CN117195585A - Atmospheric multi-pollutant emission reduction optimization regulation method and system based on dynamic scene simulation - Google Patents
Atmospheric multi-pollutant emission reduction optimization regulation method and system based on dynamic scene simulation Download PDFInfo
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Abstract
The invention discloses an atmospheric multi-pollutant emission reduction optimization regulation method and system based on dynamic scene simulation, which belong to the field of environmental system analysis and mainly comprise the following steps: inputting a reference emission list into an air quality model, verifying the corresponding relation between pollutant emission and ambient air quality, and calculating pollution contribution of various emission sources to ambient air; aiming at various emission sources, emission reduction measures with different emission reduction intensities (between 0 and 100 percent) are designed to form a dynamic emission reduction scene; an atmospheric pollutant emission list corresponding to the dynamic emission reduction scene is calculated, an air quality model is input, and the air quality target accessibility is judged; and circularly adjusting the strength of the emission reduction measures under the dynamic emission reduction scene according to the pollution emission reduction optimization regulation strategy until the air quality target is reached, and finally outputting the optimal and feasible dynamic emission reduction scene and emission reduction measures contained in the scene, scientifically and quantitatively determining the multi-pollutant emission reduction measures and emission reduction, thereby realizing the expected effect of improving the quality of the ambient air with the minimum emission reduction cost.
Description
Technical Field
The invention relates to the technical field of environmental system analysis, in particular to an atmospheric multi-pollutant emission reduction optimization regulation method and system based on dynamic scene simulation.
Background
The prediction of future medium and long term economic and social development-pollutant emission-environmental quality evolution law usually adopts a scene analysis method. Based on various key assumptions provided for the important possible evolution of influence factors such as economy, industry or technology, various possible schemes are conceived in the future, and the influence and effect of different policies or measures on the future environmental air quality development trend are evaluated by combining various model methods, so that scientific basis is provided for manager decision. The common practice is to design or assume various atmospheric pollution control or emission reduction situations, evaluate and compare pollution emission reduction or environmental improvement effects under different control situations by means of a series of integrated quantitative methods, such as an economic-energy-environment model (CGE, LEAP model) or an air quality model (WRF-CMAQ), and screen out control situations and key measure tasks for achieving the aim of environmental air quality or emission reduction.
The existing method and application cases related to the analysis of the atmospheric pollutant emission reduction situation are mainly fixed situations preset, certain flexibility is lacking in screening combination of atmospheric pollutant emission reduction measures, and the best feasible scheme meeting the environmental air quality or emission reduction targets is difficult to obtain. Meanwhile, aiming at the current atmosphere combined pollution situation, PM2.5, ozone and the like are main objects for achieving the standard of air quality and continuously improving, comprehensive emission reduction is needed for multiple pollutants, how to dynamically regulate and control the emission reduction situations of the multiple pollutants with different sources, how to determine the atmosphere pollution emission reduction measures and emission reduction amounts thereof through an optimization decision method, and the related scientific quantitative method support is still lacking at present.
Disclosure of Invention
The invention aims to solve the problems in the existing pollutant emission reduction analysis method, and provides an atmospheric multi-pollutant emission reduction optimization regulation and control method and system based on dynamic scenario simulation, which realize the expected environmental air quality improvement effect with minimum emission reduction cost.
The aim of the invention is realized by the following technical scheme:
in a first aspect, an atmospheric multi-pollutant emission reduction optimization regulation method based on dynamic scenario simulation is provided, which comprises the following steps:
s1, compiling a reference emission list of main pollution source atmospheric pollutants, and identifying key emission objects;
s2, inputting a reference emission list into an air quality model, verifying the corresponding relation between pollutant emission and ambient air quality, and determining pollution contribution of various emission sources to ambient air;
s3, aiming at various emission sources, designing emission reduction measures with different intensities to form a dynamic emission reduction scene;
s4, estimating an atmospheric pollutant emission list corresponding to the emission reduction scene in the step S3, inputting an air quality model, and judging whether an air quality target is reachable;
s5, if the air quality target in the step S4 is not reachable, adjusting the strength of the emission reduction measures under the dynamic emission reduction scene according to the pollution emission reduction optimization regulation strategy;
s6, forming a new dynamic emission reduction scene according to the step S5, and repeating the steps S4-S5 until the air quality target in the step S4 is reached, and outputting the current dynamic emission reduction scene and measures thereof.
