CN114757429A - PM (particulate matter)2.5Concentration target acquisition method and device - Google Patents
PM (particulate matter)2.5Concentration target acquisition method and device Download PDFInfo
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Abstract
The application discloses a PM2.5A concentration target obtaining method and a device belong to the technical field of environmental protection and atmospheric pollution prevention and control. The PM2.5The concentration target acquisition method comprises the following steps: acquiring a plurality of target year scene information; calculating a target year concentration predicted value under at least one target year scene information; constructing an atmospheric pollution source emission list under at least one target year scene information; respectively performing model simulation by using the atmospheric pollution source emission list under the situation information of each target year, so as to obtain a simulated concentration predicted value corresponding to the atmospheric pollution source emission list under the situation information of each target year; fusing the target annual concentration predicted value and the simulated concentration predicted value to obtain PM2.5And (4) concentration target. PM of the present application2.5Compared with the prior art, the concentration target acquisition method is moreObjectively and separately considering the deviation possibly caused by the calculation method and the deviation caused by the simulation method, thereby obtaining the most suitable PM2.5And (4) concentration target.
Description
Technical Field
The application relates to the technical field of environmental protection and atmosphere pollution prevention and control, in particular to PM 2.5Concentration target acquisition method and PM2.5And a concentration target acquisition device.
Background
Atmospheric pollution control is a long and arduous task.
At present, for PM in China2.5The concentration drop target is basically established based on PM2.5The annual average concentration reduction amplitude obtained by calculating the historical concentration is designed into PM according to the standard exceeding rate2.5Improving the concentration of PM by calculation2.5Concentration target, lack of atmospheric pollution control measures on PM2.5Quantitative evaluation of the effect of concentration reduction, and further failure to judge PM2.5Rationality of concentration targets.
Accordingly, a technical solution is desired to overcome or at least alleviate at least one of the above-mentioned drawbacks of the prior art.
Disclosure of Invention
The invention aims to provide a PM2.5A concentration target acquisition method overcomes or at least alleviates at least one of the above-mentioned deficiencies of the prior art.
In one aspect of the present invention, a PM is provided2.5Concentration target acquisition method, the PM2.5The concentration target acquisition method comprises the following steps:
acquiring a plurality of target year scene information;
calculating a target year concentration predicted value under at least one target year contextual information;
constructing an atmospheric pollution source emission list under at least one target year scene information;
respectively performing model simulation by using the atmospheric pollution source emission list under the situation information of each target year, so as to obtain a simulated concentration predicted value corresponding to the atmospheric pollution source emission list under the situation information of each target year;
Fusing the target annual concentration predicted value and the simulated concentration predicted value to obtain PM2.5And (4) concentration target.
Optionally, the target annual concentration predicted value and the simulated concentration predicted value are fused to obtain the PM2.5The concentration targets include:
respectively acquiring influence factors of the target year scene according to the scene information of each target year;
correcting the corresponding target annual concentration predicted value or simulated concentration predicted value according to each influence factor so as to obtain a corrected target annual concentration predicted value and a corrected simulated concentration predicted value;
obtaining PM according to the corrected target annual concentration predicted value and the corrected simulated concentration predicted value2.5And (4) concentration target.
Optionally, the respectively obtaining the influence factors of the target year scenario according to each piece of target year scenario information includes:
acquiring at least one of a selection method of scene information of each target year, parameter source information and trend information;
and generating the influence factor according to at least one of the selection method of the scene information of each target year, the parameter source information and the trend information.
Optionally, the target year context information of the target year concentration predicted value is different from the target year context information of the simulated concentration predicted value.
Optionally, the performing model simulation by respectively using the atmospheric pollution source emission list under the situation information of each target year includes:
and respectively carrying out model simulation by using the atmospheric pollution source emission list under the situation information of each target year by adopting a WRF-CMAQ model.
