CN113011777A - Dynamic decision-making method and device for preventing and treating ozone pollution - Google Patents
Dynamic decision-making method and device for preventing and treating ozone pollution Download PDFInfo
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Abstract
The invention discloses a dynamic decision-making method and a dynamic decision-making device for preventing and treating ozone pollution. The method and the device realize the technical route of monitoring and early warning, ozone source analysis, decision evaluation, standard reaching and performance ozone pollution prevention and control dynamic decision. And establishing a measure library according to the emission list, and continuously evaluating and optimizing the measure library through the effect of scene simulation. Fusing the local list and the regional list to realize the refined and rapid tracing of the ozone and the precursor thereof; multi-dimensional comprehensive source analysis of models, observation and lists is realized, and key emission areas and key pollution industries are accurately judged; and the rapid evaluation and dynamic optimization of the ozone pollution standard-reaching plan are realized. The method is characterized in that the ozone pollution source is subjected to multidimensional analysis by using various means such as three-dimensional monitoring, emission lists, numerical simulation, emergency emission reduction technologies and the like, the source contribution of local and peripheral areas and pollution industries is quantitatively analyzed, and the key cause of urban ozone pollution is identified.
Description
Technical Field
The invention relates to air pollution forecasting and prevention technology, in particular to a dynamic decision method and a dynamic decision device for ozone pollution prevention and treatment.
Background
Ozone, one of the main components of urban photochemical smog, is an oxidation product of primary pollutants through a series of photochemical reactions, the generation of which leads to the enhancement of atmospheric oxidation and is accompanied by PM2.5The following secondary fine particulate contamination. The formation and change of near-surface ozone are mainly related to the emission of nitrogen oxides (NOx), Volatile Organic Compounds (VOCs) and other precursor pollutants emitted by human activities, including the emission of automobile exhaust, the emission generated by industrial processes such as petrochemical industry, material synthesis and the like, the emission generated by fuel combustion of power plants and other industrial enterprises, the emission generated by biomass combustion and the like.
With the rapid development of Chinese economy and the continuous acceleration of urbanization process, fine Particulate Matters (PM) are contained in the atmosphere2.5) And ozone (O)3) The composite pollution represented by the pollution is serious, and urban dust-haze pollution and photochemical pollution gradually become hot spots concerned by all communities. Urban ozone pollution has become one of the key factors restricting the improvement and the achievement of air quality, but ozone is taken as a typical secondary pollutant, and the reaction process and the generation mechanism are very complicated, especially notThe economic industry structure and the emission characteristics of the same region are different, and the reaction mechanism and the corresponding control strategy related to the ozone are not systematically researched.
Disclosure of Invention
The invention innovatively provides a dynamic decision-making method and a dynamic decision-making device for preventing and treating ozone pollution, which utilize multiple means such as three-dimensional monitoring, emission lists, numerical simulation, emergency emission reduction technology and the like to carry out multidimensional analysis on ozone pollution sources, quantitatively analyze the source contributions of local areas, peripheral areas and pollution industries and identify the key cause of urban ozone pollution.
In order to achieve the technical purpose, the invention discloses a dynamic decision method for preventing and controlling ozone pollution. The dynamic decision method for preventing and controlling the ozone pollution comprises the following steps: observing and analyzing the ozone pollution and the precursor thereof, and identifying a target control city and a target control industry for controlling the ozone pollution; determining the control starting time of a target control city according to the air quality numerical forecast; compiling a list of atmospheric pollutant emission sources for the target control city and the target control industry; identifying an emission area and an emission industry which influence the target control of urban ozone pollution according to the compiled list of the atmospheric pollutant emission sources; establishing a prevention and control measure library aiming at the statistical results of different emission industries in the atmospheric pollutant emission source list; according to the prevention and control measure library, corresponding prevention and control measures are taken for each identified emission area and emission industry which influence targets to control the urban ozone pollution; carrying out air quality scene simulation on the adopted prevention and treatment measures, and evaluating the effect of the reduction of the discharge volume of each pollutant obtained by each prevention and treatment measure on the air quality scene simulation through the quantitative response relation between each prevention and treatment measure and the activity level and the discharge factor of the related pollution source; and adjusting prevention measures according to the evaluation result of the effect of the reduced discharge of each pollutant on the air quality scene simulation and the air quality standard to be achieved.
