CN114862175A - Method and device for obtaining air quality improvement effect under different scenes - Google Patents
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Abstract
The application discloses a method and a device for obtaining an air quality improvement effect under different scenes. The method comprises the following steps: obtaining an air quality model; acquiring control measure information under at least two scenes; generating energy consumption information under corresponding scenes according to the control measure information under each scene; acquiring a regional atmosphere pollution source emission list of each scene according to the control measure information and the energy consumption information of each scene; inputting the air pollution source emission lists of all regions into an air quality model to obtain corresponding simulation information; and acquiring the pollutant reduction effect of the scene according to the simulation information of each scene. The method provided by the application simulates according to the control measure information of different scenes and combines a pollution source emission list, and the quantitative evaluation of the emission reduction effect is carried out by using the LEAP model and the CMAQ air quality model, so that the problem that the quantitative evaluation of the atmospheric pollutant reduction effect is lacked under different scenes in the prior art is solved.
Description
Technical Field
The application relates to the technical field of environmental protection and atmospheric pollution prevention, in particular to a method for obtaining air quality improvement effects under different scenes and a device for obtaining air quality improvement effects under different scenes.
Background
Along with the rapid development of socioeconomic of China, the process of urbanization and industrialization is accelerated continuously, and the atmospheric pollution caused therewith brings serious negative effects on the health of people and the quality of daily life. Based on the severe situation of atmospheric pollution, in 2013, the former environmental protection department proposed a national city air quality standard-reaching schedule: the first city with the atmospheric pollutants exceeding 15% is strived to reach the standard in 2015; the first is that the air pollutants exceed the standard by more than 15% and less than 30% in cities, and the aim is to reach the standard in 2020; the air quality is continuously improved by making a medium-term and long-term standard-reaching plan in cities with the air pollutants exceeding 30% in the first place, and all cities in the country reach the secondary air quality standard in 2030.
At present, the evaluation of the air quality improvement effect in China is basically based on a single scene, and the method is to directly simulate and evaluate the air quality improvement effect brought by all measures in the atmospheric environment quality time limit standard-reaching planning, and lacks of measures for preventing and treating air pollution under different scenes to PM 2.5 Quantitative evaluation of the effect of concentration reduction.
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
It is an object of the present invention to overcome or at least mitigate at least one of the above-mentioned deficiencies of the prior art by providing a method of obtaining an air quality improvement effect in different scenarios.
In one aspect of the present invention, a method for obtaining an air quality improvement effect under different scenarios is provided, where the method for obtaining the air quality improvement effect under different scenarios includes:
obtaining an air quality model;
acquiring control measure information under at least two scenes;
generating energy consumption information under corresponding scenes according to the control measure information under each scene, wherein one scene generates one energy consumption information;
acquiring an atmospheric pollution source emission list of a region corresponding to each scene according to the control measure information and the energy consumption information under each scene;
respectively inputting the air pollution source emission lists of all regions into an air quality model so as to obtain corresponding simulation information;
and acquiring the pollutant reduction effect of each scene according to the simulation information of the scene.
Optionally, the obtaining at least one of the control measure information under the at least two scenarios is a reference scenario, and the obtaining the control measure information of the reference scenario includes:
acquiring a reference year atmospheric pollution source emission list;
acquiring target year structure adjustment measure information and terminal control measure information;
and generating control measure information of a reference scene according to the reference year atmospheric pollution source emission list, the target year structure adjustment measure information and the terminal control measure information.
Optionally, the acquiring the target year structure adjustment measure information and the end control measure information includes:
obtaining a structure adjustment parameter;
acquiring target year structure adjustment measure information according to the structure adjustment parameters;
acquiring a terminal control parameter;
and acquiring the terminal control measure information according to the terminal control parameters.
Optionally, the obtaining at least one of the control measure information under the at least two scenarios is an optimal technical scenario, and the obtaining the control measure information of the optimal technical scenario includes:
acquiring target year structure adjustment measure information and terminal control measure information of a reference scene;
changing the end control measure information to form optimal end control measure information;
changing the structure adjustment measure information to form optimal structure adjustment measure information;
and generating the control measure information of the optimal technical scene according to the optimal terminal control measure information and the optimal structure adjustment measure information.