The application also provides a PM2.5Concentration target acquisition means, the PM2.5The concentration target acquisition device includes:
the target year concentration predicted value calculation module is used for calculating a target year concentration predicted value under at least one piece of target year scene information;
the building module is used for building an atmospheric pollution source emission list under at least one piece of target year scene information;
the simulation module is used for performing model simulation by using the atmospheric pollution source emission list under each target year scene information respectively so as to obtain a simulated concentration predicted value corresponding to the atmospheric pollution source emission list under each target year scene information;
a fusion module for fusing the target annual concentration predicted value and the simulated concentration predicted value to obtain PM2.5And (4) concentration target.
Optionally, the fusion module comprises:
the influence factor acquisition module is used for respectively acquiring the influence factors of the target year scene according to the scene information of each target year;
The correction module is used for correcting the corresponding target annual concentration predicted value or the corresponding simulated concentration predicted value according to each influence factor so as to obtain a corrected target annual concentration predicted value and a corrected simulated concentration predicted value;
a corrected acquisition module for acquiring PM according to the corrected target annual concentration predicted value and the corrected simulated concentration predicted value2.5And (4) concentration target.
Optionally, the influence factor acquiring module includes:
the system comprises a selection method acquisition module, a selection method selection module and a selection module, wherein the selection method acquisition module is used for acquiring the selection method of the scene information of each target year;
the system comprises a parameter source acquisition module, a parameter source acquisition module and a parameter source processing module, wherein the parameter source acquisition module is used for acquiring parameter source information of scene information of each target year;
the trend information acquisition module is used for acquiring trend information of scene information of each target year;
and the influence factor generation module is used for generating the influence factor according to at least one of the selection method of the scene information of each target year, the parameter source information and the trend information.
The present application also provides an electronic device comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor, the processor implementing the PM as described above when executing the computer program 2.5And a concentration target acquisition method.
The application also providesComputer readable storage medium storing a computer program enabling a PM as described above when executed by a processor2.5And a concentration target acquisition method.
Has the beneficial effects that:
PM of the present application2.5The concentration target acquisition method acquires a target annual concentration predicted value (which is obtained by calculation) and a simulated concentration predicted value (which is obtained by software simulation), and acquires actual PM by fusing the target annual concentration predicted value and the simulated concentration predicted value2.5The concentration target is more objective than the prior art, and respectively considers the deviation possibly caused by a calculation method and the deviation caused by a simulation method, thereby obtaining the most appropriate PM2.5And (4) concentration target.
Drawings
FIG. 1 shows a PM according to an embodiment of the present application2.5A flow schematic diagram of a concentration target acquisition method;
FIG. 2 is an electronic device for implementing the PM shown in FIG. 12.5And a concentration target acquisition method.
Detailed Description
In order to make the implementation objects, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be described in more detail below with reference to the drawings in the embodiments of the present application. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are a subset of the embodiments in the present application and not all embodiments in the present application. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application. Embodiments of the present application will be described in detail below with reference to the accompanying drawings.
It should be noted that the terms "first" and "second" in the description of the present invention are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
FIG. 1 shows a PM according to an embodiment of the present application2.5And the flow chart of the concentration target acquisition method is shown.
PM as shown in FIG. 12.5The concentration target acquisition method comprises the following steps:
step 1: acquiring a plurality of target year scene information;
and 2, step: calculating a target year concentration predicted value under at least one target year contextual information;
and 3, step 3: constructing an atmospheric pollution source emission list under at least one target year scene information;
and 4, step 4: respectively performing model simulation by using the atmospheric pollution source emission list under the situation information of each target year, so as to obtain a simulated concentration predicted value corresponding to the atmospheric pollution source emission list under the situation information of each target year;
and 5: fusing the target annual concentration predicted value and the simulated concentration predicted value to obtain PM2.5And (4) concentration target.
PM of the present application2.5The concentration target acquisition method acquires a target annual concentration predicted value (which is obtained by calculation) and a simulated concentration predicted value (which is obtained by software simulation), and acquires actual PM by fusing the target annual concentration predicted value and the simulated concentration predicted value 2.5The concentration target is more objective than the prior art, and respectively considers the deviation possibly caused by a calculation method and the deviation caused by a simulation method, thereby obtaining the most appropriate PM2.5And (4) concentration target.