Further, for the dynamic decision method for ozone pollution control, urban ozone pollution and precursors thereof are observed and analyzed, and target control cities and target control industries for ozone pollution control are identified, and the method comprises the following steps: the method is characterized in that the existing data of the urban environmental air quality standard monitoring station is utilized, fixed-point sampling analysis is combined, observation data of ozone precursor components are supplemented, the space-time distribution characteristics of ozone and precursors thereof, the chemical components and the reaction activity of the precursors are analyzed, and a target control city and a target control industry for preventing and treating ozone pollution are identified.
Further, for the dynamic decision method for ozone pollution control, compiling the atmospheric pollutant emission source list comprises fusing a regional background list and a city local list.
Further, for the dynamic decision method for preventing and controlling ozone pollution, according to the compiled list of the atmospheric pollutant emission sources, the emission area and the emission industry which affect the target control city ozone pollution are identified, and the method comprises the following steps: according to the compiled list of the atmospheric pollutant emission sources, urban ozone source contributions of different areas, different industry emission sources and different time periods are quantitatively analyzed, and emission areas and emission industries influencing target control urban ozone pollution are identified.
Further, for the dynamic decision method for preventing and treating ozone pollution, the air quality standard is the ozone pollution standard-reaching plan.
In order to achieve the above technical object, in another aspect, the present invention discloses a dynamic decision device for ozone pollution control, comprising: the control city and industry identification unit is used for observing and analyzing the ozone pollution and the precursor thereof and identifying a target control city and a target control industry for controlling the ozone pollution; the starting time determining unit is used for determining the control starting time of the target control city according to the forecast of the air quality value; the list compiling unit is used for compiling an atmospheric pollutant emission source list for the target control city and the target control industry; the emission source identification unit is used for identifying an emission area and an emission industry which influence the target control city ozone pollution according to the compiled atmospheric pollutant emission source list; the control measure library establishing unit is used for establishing a control measure library aiming at the statistical results of different emission industries in the atmospheric pollutant emission source list; the control measure forming unit is used for taking corresponding control measures according to the control measure library for controlling the urban ozone pollution emission area and the emission industry of each identified influence target; the control measure evaluation unit is used for carrying out air quality scene simulation on the adopted control measures and evaluating the effect of the reduction of the discharge volume of each pollutant obtained by each control measure on the air quality scene simulation through the quantitative response relation between each control measure and the activity level and the discharge factor of the relevant pollution source; and the control measure adjusting unit is used for adjusting control measures according to the evaluation result of the simulation effect of the reduced discharge volume of each pollutant on the air quality scene and in combination with the air quality standard to be achieved.
Further, for the dynamic decision-making device for preventing and treating ozone pollution, the control city and industry identification unit is further used for utilizing data of an existing environment air quality standard monitoring station of a city, combining fixed-point sampling analysis, supplementing observation data of components of ozone precursors, analyzing space-time distribution characteristics of ozone and the precursors thereof, chemical components and reaction activity of the precursors, and identifying a target control city and a target control industry for preventing and treating ozone pollution.
Further, for the dynamic decision device for ozone pollution control, the list making unit is further used for fusing an area background list and a city local list.
To achieve the above technical object, in yet another aspect, the present invention discloses a computing device. The computing device includes: one or more processors, and a memory coupled with 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-described method.
To achieve the above technical objects, in yet another aspect, the present invention discloses a machine-readable storage medium. The machine-readable storage medium stores executable instructions that, when executed, cause the machine to perform the above-described method.
The invention has the beneficial effects that:
the dynamic decision-making method and the device for preventing and treating ozone pollution provided by the embodiment of the invention utilize multiple means such as three-dimensional monitoring, emission lists, numerical simulation, emergency emission reduction technologies and the like to carry out multi-dimensional analysis on the ozone pollution source, quantitatively analyze the source contribution of local areas, peripheral areas and pollution industries, and identify the key cause of ozone pollution in cities. On the basis of an emission list, a control measure library is established for other pollution sources such as industry, motor vehicles, technological processes and the like, and air quality scene simulation is carried out according to a scheme of ozone pollution emergency emission reduction provided by the measure library, so that the emission reduction amount of Volatile Organic Compounds (VOCs) and nitrogen oxides (NOx) in a region can be effectively controlled, and the improvement of the air quality of the environment is assisted.