Optionally, the obtaining of the control measure information under at least two scenarios includes obtaining at least three scenarios, where at least one scenario is a strengthened scenario, and the obtaining of the control measure information of the strengthened scenario includes:
acquiring control measure information of the optimal technical scene;
and reinforcing the control measure information of the optimal technical scene so as to obtain reinforced scene control measure information.
Optionally, the generating energy consumption information under the corresponding scenario according to the control measure information under each scenario, where the generating of one energy consumption information under each scenario includes:
and predicting the total energy consumption and the energy consumption structure under various scenes by using the LEAP model.
Optionally, the predicting the total energy consumption and the energy consumption structure under various scenarios by using the LEAP model includes:
acquiring basic information of a target city or region in a target year;
acquiring the motor vehicle remaining amount information of a target year;
acquiring industrial product yield information, energy efficiency information and energy distribution information corresponding to the control measure information under each scene;
the following operations are performed for each scenario:
and inputting the control measure information, the basic information and the motor vehicle remaining amount information of each scene into the LEAP model, acquiring energy consumption information corresponding to the scene, and calculating pollutant increment according to the energy consumption information, wherein the energy consumption information comprises energy consumption total amount and energy consumption structure.
Optionally, the obtaining of the regional atmosphere pollution source emission list corresponding to each scenario according to the control measure information under each scenario includes:
reading the adjustment measures A of energy, industry, transportation and land structure under different situations needing to be evaluated i Or end control measures B i In combination with the emission list of the atmospheric pollution source of the reference year andthe target annual pollutant increment is determined, and the reference emission E of the pollutant emission source in the emission list corresponding to each measure is determined i Determining the reduction rate M of the measures according to the emission reduction effect of different measures on different pollutants i Calculating pollutant reduction amount P (P ═ E) i ·M i ) And constructing a regional atmosphere pollution source emission list.
The application also provides a device for obtaining the air quality improvement effect under different scenes, the device for obtaining the air quality improvement effect under different scenes comprises:
the air quality model acquisition module is used for acquiring an air quality model;
the system comprises a scene control measure acquisition module, a scene control measure acquisition module and a scene control measure processing module, wherein the scene control measure acquisition module is used for acquiring control measure information under at least two scenes;
the energy consumption information acquisition module is used for generating energy consumption information under corresponding scenes according to the control measure information under each scene, wherein each scene generates energy consumption information;
the emission list acquisition module is used for acquiring a regional atmosphere pollution source emission list corresponding to each scene according to the control measure information and the energy consumption information under each scene;
the simulation information acquisition module is used for respectively inputting the atmospheric pollution source emission lists of all regions into the air quality model so as to acquire corresponding simulation information;
and the reduction effect acquisition module is used for acquiring the pollutant reduction effect of each scene according to the simulation information of the scene.
The application also provides an electronic device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize the method for obtaining the air quality improvement effect under different scenes.
Has the advantages that:
the method for obtaining the air quality improvement effect under different scenes simulates according to the control measure information under different scenes, combines the urban atmospheric pollutant emission source list, applies a long-term energy substitution planning system (LEAP) and a CMAQ air quality model to carry out quantitative evaluation on the emission reduction effect, and solves the problem that the prior art lacks measures for preventing and treating air pollution under different scenes on PM 2.5 Quantitative evaluation of the effect of concentration reduction.
Drawings
Fig. 1 is a schematic flow chart illustrating a method for obtaining an air quality improvement effect under different situations according to an embodiment of the present application;
fig. 2 is an electronic device for implementing the method of fig. 1 for obtaining the air quality improvement effect under different situations.
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 is a schematic flow chart of a method for obtaining an air quality improvement effect under different situations according to an embodiment of the present application.