In this embodiment, the target annual concentration predicted value and the simulated concentration predicted value are fused to obtain the PM2.5The concentration targets include:
respectively acquiring influence factors of the target year scene according to the scene information of each target year;
correcting the corresponding target annual concentration predicted value or simulated concentration predicted value according to each influence factor so as to obtain a corrected target annual concentration predicted value and a corrected simulated concentration predicted value;
obtaining PM according to the corrected target annual concentration predicted value and the corrected simulated concentration predicted value2.5And (4) concentration target.
In this embodiment, the defect and the rationality of each scenario can be further objectively considered by introducing an influence factor, for example, the target annual concentration predicted value is obtained by calculation, and the result thereof is likely to be influenced by the obtained data, and at this time, the confidence of the obtained data becomes an influence factor that influences the confidence of the calculation result, and by giving the influence factor, the post-calculation correction can be performed well, and the final PM due to the distortion of the data is prevented 2.5The concentration target deviates far from the ideal value.
In this embodiment, the step of respectively obtaining the influence factors of the target year scenario according to the target year scenario information includes:
acquiring at least one of a selection method of scene information of each target year, parameter source information and trend information; the trend information includes economic information, policy information, energy information, and the like.
And generating the influence factor according to at least one of the selection method of the scene information of each target year, the parameter source information and the trend information.
In this embodiment, the target annual situation information for obtaining the target annual concentration predicted value is different from the target annual situation information for obtaining the simulated concentration predicted value, and the simulated concentration predicted value is obtained by simulation according to the atmospheric pollution source emission list.
It can be understood that, if each target year scenario information is the same, the final fusion result may be the same as the actual calculation or simulation result, and therefore, in order to ensure the representativeness of the fused result, the target year scenario information of the target year concentration predicted value and the target year scenario information of the simulated concentration predicted value are set to be different scenarios as much as possible, so that the data set can be enriched.
In this embodiment, the performing model simulation by using the atmospheric pollution source emission list under the situation information of each target year respectively includes:
and respectively carrying out model simulation by using the atmospheric pollution source emission list under the situation information of each target year by adopting a WRF-CMAQ model.
The present application is further described in detail below by way of examples, and it should be understood that the examples are not to be construed as limiting the present application in any way.
Selecting a reference year of 2020, wherein the reference year PM is2.5The concentration is 70 mu g/m3To reach the national air quality secondary standard of 35 mu g/m3Predicting 2025 year PM for ultimate goal2.5And (4) concentration target.
According to PM2.5Setting a plurality of target year scene information according to year-by-year overproof levels of concentration, wherein the target year scene information can comprise a reference scene and an enhanced scene, and respectively calculating PM of each target year scene information2.5And (4) concentration target. Wherein
。
In this embodiment, the reference scenario setting requirement is as follows: suppose historical PM2.5The annual average decline is 3.0%, the benchmark scene is calculated according to the decline of 3% per year, the standard is kept after the standard is reached, and the decline is 0.
In this embodiment, each enhancement scenario setting requirement is:
and setting the annual average reduction proportion according to the standard exceeding rate grade.
For example, the reinforcement scenario 1 is set as: after the standard is reached, the annual rate is reduced by 1.0 percent; when the standard exceeds 20% and is within, the annual average decline is consistent with the standard scene and is 3.0%; when the content exceeds 20 to 50 percent (inclusive), the annual average decrease is increased by 1.0 percent compared with the upper level and is 4.0 percent; when the content exceeds 50-100% (inclusive), the annual average decline is increased by 1.0% compared with the upper grade and is 5.0%; when the standard exceeds 100%, the annual average decrease is increased by 1.0% and is 6.0%.
In this example, 4 sets of strengthening scenarios are set in total, and the setting principle of the strengthening scenarios 2-4 is as follows: the reduction of the standard reaches 1.0% every year, and the other exceeding grades are increased by 0.5% compared with the last strengthening scene.