The real-time analysis of the atmospheric ozone source and the dynamic emission reduction of the precursor are realized through an ozone pollution control dynamic decision technical route of 'monitoring and early warning-ozone source analysis-decision evaluation-standard and performance', and a technical support and treatment scheme is provided for the air quality management work. And establishing a measure library according to the emission list, and continuously evaluating and optimizing the measure library through the effect of scene simulation. Fusing the local list and the regional list to realize the refined and rapid tracing of the ozone and the precursor thereof; multi-dimensional comprehensive source analysis of models, observation and lists is realized, and key emission areas and key pollution industries are accurately judged; and the rapid evaluation and dynamic optimization of the ozone pollution standard-reaching plan are realized.
Drawings
In the figure, the position of the upper end of the main shaft,
FIG. 1 is a flow chart of a dynamic decision method for ozone pollution control according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a dynamic decision-making device for ozone pollution control according to another embodiment of the present invention;
fig. 3 is a block diagram of a computing device for dynamic decision-making process for ozone pollution control according to an embodiment of the present invention.
Detailed Description
The dynamic decision method and device for preventing and treating ozone pollution provided by the invention are explained and explained in detail in the following by combining the attached drawings of the specification.
Fig. 1 is a flowchart of a dynamic decision method for ozone pollution control according to an embodiment of the present invention.
As shown in fig. 1, in step S110, the ozone pollution and the precursor thereof are observed and analyzed, and a target control city and a target control industry for controlling the ozone pollution are identified. As an alternative embodiment, data of existing environmental air quality standard monitoring sites in cities, such as detection data of ozone and/or nitrogen oxides (NOx), etc., may be utilized, and combined with fixed-point sampling analysis, observation data of ozone precursor components are supplemented, spatial and temporal distribution characteristics of ozone and its precursors, and chemical components and reactivity of the precursors are analyzed, and a target control city and a target control industry for ozone pollution prevention and treatment are identified. The data observed at different times at each set point are collected to obtain the space-time distribution characteristics of the ozone and the precursor thereof. According to historical data such as the space-time distribution characteristics of ozone and precursors thereof, a pollution source city with the concentration of the discharged ozone exceeding a preset concentration value can be selected as a target control city. The target control industry can be identified according to historical data such as chemical components and reactivity of the precursor, and the corresponding industry can be found according to the precursor with high reactivity.
In step S120, a management and control start time of the target management and control city is determined according to the air quality numerical forecast. The air quality numerical prediction can predict the city about to generate ozone pollution or predict the time of the target control city about to generate ozone pollution, so that the target control city about to generate ozone pollution is controlled in advance.
In step S130, an atmospheric pollutant emission source list is compiled for the target control city and the target control industry. Compiling the list of the atmospheric pollutant emission sources may include merging the regional background list with the city local list, merging the lists with different resolutions into one, and providing input data for the analysis of the contribution of the pollution sources. Wherein the list of atmospheric pollutant emission sources may be a high resolution list of atmospheric pollutant emission sources. The list of atmospheric pollutant emission sources may be grid data on a map, the grid being smaller for high resolution lists than for low resolution lists, the resolution of the data being high. The regional background list generally spans provinces and cities, and the resolution is not high; the city local list is local and the resolution is relatively high.
In step S140, according to the compiled list of the atmospheric pollutant emission sources, the emission areas and the emission industries affecting the target control city ozone pollution are identified. As an alternative implementation, the urban ozone source contributions of different areas, different industry emission sources and different time periods can be quantitatively analyzed according to the compiled list of the atmospheric pollutant emission sources, and the emission areas and the emission industries affecting the target management and control of the urban ozone pollution are identified. For example, the areas and industries with the emission amounts positioned in the first few positions can be respectively selected according to the areas and industries with the emission amounts of Volatile Organic Compounds (VOCs) in the list of the atmospheric pollutant emission sources ranked from large to small.