The method for obtaining the air quality improvement effect under different scenes as shown in fig. 1 comprises the following steps:
step 1: obtaining an air quality model;
and 2, step: acquiring control measure information under at least two scenes;
and step 3: generating energy consumption information under corresponding scenes according to the control measure information under each scene, wherein one scene generates one energy consumption information;
and 4, step 4: acquiring an atmospheric pollution source emission list of an area corresponding to each scene according to the control measure information and the energy consumption information under each scene;
and 5: respectively inputting the air pollution source emission lists of all regions into an air quality model so as to obtain corresponding simulation information;
step 6: and acquiring the pollutant reduction effect of each scene according to the simulation information of the scene.
The method for obtaining the air quality improvement effect under different scenes simulates according to control measure information under different scenes, combines an atmospheric pollution source emission list, applies a long-term energy substitution planning system (LEAP) and a CMAQ air quality model to carry out quantitative evaluation on emission reduction effect, and solves the problem that the prior art lacks of atmosphere pollution prevention and control measures to PM under different scenes 2.5 Quantitative evaluation of the effect of concentration reduction.
In this embodiment, acquiring at least one of the control measure information under at least two scenarios as a reference scenario includes:
acquiring a reference year atmospheric pollution source emission list;
acquiring target year structure adjustment measure information and terminal control measure information;
and generating control measure information of a reference scene according to the reference year atmospheric pollution source emission list, the target year structure adjustment measure information and the terminal control measure information.
In this embodiment, the following method is specifically adopted to obtain the air quality model:
PM (particulate matter) by constructing air quality simulation system by using WRF-CMAQ model 2.5 Average concentration per yearSimulating;
respectively to PM 2.5 Performing iterative calculation on the medium key components, and setting the PM 2.5 And the components of the aerosol comprise sulfate, nitrate, ammonium salt, secondary organic aerosol and primary PM 2.5 Target limit of (2), PM is performed 2.5 Target limit setting, PM in Table 1 2.5 Correspondence to its precursor contaminants;
TABLE 1PM 2.5 Corresponding relation with precursor
A reduction scheme is formulated, and the emission quantity and the PM of pollutants are assumed to be within a certain emission reduction range 2.5 The concentration is in a linear relationship according to sulfate, nitrate, ammonium salt, secondary organic aerosol and primary PM 2.5 The ratio of the annual average concentration of the key components to the standard-reaching limit value is respectively set up 2 、NO X 、VOCs、NH 3 And primary PM 2.5 The emission reduction scheme of (1);
based on the emission reduction scheme, when PM 2.5 When the annual average concentration reaches the standard, SO is obtained 2 、NO X 、VOCs、NH 3 And primary PM 2.5 Calculating a new atmospheric pollution source emission list, and simulating the PM of a target city under a new reduction scheme 2.5 And the annual average concentration of the key components;
then repeating the above steps until PM 2.5 The annual average concentration is less than or close to 35 mu g/m 3 Target limit of (3), obtaining SO 2 、NO X 、VOCs、NH 3 And primary PM 2.5 I.e. the atmospheric environmental capacity.
By adopting the method, the verified air quality model can be obtained.
In this embodiment, the target year structure adjustment measure information is shown in table 2, and includes energy structure information, industry structure information, transportation structure information, land structure adjustment information, and the like.
In this embodiment, the acquiring the target year structure adjustment measure information and the end point control measure information includes: obtaining a structure adjustment parameter; acquiring target year structure adjustment measure information according to the structure adjustment parameters; acquiring a terminal control parameter; acquiring terminal control measure information according to terminal control parameters, specifically, executing the existing prevention and control measures based on the energy consumption, the industrial product yield, the current motor vehicle emission situation, the dust emission control and the terminal control technology distribution of a target city or an area reference year, quantizing the target year structure adjustment measures according to the emission reduction ratios of different measures to different pollutants, thereby acquiring the target year structure adjustment measure information and quantizing the target year terminal control measures, thereby acquiring the terminal control measure information.