The annual average reduction ratio is specifically as follows:
according to the concentration reduction proportion set by the target scene, the annual concentration predicted values of the reference scene and the enhanced scene are obtained through calculation, and the method is shown in the following table:
an atmospheric pollution source emission list under the situation information of a plurality of target years is constructed as follows:
based on a benchmark annual atmospheric pollution source emission list, the new emission amount of regional pollutants caused by the newly increased and preserved quantity of motor vehicles and the newly increased and used quantity of natural gas in 2025 years is predicted, the multi-scenario pollutant emission reduction amount is calculated according to the multi-scenario atmospheric pollution prevention and treatment measures in the target year, the atmospheric pollution source emission list in the target year is further constructed, and an air quality simulation system is built by utilizing a WRF-CMAQ model to obtain a PM (particulate matter) pollution source emission list 2.5The annual average concentration. In this example, it is assumed that 4 sets of emission reduction scenarios (i.e. 4 atmospheric pollution source emission lists) are set, and the results are 57 μ g/m respectively after model simulation3、56μg/m3、55μg/m3、54μg/m3。
Acquiring influence factors of the target year scene according to each piece of target year scene information, wherein in this embodiment, the influence factors include: evaluating factors such as representativeness of the selection method, parameter integrity, objectivity of parameter selection, reasonability of result and the like are scored according to actual conditions, then weight is calculated according to scoring results, and finally PM of the target year is determined2.5Concentration targets (it is understood that scoring may be provided manually or by big data acquisition, for example, scoring may be performed with reference to the selection method representativeness, parameter completeness, objectivity of parameter selection, and probability that the result rationality may affect the actual result in each of the various situations of the previous year). The scoring is given in Table 1 (example) below, and the calculation formula is as follows. As can be seen from the table, the end result isThe goal to 54, therefore, was to determine the year 2025 PM2.5The target was 54. mu.g/m3。
Wherein, P is the final target, G is the target calculated by each method, S is the weight of each method, and t is the score obtained by each method.
Table 1:
by the method of the application, PM is obtained not only in a certain mode2.5Instead of the concentration target, the concentration target under the calculation method and the concentration target under the simulation method are respectively considered to obtain a more objective value, and the PM is obtained by introducing an influence factor2.5The concentration target is more consistent with the value expected by a user from the practical angle.
The application also provides a PM2.5Concentration target acquisition means, the PM2.5The concentration target acquisition device comprises a target year scene information acquisition module, a target year concentration predicted value calculation module, a construction module, a simulation module and a fusion module, wherein the target year scene information acquisition module is used for acquiring a plurality of target year scene information; the target year concentration predicted value calculating module is used for calculating a target year concentration predicted value under at least one piece of target year scene information; the construction module is used for constructing an atmospheric pollution source emission list under at least one piece of target year scene information; the simulation module is used for performing model simulation by respectively utilizing the atmospheric pollution source emission list under each target year scene information so as to obtain a simulated concentration predicted value corresponding to the atmospheric pollution source emission list under each target year scene information; a fusion module for fusing the target annual concentration predicted value to And simulating the predicted concentration value to obtain PM2.5And (4) concentration target.
In this embodiment, the fusion module includes an influence factor obtaining module, a modification module, and a modified obtaining module, where the influence factor obtaining module is configured to obtain, according to the information of each target year scenario, an influence factor of the target year scenario; the correction module is used for correcting the corresponding target annual concentration predicted value or the corresponding simulated concentration predicted value according to each influence factor so as to obtain a corrected target annual concentration predicted value and a corrected simulated concentration predicted value; the corrected acquisition module is used for acquiring PM according to the corrected target annual concentration predicted value and the corrected simulated concentration predicted value2.5And (4) concentration target.
In this embodiment, the influence factor acquisition module includes a selection method acquisition module, a parameter source acquisition module, a trend information acquisition module and an influence factor generation module, and the selection method acquisition module is used for acquiring a selection method of the scene information of each target year; the parameter source acquisition module is used for acquiring parameter source information of the scene information of each target year; the trend information acquisition module is used for acquiring trend information of scene information of each target year; the influence factor generation module is used for generating the influence factor according to at least one of the selection method of the scene information of each target year, the parameter source information and the trend information.