In some possible embodiments, the method for optimizing and controlling the emission reduction of multiple pollutants in the atmosphere based on dynamic scenario simulation is provided, wherein the main pollution sources comprise a fixed combustion source, a process source, a solvent use source, a mobile source, other sources and the like.
In some possible embodiments of the present invention, the method for optimizing and controlling emission reduction of multiple pollutants in the atmosphere based on dynamic scenario simulation, wherein the step of inputting a reference emission list into an air quality model to verify a correspondence between pollutant emission and ambient air quality includes:
and outputting a grid emission list required by the air quality model WRF-CMAQ by using the list processing model SMOKE, inputting the grid emission list into the WRF-CMAQ model to obtain an air quality simulation result, and comparing and verifying the air quality simulation result with an air quality monitoring result.
In some possible embodiments of the present invention, in the method for optimizing and controlling emission reduction of multiple pollutants in the atmosphere based on dynamic scenario simulation, in step S2, the pollution contribution of various emission sources to the ambient air is clarified, including:
based on an air quality model WRF-CMAQ, a classical Brute-Force response evaluation method is applied to simulate different emission situations, so that pollution contributions of different key emission sources to the quality of the ambient air are obtained.
In some possible embodiments, the method for optimizing and controlling the emission reduction of the atmospheric multi-pollutant based on dynamic scene simulation includes atmospheric pollution control and structure optimization adjustment.
In some possible embodiments of the present invention, in the method for optimizing and controlling emission reduction of multiple pollutants in the atmosphere based on dynamic scenario simulation, designing emission reduction measures with different intensities in step S3 includes:
and quantifying the intensity of the atmospheric pollution emission reduction measures to between 0 and 100 percent by a scene analysis method.
The intensity of the air pollution treatment measures is related to the popularization rate of the treatment technology and the reduction rate of pollutants, and the regulation and control range of the popularization rate and the reduction rate is between 0 and 100 percent; the structural optimization adjustment measure is related to the structural proportion of energy, industry and transportation, and the structural proportion regulation range is between 0 and 100 percent. In some possible embodiments of the present invention, the method for optimizing and controlling emission reduction of multiple pollutants in an atmosphere based on dynamic scenario simulation includes:
in the aspect of atmospheric pollution treatment, a single-target nonlinear constraint optimization model is constructed by applying cost analysis, and pollution emission reduction intensity and target requirements are coupled to realize the optimization regulation and control of atmospheric pollution emission reduction; the objective function is that the atmospheric pollution treatment cost is minimum, and the decision variable is the popularity of each atmospheric pollution treatment technology and the reduction rate of pollutants; the constraint condition is the allowable emission of the atmospheric pollutants, and the application range of the popularization rate and the reduction rate of the atmospheric pollution treatment technology.
In the aspect of structural optimization adjustment, the current and possible future environmental policies are considered, and the emission reduction strength of different structural optimization adjustment measures is enhanced; and (3) based on the ranking of the contribution sizes of the pollution sources identified by the S2, firstly determining an industrial structure, then optimizing an energy structure, and finally adjusting a traffic and transportation structure.
In a second aspect, an atmospheric multi-pollutant emission reduction optimization regulation system based on dynamic scenario simulation is provided, including:
the reference emission list programming module is configured to program a reference emission list of main pollution source atmospheric pollutants and identify key emission objects;
the pollution contribution calculation module is configured to input a reference emission list into the air quality model, verify the corresponding relation between pollutant emission and ambient air quality and determine pollution contributions of various emission sources to the ambient air;
the emission reduction scene design module is configured to design emission reduction measures with different intensities aiming at various emission sources to form a dynamic emission reduction scene;
the air quality target accessibility judging module is configured to estimate an atmospheric pollutant emission list corresponding to the emission reduction scene, input an air quality model and judge the air quality target accessibility;
the emission reduction scene dynamic adjustment module is configured to adjust the emission reduction measure intensity under the dynamic emission reduction scene according to the pollution emission reduction optimization regulation strategy until the air quality target is reached.