In this embodiment, acquiring at least one of the control measure information under at least two scenarios as the optimal technical scenario includes:
acquiring reference scene target year structure adjustment measure information and terminal control measure information;
changing the end control measure information to form optimal end control measure information;
changing the structure adjustment measure information to form optimal structure adjustment measure information;
and generating the control measure information of the optimal technical scene according to the optimal terminal control measure information and the optimal structure adjustment measure information.
In this embodiment, the at least three scenarios obtained by obtaining the control measure information under the at least two scenarios are obtained, where at least one of the scenarios is a strengthened scenario, and obtaining the control measure information of the strengthened scenario includes:
acquiring control measure information of the optimal technical scene;
and reinforcing the control measure information of the optimal technical scene so as to obtain reinforced scene control measure information.
In this embodiment, the method for generating energy consumption information under corresponding scenarios according to the control measure information under each scenario, wherein generating one energy consumption information under each scenario includes:
and predicting the total energy consumption and the energy consumption structure under various scenes by using the LEAP model.
In this embodiment, the predicting the total energy consumption and the energy consumption structure under various scenarios by using the LEAP model includes:
acquiring basic information of a target city or region in a target year, wherein the basic information comprises a total regional production value, a number of permanent population and a urbanization rate of the target city or region; more specifically, on the basis of city or regional statistical yearbook, a linear regression method or an elastic coefficient method is used for predicting the regional production total value, the number of permanent population and the urbanization rate of a target city or region in a target year;
acquiring the motor vehicle inventory information of the target year, specifically, predicting the motor vehicle inventory of the target year by using the basic information and a Gompertz model;
acquiring control measure information under each scene;
and respectively inputting the control measure information under various scenes into the LEAP model, and respectively acquiring energy consumption information corresponding to different scenes, wherein the energy consumption information comprises the total energy consumption amount and the energy consumption structure. Specifically, based on the yield of industrial products, energy efficiency, energy distribution and the like under different situations, different parameters are input into the LEAP model, and the total energy consumption and the energy consumption structure (including the consumption proportion of coal, oil, natural gas, primary power and other energy) under different situations are simulated.
In this embodiment, the obtaining of the regional atmosphere pollution source emission list corresponding to each scenario according to the control measure information under each scenario includes:
predicting new pollutant increment of target city or area in target year, and reading energy, industry, transportation and land structure regulation measures A in different situations needing evaluation i Or end control measures B i And determining the reference emission E of the pollutant emission source corresponding to each measure by combining the reference annual atmospheric pollution source emission list and the target annual pollutant increment i And measure reduction rate M i Calculating pollutant reduction amount P (P ═ E) i ×M i ) And constructing a target year city or regional atmospheric pollution source emission list.
Specifically, new increase of pollutants in target cities or areas in target years is predicted, new increase of pollutants in the future mainly comes from increase of the holding capacity of motor vehicles, increase of natural gas utilization, various newly-built industrial enterprises, and emission of new pollutants of motor vehicles
Forecasting the holding quantity V of different types of motor vehicles according to target years i (V ═ V × E ^ (α E) ^ β E, where V represents vehicle holding capacity, V ^ represents thousand motor vehicle holding capacity saturation value, E represents regional average GDP) and emission factor Y of different vehicle types i Calculating the newly increased discharge amount of natural gas
According to the newly added usage L of different natural gas use departments (including industry, electric power, heat supply, civil use and traffic) i And an emission factor F i Calculating; newly building a new reference environment evaluation report of new pollutant increment (D) of the industrial enterprise;
reading the energy, industry, transportation and land structure adjustment measures A under different situations needing to be evaluated i Or end control measures B i And determining the standard emission E of the pollutant emission source corresponding to each measure by combining the atmospheric pollution source emission list and the target annual pollutant increment i And measure reduction rate M i Calculating pollutant reduction amount P (P ═ E) i ×M i ) And finally reducing the discharge amount Z (Z is P-G-T-D), and constructing a target annual urban or regional atmospheric pollution source discharge list.