In step S150, a prevention and control measure library is established for the statistical results of different emission industries in the atmospheric pollutant emission source list. Specifically, a prevention and control measure library can be established on the basis of a high-resolution list of the atmospheric pollutant emission sources according to the statistical results of the list of the types of the emission sources of industry, motor vehicles, process processes and the like. The prevention and treatment measures can be established according to the statistical results of the atmospheric pollutant emission source list, the statistical results of the atmospheric pollutant emission source list can represent the industry sources of the main pollutants, and a prevention and treatment measure library can be established by combining industry experience, namely how to reduce emission of each industry when pollution occurs. Wherein the control measures in the control measure library can be dynamically adjusted according to the local list and/or the emission reduction effect after the control measures are executed, so as to continuously optimize the control measure library.
In step S160, according to the prevention and treatment measure library, corresponding prevention and treatment measures are taken for each identified emission area and emission industry where the influence targets control the urban ozone pollution.
In step S170, air quality scenario simulation is performed on the adopted prevention measures, and the effect of the reduced volume of each pollutant obtained by each prevention measure on the air quality scenario simulation is evaluated through the quantitative response relationship between each prevention measure and the activity level and emission factor of the relevant pollution source. As a specific example, it can be determined what control measures should be taken by the ozone-polluted area and the important emission industry according to the result of step S160, and an air quality scenario simulation is performed on the formed emission reduction scheme, and the effect of the emission reduction amount of each pollutant on the air quality scenario simulation is continuously evaluated through the quantitative response relationship between each measure and the activity level and the emission factor of the relevant source.
In step S180, the prevention measure is adjusted according to the evaluation result of the effect of the reduced volume of each pollutant on the air quality scene simulation in combination with the air quality standard to be achieved. Wherein, the air quality standard can be the ozone pollution standard planning. As a specific example, dynamic evaluation between air quality scene simulation and standard reaching planning can be constructed, feasibility of air quality standard reaching is promoted according to the result of a prevention and control scheme, meanwhile, the standard reaching planning is brought into a control scheme and an emission reduction planning, continuous tracking and evaluation are carried out, and overall quick evaluation and dynamic optimization of the ozone pollution standard reaching planning are realized.
Fig. 2 is a schematic structural diagram of a dynamic decision-making device for ozone pollution control according to another embodiment of the present invention. As shown in fig. 2, the dynamic decision device 200 for ozone pollution control provided by this embodiment includes a city management and control recognition unit 210, a start time determination unit 220, a list making unit 230, an emission source recognition unit 240, a control measure library establishment unit 250, a control measure forming unit 260, a control measure evaluation unit 270, and a control measure adjustment unit 280.
The control city and industry recognition unit 210 is used for observing and analyzing the ozone pollution and the precursor thereof, and recognizing a target control city and a target control industry for controlling the ozone pollution. The operation of the governing city recognizing unit 210 may refer to the operation of step S110 described above with reference to fig. 1.
And the starting time determining unit 220 is configured to determine a control starting time of the target control city according to the air quality numerical forecast. The operation of the activation time determination unit 220 may refer to the operation of step S120 described above with reference to fig. 1.
The list compiling unit 230 is used for compiling the list of the atmospheric pollutant emission source for the target control city and the target control industry. The operation of the list making unit 230 may refer to the operation of step S130 described above with reference to fig. 1.
The emission source identification unit 240 is configured to identify an emission area and an emission industry that affect the target controlled urban ozone pollution according to the compiled list of atmospheric pollutant emission sources. The operation of the emission source identification unit 230 may refer to the operation of step S140 described above with reference to fig. 1.
The control measure library establishing unit 250 is configured to establish a control measure library according to statistical results of different emission industries in the atmospheric pollutant emission source list. The operation of the control measure library establishing unit 250 may refer to the operation of step S150 described above with reference to fig. 1.
The preventive measure forming unit 260 is configured to take corresponding preventive measures for each identified influence target to control the discharge area and discharge industry of the urban ozone pollution according to the preventive measure library. The operation of the preventive measure forming unit 260 may refer to the operation of step S160 described above with reference to fig. 1.
The preventive measure evaluation unit 270 is configured to perform air quality scenario simulation on the adopted preventive measures, and evaluate the effect of the reduced volume of each pollutant obtained by each preventive measure on the air quality scenario simulation through the quantitative response relationship between each preventive measure and the activity level and the emission factor of the relevant pollution source. The operation of the preventive measure evaluation unit 270 may refer to the operation of step S170 described above with reference to fig. 1.
