system or apparatus equipped with a readable storage medium on which a software program code implementing the functions of any of the above-described embodiments is stored, and a computer or processor of the system or apparatus reading out and executing the instructions stored in the readable storage medium can be provided.

[0062] In this case, the program code read from the readable medium itself can implement the functions of any of the above-described embodiments, and thus the machine-readable code and the readable storage medium storing the machine-readable code constitute a part of the present invention.​​

[0063] Embodiments of the readable storage medium include floppy disks, hard disks, optical disks (e.g., CD-ROMs, CD-Rs, CD-RWs, DVD-ROMs, DVD-RAMs, DVD-RWs, DVD-RWs), magnetic tapes, flash memories, and ROMs. The program code, in this instance, can be downloaded from a server computer, or a cloud, over a communication network.

[0064] The above merely provides the embodiment of the present application and is not intended to limit the protection scope of the claims of the present application. Any equivalent structure or equivalent process conversion made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, are also included in the protection scope of the claims of the present application.

Claims

1. A dynamic decision method for ozone pollution prevention, characterized in that, The method comprises the following steps: Observing and analyzing ozone pollution and its precursors, identifying target control cities and target control industries for ozone pollution prevention and control; According to the numerical prediction of air quality, the starting time of the target control city is determined; For the target control city and the target control industry, a list of atmospheric pollutant emission sources is prepared, which includes the fusion of regional background list and local city list; According to the prepared list of atmospheric pollutant emission sources, the emission areas and industries affecting the ozone pollution of the target control city are identified; According to the statistical results of different emission industries in the list of atmospheric pollutant emission sources, a prevention and control measure library is established; According to the prevention and control measure library, corresponding prevention and control measures are taken for each identified emission area and industry affecting the ozone pollution of the target control city; The effects of air quality scenario simulation of the taken prevention and control measures are simulated, and the effects of the reduction of each pollutant by each prevention and control measure on the air quality scenario simulation are evaluated through the quantitative response relationship between the activity level and emission factor of each pollution source and each prevention and control measure; According to the evaluation results of the effects of the reduction of each pollutant on the air quality scenario simulation, the prevention and control measures are adjusted in combination with the air quality standard to be reached.

2. The dynamic decision-making method for ozone pollution prevention and control according to claim 1, characterized in that, Observing and analyzing urban ozone pollution and its precursors, identifying target control cities and target control industries for ozone pollution prevention and control, comprising: Using the data of the existing environmental air quality standard monitoring sites in the city, combining with the fixed-point sampling analysis, supplementing the observation data of ozone precursor components, analyzing the temporal and spatial distribution characteristics of ozone and its precursors, as well as the chemical composition and reaction activity of the precursors, and identifying the target control cities and target control industries for ozone pollution prevention and control.

3. The dynamic decision-making method for ozone pollution prevention according to claim 1, characterized in that, According to the prepared list of atmospheric pollutant emission sources, the emission areas and industries affecting the ozone pollution of the target control city are identified, comprising: According to the prepared list of atmospheric pollutant emission sources, the source contribution of urban ozone in different regions, different industries, and different time periods is quantitatively analyzed, and the emission areas and industries affecting the ozone pollution of the target control city are identified.

4. The dynamic decision-making method for ozone pollution prevention and control according to claim 1, characterized in that, The air quality standard is the ozone pollution standard planning.

5. The dynamic decision-making method for ozone pollution prevention according to claim 1, characterized in that, Preparing the list of atmospheric pollutant emission sources, including preparing the list of atmospheric pollutant emission sources based on the emission inventory research data of the target control city, the existing atmospheric pollution source research results, and the relevant data of other cities.

6. The dynamic decision-making method for ozone pollution prevention according to claim 1, characterized in that, Also including: Dynamically updating the emission amount, temporal and spatial distribution, and chemical composition of each pollutant in the list of atmospheric pollutant emission sources.

7. The dynamic decision-making method for ozone pollution prevention according to claim 1, characterized in that, The effects of air quality scenario simulation include one or more of the following: simulation effects of pollution emergency emission reduction, emission source shutdown, and regional transportation impact.

8. A dynamic decision device for ozone pollution prevention and control, characterized in that, Comprising: A control city and industry identification unit for observing and analyzing ozone pollution and its precursors, identifying target control cities and target control industries for ozone pollution prevention and control; A starting time determination unit for determining the starting time of the target control city according to the numerical prediction of air quality; The list compiling unit is configured to compile an atmospheric pollutant emission source list for the target control city and the target control industry, and the compiling of the atmospheric pollutant emission source list comprises: fusing a regional background list and a city local list; The emission source identifying unit is configured to identify emission regions and emission industries affecting ozone pollution of the target control city according to the compiled atmospheric pollutant emission source list; The prevention and control measure library establishing unit is configured to establish a prevention and control measure library according to statistical results of different emission industries in the atmospheric pollutant emission source list; The prevention and control measure forming unit is configured to take corresponding prevention and control measures for each of the identified emission regions and emission industries affecting ozone pollution of the target control city according to the prevention and control measure library; The prevention and control measure evaluating unit is configured to perform air quality scenario simulation on the taken prevention and control measures, and evaluate effects of the air quality scenario simulation by the reduction amount of each pollutant achieved by each prevention and control measure on the activity level and the emission factor of the related pollution source; The prevention and control measure adjusting unit is configured to adjust the prevention and control measures according to the evaluation results of the effects of the air quality scenario simulation by the reduction amount of each pollutant and in combination with the air quality standard to be achieved.

9. The dynamic decision device for ozone pollution prevention and control according to claim 8, characterized in that, The control city and industry identifying unit is further configured to analyze the spatial and temporal distribution characteristics of ozone and its precursors, the chemical composition of the precursors and the reaction activity by using data of existing environmental air quality standard monitoring sites of the city, supplementing observation data of ozone precursors by point sampling analysis, and identifying the target control city and the target control industry for ozone pollution prevention and control.

10. A computing device, comprising: comprise: one or more processors, and a memory coupled to the one or more processors storing instructions that, when executed by the one or more processors, cause the one or more processors to perform the method of any one of claims 1 to 7.

11. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores executable instructions that, when executed, cause the machine to perform the method of any one of claims 1 to 7.

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