It should be further noted that the technical features corresponding to the above-mentioned option embodiments may be combined with each other or replaced to form a new technical scheme without collision.
Compared with the prior art, the invention has the beneficial effects that:
(1) According to the invention, through a scene analysis method, the intensity of the atmospheric pollution emission reduction measures (between 0 and 100 percent) is quantized, and a dynamic emission reduction scene is constructed; and the air quality simulation based on the emission reduction scene and the dynamic optimization and regulation of the emission reduction measure intensity form the best feasible emission reduction scene meeting the environmental air quality target and the emission reduction measure combination corresponding to the best feasible emission reduction scene, thereby providing scientific and quantitative method support for the accurate prevention and control of the atmospheric pollution.
(2) In one example, the invention constructs a single-target nonlinear constraint optimization model based on the air pollution treatment cost-effectiveness analysis, and couples the emission reduction measure intensity and the emission reduction requirement of the air quality improvement simulation under different emission reduction scenes to form a systematic air multi-pollutant emission reduction optimization regulation method.
(3) In one example, the invention aims at dynamic emission reduction scenes, forms scene lists with different emission reduction intensities through different source emission reduction measure intensities (namely emission reduction ratio), applies an air quality model (WRF-CMAQ) to simulate concentration values of air quality indexes (such as PM2.5 or ozone), and circularly iterates until the environmental air quality meets the target requirement, and outputs feasible emission reduction scenes and measures thereof. In the loop iteration, the atmospheric pollution optimization regulation model is coupled, the intensity variation amplitude of the emission reduction measures is controlled, the feasible emission reduction scene of optimizing and obtaining the minimum emission reduction cost and the measures thereof can be realized, and the expected environmental air quality improvement effect can be realized by the minimum emission reduction cost.
(4) In one example, before the emission reduction scene simulation, the atmospheric pollutant reference emission list is input into the air quality model simulation, so that the corresponding relation verification between the reference pollutant emission amount and the current ambient air quality is performed, and the reliability of the emission reduction scene simulation is ensured.
Drawings
FIG. 1 is a flow chart of an atmospheric multi-pollutant emission reduction optimization regulation method based on dynamic scenario simulation, which is shown in the embodiment of the invention;
FIG. 2 shows the main atmospheric pollution abatement measures and categories according to the embodiments of the present invention;
fig. 3 is a technical roadmap of an atmospheric pollution emission reduction optimization regulation method with dynamic scenario simulation according to an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully understood from the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In addition, the technical features of the different embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
In an exemplary embodiment, referring to fig. 1, an atmospheric multi-pollutant emission reduction optimization regulation method based on dynamic scenario simulation is provided, which includes the following steps:
s1, compiling a reference emission list of main pollution source atmospheric pollutants, and identifying key emission objects;
s2, inputting a reference emission list into an air quality model, verifying the corresponding relation between pollutant emission and ambient air quality, and determining pollution contribution of various emission sources to ambient air;
s3, aiming at various emission sources, designing emission reduction measures with different intensities to form a dynamic emission reduction scene;
s4, estimating an atmospheric pollutant emission list corresponding to the emission reduction scene in the step S3, inputting an air quality model, and judging whether an air quality target is reachable;
s5, if the air quality target in the step S4 is not reachable, adjusting the strength of the emission reduction measures under the dynamic emission reduction scene according to the pollution emission reduction optimization regulation strategy;
s6, forming a new dynamic emission reduction scene according to the step S5, and repeating the steps S4-S5 until the air quality target in the step S4 is reached, and outputting the current dynamic emission reduction scene and measures thereof.