The preventive measure adjustment unit 280 is configured to adjust a preventive measure according to an evaluation result of an effect of the reduced volume of each pollutant on the air quality scenario simulation in combination with an air quality standard to be achieved. Wherein, the air quality standard can be the ozone pollution standard planning. The operation of the preventive measure adjustment unit 280 may refer to the operation of step S180 described above with reference to fig. 1.
As an optional implementation, the city management and control and industry identification unit 210 may be further configured to utilize data of an existing ambient air quality standard monitoring site of a city, combine fixed-point sampling analysis, supplement observation data of ozone precursor components, analyze spatial-temporal distribution characteristics of ozone and precursors thereof, and chemical components and reactivity of the precursors, and identify a target city management and control and a target industry management and control for ozone pollution prevention and control.
As an alternative embodiment, the list preparation unit 230 may be further configured to merge the regional background list and the city local list.
As an alternative embodiment, the emission source identification unit 240 may be further configured to quantitatively analyze the urban ozone source contributions of different regions, different industry emission sources, and different time periods according to the compiled list of the atmospheric pollutant emission sources, and identify the emission region and the emission industry that affect the target to control the urban ozone pollution.
The dynamic decision-making method and the device for preventing and treating ozone pollution provided by the embodiment of the invention utilize multiple means such as three-dimensional monitoring, emission lists, numerical simulation, emergency emission reduction technologies and the like to carry out multi-dimensional analysis on the ozone pollution source, quantitatively analyze the source contribution of local areas, peripheral areas and pollution industries, and identify the key cause of ozone pollution in cities. On the basis of an emission list, a control measure library is established for other pollution sources such as industry, motor vehicles, technological processes and the like, and air quality scene simulation is carried out according to a scheme of ozone pollution emergency emission reduction provided by the measure library, so that the emission reduction amount of Volatile Organic Compounds (VOCs) and nitrogen oxides (NOx) in a region can be effectively controlled, and the improvement of the air quality of the environment is assisted.
The dynamic decision method and the device for preventing and treating the ozone pollution provided by the embodiment of the invention realize the technical route of dynamic decision of preventing and treating the ozone pollution, including monitoring and early warning, ozone source analysis, decision evaluation, standard reaching and performance. And establishing a measure library according to the emission list, and continuously evaluating and optimizing the measure library through the effect of scene simulation. Fusing the local list and the regional list to realize the refined and rapid tracing of the ozone and the precursor thereof; multi-dimensional comprehensive source analysis of models, observation and lists is realized, and key emission areas and key pollution industries are accurately judged; and the rapid evaluation and dynamic optimization of the ozone pollution standard-reaching plan are realized.
Fig. 3 is a block diagram of a computing device for dynamic decision-making process for ozone pollution control according to an embodiment of the present invention.
As shown in fig. 3, computing device 300 may include at least one processor 310, storage 320, memory 330, communication interface 340, and internal bus 350, and at least one processor 310, storage 320, memory 330, and communication interface 340 are connected together via bus 350. The at least one processor 310 executes at least one computer-readable instruction (i.e., an element described above as being implemented in software) stored or encoded in a computer-readable storage medium (i.e., memory 320).
In one embodiment, stored in the memory 320 are computer-executable instructions that, when executed, cause the at least one processor 310 to perform: observing and analyzing the ozone pollution and the precursor thereof, and identifying a target control city and a target control industry for controlling the ozone pollution; determining the control starting time of a target control city according to the air quality numerical forecast; compiling a list of atmospheric pollutant emission sources for the target control city and the target control industry; establishing a prevention and control measure library aiming at the statistical results of different emission industries in the high-resolution atmospheric pollutant emission source list; according to the prevention and control measure library, corresponding prevention and control measures are taken for each identified emission area and emission industry which influence targets to control the urban ozone pollution; carrying out air quality scene simulation on the adopted prevention and treatment measures, and evaluating the effect of the reduction of the discharge volume of each pollutant obtained by each prevention and treatment measure on the air quality scene simulation through the quantitative response relation between each prevention and treatment measure and the activity level and the discharge factor of the related pollution source; and adjusting prevention measures according to the evaluation result of the effect of the reduced discharge of each pollutant on the air quality scene simulation and the air quality standard to be achieved. .