In this embodiment, the emission lists of the atmospheric pollution sources in each region are respectively input to the air quality model, so as to obtain corresponding simulation information as follows:
according to a high space-time resolution discharge list (namely, a discharge list of atmospheric pollution sources in each region) of a reference year and a target year of air quality standard reaching, a CMAQ model three-layer nesting technology is used for quantitatively simulating and analyzing the implementation of the target year air quality standard reaching measures under different scenesAtmospheric environment improvement effect, and PM analysis by comparison with the reference year simulation result 2.5 And the concentration reduction proportion is compared with the target annual air quality improvement target to evaluate the accessibility of the air quality target, and scientific support is provided for the city or the region to reach the air quality secondary standard.
In the present embodiment, the scenarios include a reference scenario BAU, an optimal technology scenario BAT, and an enhanced scenario EES, specifically, based on PM 2.5 The concentration reaches 35 mu g/m 3 According to structural adjustment measures A that can reduce emissions from the source i (including energy, industry, transportation, land structure adjustment measures), and terminal control measures B for reducing emissions before the pollutants are emitted into the atmosphere i The reference scenario BAU, the optimal technical scenario BAT, and the reinforcement scenario EES are set.
In this embodiment, the performing of the baseline year air quality simulation based on the baseline year atmospheric pollution source emission list and the WRF-CMAQ air quality model and the verifying of the accuracy of the simulation result includes the following processes:
(1) PM (particulate matter) by constructing air quality simulation system by using WRF-CMAQ model 2.5 Annual average concentration simulation: an air quality simulation system suitable for a target city or area is built based on a WRF-CMAQ model, and monthly PM of a reference year is simulated 2.5 And the concentration of the key component, the average concentration of 12 months is the annual average concentration;
(2) respectively to PM 2.5 And PM 2.5 Performing iterative calculation on the medium key components, and setting the PM 2.5 Sulfate, nitrate, ammonium salt, primary PM 2.5 Target limit, PM is performed 2.5 Setting a target limit value: according to PM 2.5 Target limit and monthly simulated sulfate, nitrate, ammonium salt, secondary organic aerosol, primary PM of reference year 2.5 Occupy PM 2.5 Average ratio, set PM 2.5 Sulfate, nitrate, ammonium salt, secondary organic aerosol and primary PM 2.5 A target limit;
(3) and (3) making a reduction scheme: according to sulfate, nitrate, ammonium salt, secondary organic aerosol and primary PM 2.5 The ratio of the annual average concentration of the key components to the compliance limitAnd respectively formulating emission reduction schemes of different pollutants. Assuming that the pollutants are in a certain emission reduction range, the emission amount and PM of the pollutants are 2.5 The concentrations of the corresponding components in (1) are in a linear relationship. According to sulfate, nitrate, ammonium salt, secondary organic aerosol and primary PM 2.5 Respectively formulating SO according to the analysis result of the particulate matter source by combining the ratio of the annual average concentration to the target limit value and the emission list of the atmospheric pollution source 2 、NO X 、NH 3 VOCs and primary PM 2.5 The emission reduction scheme of (1);
(4) based on a reduction scheme, when PM 2.5 When the annual average concentration reaches the standard, the environmental capacities of different pollutants are obtained, a new multi-pollutant emission list is calculated, and the PM of a target city under a new reduction scheme is simulated 2.5 And the annual average concentration of the key components, and then repeating the processes (3) and (4) until the PM is obtained 2.5 The annual average concentration is less than and close to the target limit value to obtain SO 2 、NO X 、NH 3 VOCs and primary PM 2.5 The maximum allowable discharge amount, namely the atmospheric environment capacity, provides scientific basis for formulating measures for preventing and treating atmospheric pollution in cities or regions.
In this embodiment, the scenes include three scenes, and each scene is generated as follows:
based on PM 2.5 The concentration reaches 35 mu g/m 3 The target of air quality, setting three scenes for target accessibility analysis, wherein the three scenes are set according to two key factors closely related to the future pollutant emission, including structural adjustment measures (energy, industry, transportation, land use) and terminal control measures.