It should be noted that the foregoing explanations of the method embodiments are also applicable to the system of this embodiment, and are not repeated herein.
The application also provides an electronic device comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor, the processor implementing the PM as above when executing the computer program2.5And a concentration target acquisition method.
The present application also provides a computer-readable storage medium storing a computer program which, when executed by a processor, can implement the PM as above2.5And a concentration target acquisition method.
FIG. 2 is a block diagram of a computer-readable medium capable of implementing one embodiment according to the present applicationProvided PM2.5Exemplary structural diagrams of the electronic device of the concentration target acquisition method.
As shown in fig. 2, the electronic device includes an input device 501, an input interface 502, a central processor 503, a memory 504, an output interface 505, and an output device 506. The input interface 502, the central processing unit 503, the memory 504 and the output interface 505 are connected to each other through a bus 507, and the input device 501 and the output device 506 are connected to the bus 507 through the input interface 502 and the output interface 505, respectively, and further connected to other components of the electronic device. Specifically, the input device 501 receives input information from the outside and transmits the input information to the central processor 503 through the input interface 502; the central processor 503 processes input information based on computer-executable instructions stored in the memory 504 to generate output information, temporarily or permanently stores the output information in the memory 504, and then transmits the output information to the output device 506 through the output interface 505; the output device 506 outputs the output information to the outside of the electronic device for use by the user.
That is, the electronic device shown in fig. 2 may also be implemented to include: a memory storing computer executable instructions; and one or more processors that, when executing computer-executable instructions, may implement the PM described in conjunction with FIG. 12.5And a concentration target acquisition method.
In one embodiment, the electronic device shown in fig. 2 may be implemented to include: a memory 504 configured to store executable program code; one or more processors 503 configured to execute executable program code stored in the memory 504 to perform PM in the above-described embodiments2.5And a concentration target acquisition method.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media include both non-transitory and non-transitory, removable and non-removable media that implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
Furthermore, it will be obvious that the term "comprising" does not exclude other elements or steps. A plurality of units, modules or devices recited in the device claims may also be implemented by one unit or overall device by software or hardware. The terms first, second, etc. are used to identify names, but not any particular order.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks identified in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The Processor referred to in this embodiment may be a Central Processing Unit (CPU), and may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may be used to store computer programs and/or modules, and the processor may implement various functions of the apparatus/terminal device by executing or performing the computer programs and/or modules stored in the memory, as well as invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
In this embodiment, the module/unit integrated with the apparatus/terminal device may be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as a separate product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by hardware related to instructions of a computer program, and the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments described above may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic diskette, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signal, telecommunications signal, software distribution medium, etc.
It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in the jurisdiction. Although the present application has been described with reference to the preferred embodiments, it is not intended to limit the present application, and those skilled in the art can make variations and modifications without departing from the spirit and scope of the present application.
Although the invention has been described in detail with respect to the general description and the specific embodiments thereof, it will be apparent to those skilled in the art that modifications and improvements can be made based on the invention. Accordingly, it is intended that all such modifications and alterations be included within the scope of this invention as defined in the appended claims.
Claims (10)
1. PM (particulate matter)2.5A density target acquisition method, characterized in that the PM2.5The concentration target acquisition method comprises the following steps:
acquiring a plurality of target year scene information;
calculating a target year concentration predicted value under at least one target year scene information;
constructing an atmospheric pollution source emission list under at least one target year scene information;
respectively performing model simulation by using the atmospheric pollution source emission list under the situation information of each target year, so as to obtain a simulated concentration predicted value corresponding to the atmospheric pollution source emission list under the situation information of each target year;
fusing the target annual concentration predicted value and the simulated concentration predicted value to obtain PM2.5And (4) concentration target.