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 fig. 1-2 in the various embodiments of the present disclosure.
In the present disclosure, computing device 300 may include, but is not limited to: personal computers, server computers, workstations, desktop computers, laptop computers, notebook computers, mobile computing devices, smart phones, tablet computers, cellular phones, Personal Digital Assistants (PDAs), handheld devices, messaging devices, wearable computing devices, consumer electronics, and so forth.
According to one embodiment, a program product, such as a non-transitory machine-readable medium, is provided. A non-transitory machine-readable medium may have instructions (i.e., elements described above as being implemented in software) that, when executed by a machine, cause the machine to perform various operations and functions described above in connection with fig. 1-2 in various embodiments of the disclosure.
Specifically, a system or apparatus may be provided which is provided with a readable storage medium on which software program code implementing the functions of any of the above embodiments is stored, and causes a computer or processor of the system or apparatus to read out and execute instructions stored in the readable storage medium.
In this case, the program code itself read from the readable medium can realize 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 form part of the present invention.
Examples of the readable storage medium include floppy disks, hard disks, magneto-optical disks, optical disks (e.g., CD-ROMs, CD-R, CD-RWs, DVD-ROMs, DVD-RAMs, DVD-RWs), magnetic tapes, nonvolatile memory cards, and ROMs. Alternatively, the program code may be downloaded from a server computer or from the cloud via a communications network.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the claims, and all equivalent structures or equivalent processes that are transformed by the content of the specification and the drawings, or directly or indirectly applied to other related technical fields are included in the scope of the claims.
Claims (10)
1. A dynamic decision-making method for preventing and controlling ozone pollution is characterized by comprising the following steps:
observing and analyzing the ozone pollution and the precursor thereof, and identifying a target control city and a target control industry for controlling the ozone pollution;
determining the control starting time of a target control city according to the air quality numerical forecast;
compiling a list of atmospheric pollutant emission sources for the target control city and the target control industry;
identifying an emission area and an emission industry which influence the target control of urban ozone pollution according to the compiled list of the atmospheric pollutant emission sources;
establishing a prevention and control measure library aiming at the statistical results of different emission industries in the atmospheric pollutant emission source list;
according to the prevention and control measure library, corresponding prevention and control measures are taken for each identified emission area and emission industry which influence targets to control the urban ozone pollution;
carrying out air quality scene simulation on the adopted prevention and treatment measures, and evaluating the effect of the reduction of the discharge volume of each pollutant obtained by each prevention and treatment measure on the air quality scene simulation through the quantitative response relation between each prevention and treatment measure and the activity level and the discharge factor of the related pollution source;
and adjusting prevention measures according to the evaluation result of the effect of the reduced discharge of each pollutant on the air quality scene simulation and the air quality standard to be achieved.
2. The dynamic decision method for ozone pollution control as claimed in claim 1, wherein the urban ozone pollution and its precursors are observed and analyzed to identify the target control city and target control industry for ozone pollution control, comprising:
the method is characterized in that the existing data of the urban environmental air quality standard monitoring station is utilized, fixed-point sampling analysis is combined, observation data of ozone precursor components are supplemented, the space-time distribution characteristics of ozone and precursors thereof, the chemical components and the reaction activity of the precursors are analyzed, and a target control city and a target control industry for preventing and treating ozone pollution are identified.
3. The dynamic decision-making method for ozone pollution control as claimed in claim 1, wherein compiling a list of atmospheric pollutant emission sources includes fusing a regional background list and a city local list.
4. The dynamic decision method for ozone pollution control according to claim 1, wherein identifying the emission area and emission industry affecting the target control city ozone pollution according to the compiled list of atmospheric pollutant emission sources comprises:
according to the compiled list of the atmospheric pollutant emission sources, urban ozone source contributions of different areas, different industry emission sources and different time periods are quantitatively analyzed, and emission areas and emission industries influencing target control urban ozone pollution are identified.
5. The dynamic decision-making method for ozone pollution control according to claim 1, wherein the air quality standard is ozone pollution compliance program.