Firstly, based on the energy consumption, the product yield, the current motor vehicle emission situation, the raised dust control and the terminal control technology distribution of a target city or a regional reference year, executing the existing prevention and control measures, quantifying the target year structure adjustment measures and the terminal control measures according to the emission reduction effect of different measures on different pollutants, and setting the measures as reference scenes;
secondly, based on the reference scene, strengthening the terminal control measures and executing stricter structure adjustment measures, and establishing an optimal technical scene;
and finally, further strengthening energy, industry, transportation, land structure adjustment and terminal control on the basis of the optimal technical situation, and establishing a strengthened situation, wherein the strengthened situation comprises a strengthened structure adjusting measure and a terminal control measure. The terminal control measure refers to specific limiting content of execution. In the present embodiment, the control parameters are shown in table 2, where table 2 is the basic contents of the structure adjustment measure and the end control measure;
TABLE 2 basic contents of the structural adjustment measures and end control measures
In this embodiment, generating the energy consumption information in the corresponding scenario according to the control measure information in each scenario includes:
firstly, predicting socioeconomic development data of a target city or region in a target year by using a linear regression method or an elastic coefficient method based on city or region statistical yearbook, wherein the socioeconomic development data comprises a total regional production value, a number of permanent population and a urbanization rate;
then, predicting the motor vehicle holding amount in the target year by using the predicted socioeconomic development data and a Gompertz model;
and finally, inputting different parameters into the LEAP model based on the industrial product yield, the energy efficiency, the energy distribution and the like under different situations, and simulating the total energy consumption and the structure under different situations. Specifically, important social and economic indexes such as the total production value of a region, the population number, the urbanization rate, the motor vehicle holding capacity and the like and basic data of various industries such as life, business, traffic, industry, agriculture, energy and the like under different situations are input into the LEAP model, modeling and analysis are carried out on the energy demand and distribution of various terminal applications, and the model is operated and the total energy consumption amount and the energy consumption structure under different situations are output.
In this embodiment, obtaining the regional atmospheric pollution source emission list corresponding to each scenario according to the control measure information in each scenario includes constructing emission lists of target years in three different scenarios, and based on the reference annual regional emission list and the energy consumption structure and consumption in different scenarios, by predicting the new regional pollutant emission caused by the new increase and maintenance of motor vehicles and the new increase and consumption of natural gas in the target year, and according to the adjustment and planning of energy, industry, transportation and land structure, and the terminal control measure of the key industry, constructing the target annual emission lists in the three different scenarios, the emission reduction of the main pollutants in the region is obtained.
Specifically, new increase of pollutants in a target city or region in a target year is predicted, and new increase of pollutants in the future mainly comes from increase of the holding capacity of motor vehicles, increase of natural gas utilization and various newly-built industrial enterprises. Wherein the motor vehicle newly increases pollutant emission
Forecasting the holding quantity V of different types of motor vehicles according to target years i (V ═ V × E ^ (α E) ^ β E, where V represents vehicle holding capacity, V ^ represents thousand motor vehicle holding capacity saturation value, E represents regional average GDP) and emission factor Y of different vehicle types i Calculating; the increment of the natural gas use can be derived through an LEAP model, and the newly increased discharge amount of the natural gas
According to the newly added use amount L of different natural gas use departments (comprising five fields of industry, electric power, heat supply, civil use and traffic) i And an emission factor F i Calculating; newly building a new pollutant increment (D) of the industrial enterprise according to an environmental evaluation report;
(2) according to the adjustment and planning of energy, industry, transportation and land structure under different situations A i And end of key industryEnd control measure B i Determining the reference emission E of the pollutant emission source corresponding to each measure i And measure reduction rate M i Calculating pollutant reduction amount P (P ═ E) i ×M i ). And constructing a target annual pollutant emission list under three different scenes by combining a target annual pollutant new increment W (W is T + G + D), and obtaining a final emission reduction amount Z (Z is P-W) of the regional main pollutants.