2. The PM of claim 12.5The method for obtaining the concentration target is characterized in that the PM is obtained by fusing the target annual concentration predicted value and the simulated concentration predicted value 2.5The concentration targets include:
respectively acquiring influence factors of the target year scene according to the scene information of each target year;
correcting the corresponding target annual concentration predicted value or simulated concentration predicted value according to each influence factor so as to obtain a corrected target annual concentration predicted value and a corrected simulated concentration predicted value;
obtaining PM according to the corrected target annual concentration predicted value and the corrected simulated concentration predicted value2.5And (4) concentration target.
3. The PM of claim 22.5The method for acquiring the concentration target is characterized in that the step of respectively acquiring the influence factors of the target year scene according to the scene information of each target year comprises the following steps:
acquiring at least one of a selection method of scene information of each target year, parameter source information and trend information;
and generating the influence factor according to at least one of the selection method of the scene information of each target year, the parameter source information and the trend information.
4. The PM of claim 32.5The method for acquiring the concentration target is characterized in that target year scene information for acquiring the target year concentration predicted value is different from target year scene information for acquiring the simulated concentration predicted value.
5. The PM of claim 4 2.5The concentration target obtaining method is characterized in that the model simulation by respectively utilizing the atmospheric pollution source emission list under the situation information of each target year comprises the following steps:
and respectively carrying out model simulation by using the WRF-CMAQ model and the atmospheric pollution source emission list under the scene information of each target year.
6. PM (particulate matter)2.5A concentration target acquisition device, characterized in that the PM2.5The concentration target acquisition device includes:
the system comprises a target year scene information acquisition module, a target year scene information acquisition module and a scene information processing module, wherein the target year scene information acquisition module is used for acquiring a plurality of target year scene information;
the target annual concentration predicted value calculation module is used for calculating a target annual concentration predicted value under at least one piece of target annual contextual information;
the building module is used for building an atmospheric pollution source emission list under at least one target year scene information;
the simulation module is used for performing model simulation by using the atmospheric pollution source emission list under each target year scene information respectively so as to obtain a simulated concentration predicted value corresponding to the atmospheric pollution source emission list under each target year scene information;
a fusion module for fusing the target annual concentration predicted value and the simulated concentration predicted value to obtain PM 2.5And (4) concentration target.
7. The PM of claim 62.5Concentration target acquisition device, its characterized in that, the fusion module includes:
the influence factor acquisition module is used for respectively acquiring the influence factors of the target year scene according to the scene information of each target year;
the correction module is used for correcting the corresponding target annual concentration predicted value or the corresponding simulated concentration predicted value according to each influence factor so as to obtain a corrected target annual concentration predicted value and a corrected simulated concentration predicted value;
a corrected acquisition module for acquiring PM according to the corrected target annual concentration predicted value and the corrected simulated concentration predicted value2.5And (4) concentration target.
8. The PM of claim 72.5The concentration target obtaining device is characterized in that the influence factor obtaining module comprises:
the selection method acquisition module is used for acquiring a selection method of scene information of each target year;
the parameter source acquisition module is used for acquiring parameter source information of the scene information of each target year;
the trend information acquisition module is used for acquiring trend information of scene information of each target year;
And the influence factor generation module is used for generating the influence factor according to at least one of the selection method of the scene information of each target year, the parameter source information and the trend information.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the PM of any of claims 1 to 5 when executing the computer program2.5Concentration targetAnd (4) obtaining the method.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, is capable of implementing a PM according to any one of claims 1 to 52.5And a concentration target acquisition method.
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| CN111507533A (en) * | 2020-04-22 | 2020-08-07 | 中科三清科技有限公司 | Air pollutant concentration prediction method and device, electronic equipment and storage medium |
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| CN113627529A (en) * | 2021-08-11 | 2021-11-09 | 成都佳华物链云科技有限公司 | Air quality prediction method, device, electronic equipment and storage medium |
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| CN111507533A (en) * | 2020-04-22 | 2020-08-07 | 中科三清科技有限公司 | Air pollutant concentration prediction method and device, electronic equipment and storage medium |
| CN111882205A (en) * | 2020-07-24 | 2020-11-03 | 中科三清科技有限公司 | Air quality standard-reaching analysis method and device, electronic equipment and storage medium |
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