6. A dynamic decision-making device for preventing and treating ozone pollution is characterized by comprising:
the control city and industry identification unit is used for observing and analyzing the ozone pollution and the precursor thereof and identifying a target control city and a target control industry for controlling the ozone pollution;
the starting time determining unit is used for determining the control starting time of the target control city according to the forecast of the air quality value;
the list compiling unit is used for compiling an atmospheric pollutant emission source list for the target control city and the target control industry;
the emission source identification unit is used for identifying an emission area and an emission industry which influence the target control city ozone pollution according to the compiled atmospheric pollutant emission source list;
the control measure library establishing unit is used for establishing a control measure library aiming at the statistical results of different emission industries in the atmospheric pollutant emission source list;
the control measure forming unit is used for taking corresponding control measures according to the control measure library for controlling the urban ozone pollution emission area and the emission industry of each identified influence target;
the control measure evaluation unit is used for carrying out air quality scene simulation on the adopted control measures and evaluating the effect of the reduction of the discharge volume of each pollutant obtained by each control measure on the air quality scene simulation through the quantitative response relation between each control measure and the activity level and the discharge factor of the relevant pollution source;
and the control measure adjusting unit is used for adjusting control measures according to the evaluation result of the simulation effect of the reduced discharge volume of each pollutant on the air quality scene and in combination with the air quality standard to be achieved.
7. The dynamic decision-making device for ozone pollution control according to claim 6, wherein the city management and control and industry recognition unit is further configured to utilize data of existing ambient air quality standard monitoring sites in cities, combine fixed-point sampling analysis, supplement observation data of ozone precursor components, analyze space-time distribution characteristics of ozone and precursors thereof, and chemical components and reactivity of the precursors, and recognize target city management and control and industry of ozone pollution control.
8. The dynamic decision-making device for ozone pollution control according to claim 6, wherein said list making unit is further configured to merge a regional background list with a city local list.
9. A computing device, comprising:
one or more processors, and
a memory coupled with 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 method of any of claims 1-5.
10. A machine-readable storage medium having stored thereon executable instructions that, when executed, cause the machine to perform the method of any one of claims 1 to 5.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
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| CN202110373543.8A CN113011777A (en) | 2021-04-07 | 2021-04-07 | Dynamic decision-making method and device for preventing and treating ozone pollution |
| CN202210289898.3A CN114707831A (en) | 2021-04-07 | 2022-03-23 | Dynamic decision-making method and device for preventing and treating ozone pollution |
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| CN113672873A (en) * | 2021-08-25 | 2021-11-19 | 中科三清科技有限公司 | Pollutant source analysis method and device, electronic equipment and storage medium |
| CN114218751A (en) * | 2021-11-16 | 2022-03-22 | 中科三清科技有限公司 | Quantitative evaluation method and device for ozone pollution, computer equipment and storage medium |
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| CN113672873B (en) * | 2021-08-25 | 2022-05-13 | 中科三清科技有限公司 | Pollutant source analysis method and device, electronic equipment and storage medium |
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| CN114218976A (en) * | 2021-09-27 | 2022-03-22 | 中国环境科学研究院 | Method and system for tracing and positioning fine emission source in ozone precursor high-value area |
| CN114218751A (en) * | 2021-11-16 | 2022-03-22 | 中科三清科技有限公司 | Quantitative evaluation method and device for ozone pollution, computer equipment and storage medium |
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| CN115238658B (en) * | 2022-09-22 | 2023-01-31 | 中科三清科技有限公司 | Data processing method and device, storage medium and electronic equipment |
| CN115526525A (en) * | 2022-10-18 | 2022-12-27 | 中科三清科技有限公司 | Ozone pollution treatment method, device, equipment and storage medium |
| CN117195585A (en) * | 2023-09-27 | 2023-12-08 | 重庆市生态环境科学研究院 | Atmospheric multi-pollutant emission reduction optimization regulation method and system based on dynamic scene simulation |
| CN117408856A (en) * | 2023-12-13 | 2024-01-16 | 浙江省生态环境监测中心(浙江省生态环境信息中心) | Pollutant tracing method and device, storage medium and electronic equipment |
| CN117408856B (en) * | 2023-12-13 | 2024-03-26 | 浙江省生态环境监测中心(浙江省生态环境信息中心) | Pollutant tracing method and device, storage medium and electronic equipment |
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