In this embodiment, according to the high spatial-temporal resolution discharge list of the reference year and the target year, the atmospheric environment effect after the implementation of the air quality standard-reaching measure of the target year under different scenes is quantitatively simulated and analyzed by the CMAQ model three-layer nesting technology, and the PM is analyzed by comparing with the simulation result of the reference year 2.5 And the concentration reduction proportion is compared with the air quality improvement target in the target year to evaluate the accessibility of the air quality target under different scenes, and a measure needing strengthening and implementation is provided to provide scientific and technological support for the city or the region to reach the air quality secondary standard.
According to the method for obtaining the air quality improvement effect under different scenes, due to the fact that different scenes are set, the control effect of different energy sources, industries, transportation, land structure adjustment and terminal control measures on the atmospheric pollutant emission in a target year and the air quality improvement condition can be quantitatively analyzed, and a basis is provided for city or region selection and implementation of effective measures to carry out air pollution treatment.
The application also provides a device for acquiring the air quality improvement effect under different scenes, which comprises an air quality model acquisition module, a scene control measure acquisition module, an energy consumption information acquisition module, an emission list acquisition module, a simulation information acquisition module and a reduction effect acquisition module, wherein,
the air quality model acquisition module is used for acquiring an air quality model;
the scene control measure acquisition module is used for acquiring control measure information under at least two scenes;
the energy consumption information acquisition module is used for generating energy consumption information under corresponding scenes according to the control measure information under each scene, wherein each scene generates energy consumption information;
the emission list acquisition module is used for acquiring the regional atmosphere pollution source emission list corresponding to each scene according to the control measure information and the energy consumption information under each scene;
the simulation information acquisition module is used for respectively inputting the atmospheric pollution source emission lists of all regions into the air quality model so as to acquire corresponding simulation information;
the reduction effect acquisition module is used for acquiring the pollutant reduction effect of each scene according to the simulation information of the scene.
It should be noted that the foregoing explanations of the method embodiments also apply to the apparatus of this embodiment, and are not repeated herein.
The application also provides an electronic device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize the method for obtaining the air quality improvement effect under different scenes.
The present application also provides a computer-readable storage medium, in which a computer program is stored, and the computer program, when executed by a processor, can implement the above method for obtaining the air quality improvement effect under different scenarios.
Fig. 2 is an exemplary block diagram of an electronic device capable of implementing the method for obtaining an air quality improvement effect under different scenarios according to an embodiment of the present application.
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 which, when executing the computer-executable instructions, may implement the method of obtaining air quality improvement in different scenarios described in connection with fig. 1.
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 the executable program code stored in the memory 504 to perform the method of obtaining the air quality improvement effect under different scenarios in the above-described embodiment.
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 the like) 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 in this embodiment may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, and so on. 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 running or executing the computer programs and/or modules stored in the memory, as well as by 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, and the like. 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, which 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 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 disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like.
It should be noted that the computer readable medium may contain content that is appropriately increased or decreased 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 hereinabove with respect to a general description and specific embodiments thereof, it will be apparent to those skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (10)
1. A method for obtaining air quality improvement effect under different scenes is characterized by comprising the following steps:
obtaining an air quality model;
acquiring control measure information under at least two scenes;
generating energy consumption information under corresponding scenes according to the control measure information under each scene, wherein one scene generates one energy consumption information;
acquiring an atmospheric pollution source emission list of a region corresponding to each scene according to the control measure information and the energy consumption information under each scene;
respectively inputting the atmospheric pollution source emission lists of all regions into an air quality model so as to obtain corresponding simulation information;
and acquiring the pollutant reduction effect of each scene according to the simulation information of the scene.
2. The method of obtaining air quality improvement under different circumstances as set forth in claim 1,
the acquiring of at least one of the control measure information under at least two scenarios is a reference scenario, and the acquiring of the control measure information of the reference scenario includes:
acquiring a reference year atmospheric pollution source emission list;
acquiring target year structure adjustment measure information and terminal control measure information;
and generating control measure information of a reference scene according to the reference year atmospheric pollution source emission list, the target year structure adjustment measure information and the terminal control measure information.
3. The method for obtaining air quality improvement effect under different scenes as claimed in claim 2, wherein said obtaining target annual structure adjustment measure information and terminal control measure information comprises:
obtaining a structure adjustment parameter;
acquiring target year structure adjustment measure information according to the structure adjustment parameters;
acquiring a terminal control parameter;
and acquiring the terminal control measure information according to the terminal control parameters.
4. The method of claim 3, wherein at least one of the at least two scenarios of obtaining the control measure information is an optimal technical scenario, and obtaining the control measure information of the optimal technical scenario comprises:
acquiring target year structure adjustment measure information and terminal control measure information of a reference scene;
changing the end control measure information to form optimal end control measure information;
changing the structure adjustment measure information to form optimal structure adjustment measure information;
and generating the control measure information of the optimal technical scene according to the optimal terminal control measure information and the optimal structure adjustment measure information.
5. The method of claim 4, wherein the obtaining of the control measure information under at least two scenarios comprises at least three scenarios, at least one of the scenarios is an enhanced scenario, and the obtaining of the control measure information under the enhanced scenario comprises:
acquiring control measure information of the optimal technical scene;
and reinforcing the control measure information of the optimal technical scene so as to obtain reinforced scene control measure information.
6. The method of claim 5, wherein the generating of the energy consumption information according to the control measure information of each scenario comprises:
and predicting the total energy consumption and the energy consumption structure under various scenes by using the LEAP model.
7. The method of claim 6, wherein the predicting the total energy consumption and the energy consumption structure under various scenes by using the LEAP model comprises:
acquiring basic information of a target city or region in a target year;
acquiring the motor vehicle remaining amount information of a target year;
acquiring industrial product yield information, energy efficiency information and energy distribution information corresponding to the control measure information under each scene;
the following operations are performed for each scenario:
and inputting the control measure information, the basic information and the motor vehicle remaining amount information of each scene into the LEAP model, acquiring energy consumption information corresponding to the scene, and calculating pollutant increment according to the energy consumption information, wherein the energy consumption information comprises energy consumption total amount and energy consumption structure.
8. The method for obtaining the air quality improvement effect under different scenes as claimed in claim 7, wherein the step of obtaining the regional air pollution source emission list corresponding to each scene according to the control measure information under each scene comprises the following steps:
reading the adjustment measures A of energy, industry, transportation and land structure under different situations needing to be evaluated i Or end control measures B i And determining the reference emission E of the pollutant emission source in the emission list corresponding to each measure by combining the reference annual atmospheric pollution source emission list and the target annual pollutant increment i Determining the reduction rate M of the measures according to the emission reduction effect of different measures on different pollutants i Calculating pollutant reduction amount P (P ═ E) i ·M i ) And constructing a regional atmospheric pollution source emission list.
9. An apparatus for obtaining an air quality improvement effect under different scenes, the apparatus comprising:
the air quality model acquisition module is used for acquiring an air quality model;
the system comprises a scene control measure acquisition module, a scene control measure acquisition module and a scene control measure processing module, wherein the scene control measure acquisition module is used for acquiring control measure information under at least two scenes;
the energy consumption information acquisition module is used for generating energy consumption information under corresponding scenes according to the control measure information under each scene, wherein each scene generates energy consumption information;
the emission list acquisition module is used for acquiring a regional atmosphere pollution source emission list corresponding to each scene according to the control measure information and the energy consumption information under each scene;
the simulation information acquisition module is used for respectively inputting the atmospheric pollution source emission lists of all regions into the air quality model so as to acquire corresponding simulation information;
and the reduction effect acquisition module is used for acquiring the pollutant reduction effect of each scene according to the simulation information of the scene.
10. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the method of obtaining an air quality improvement effect under different scenarios as claimed in any one of claims 1 to 8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116738812A (en) * | 2023-08-14 | 2023-09-12 | 中科三清科技有限公司 | Scene simulation system, computing platform 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 |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116738812A (en) * | 2023-08-14 | 2023-09-12 | 中科三清科技有限公司 | Scene simulation system, computing platform and storage medium |
| CN116738812B (en) * | 2023-08-14 | 2023-11-21 | 中科三清科技有限公司 | Scene simulation system, computing platform 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 |
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