The step S1 specifically includes:
according to the atmospheric pollutant emission list, the guidelines are compiled, the regional (or urban) atmospheric pollutant emission list is compiled, which comprises fixed combustion sources (including power plants, industrial boilers, resident incinerators and the like), process sources (including industries of steel, cement, glass, ceramics, petrifaction, chemical industry and the like), solvent use sources (industries of automobile and parts manufacturing, furniture, package printing, electronics and the like), mobile sources (buses, trucks, non-road mobile machinery, inland ships and the like), other sources (including living solvent use, catering oil fume, dust raising and the like), and main pollutants comprise SO 2 NOx, PM, VOCs and NH 3 Etc.
The important areas with larger pollutant discharge amount and important industries are identified through the discharge list, and important attention is paid to the popularization and application conditions of the optimal practical technology (BAT) of industrial enterprises and the structural characteristics of the areas (such as coal burning use, surplus productivity and the like), so that the discharge objects with larger emission reduction potential are identified.
Further, step S2 specifically includes:
based on the reference year atmospheric pollutant emissions list obtained in step S1, the grid emissions list required by the air quality model WRF-CMAQ is output by using the list processing model SMOKE. And carrying out model verification according to an air quality model WRF-CMAQ parameterization scheme so as to ensure that the corresponding relation between the reference pollutant emission and the current ambient air quality is established. The model validation procedure was as follows:
(1) inputting an atmospheric pollutant emission list of a reference year into an air quality model WRF-CMAQ, and simulating to obtain the environmental air quality (including fine particulate matter PM2.5 or ozone concentration and the like); (2) selecting a current environmental air quality monitoring station as an evaluation point, taking the atmospheric environment PM2.5 or ozone observation concentration as an evaluation index according to actual requirements, and comparing model simulation interpolation results corresponding to longitude and latitude coordinates of the evaluation point; (3) referring to the existing recommended model evaluation scheme and standard, the correlation coefficient (R), the standardized mean deviation (NMB), the standardized mean error (NME) and the consistency coefficient (IOA) between the simulation result and the observation result are selected as evaluation parameters, and the corresponding relation between pollutant emission and environmental air quality is considered to be better when R is more than or equal to 0.60, NMB is less than or equal to +/-20%, NME is less than or equal to 45% and IOA is more than or equal to 0.70.
Based on an air quality model WRF-CMAQ, a classical Brute-Force response evaluation method (a light-off method) is applied to simulate different emission situations, so that pollution contributions of different key sources to the air quality of the environment are obtained.
Further, the emission reduction measures comprise air pollution control and structure optimization adjustment, wherein the intensity of the air pollution emission reduction measures is quantized to between 0 and 100% through a scene analysis method. Specifically, based on an atmospheric pollutant emission list, a dynamic emission reduction scenario is designed for pollutant emission objects and pollution contributions thereof, taking national and local environmental policies and quality target requirements into consideration.
First, a base policy scenario is determined. Under the precondition of keeping the existing environmental policy unchanged by considering the social economic development and the increment of pollution emission, a basic policy scenario is formed and is used as the 'lower limit' of the dynamic emission reduction scenario.
Next, a maximum potential scenario is determined. Aiming at the existing air pollution source, the maximum emission reduction potential mining application is realized without considering the socioeconomic cost, and the maximum emission reduction potential mining application is used as the upper limit of the dynamic emission reduction scene.
Then, a dynamic emission reduction scenario is set. Between the basic policy scene of 'lower limit' and the maximum potential scene of 'upper limit', the emission reduction scene is initially set, and the emission reduction measure intensity is higher than that of the basic policy scene, but cannot exceed the maximum potential scene.
In the dynamic emission reduction scenario, corresponding emission reduction measures are formulated corresponding to all main atmospheric pollution sources, and the emission reduction measures comprise atmospheric pollution treatment and structure optimization adjustment (see an example in fig. 2). The pollution control aspect mainly comprises ultra-low emission reconstruction in thermal power industry, steel industry and cement industry, deep control in other important industries and the like; the structural optimization adjustment aspect mainly comprises energy structural optimization (reducing a coal-fired boiler, improving energy efficiency of key industries and the like), industrial structural optimization (reducing productivity of high pollution industries, improving substitution rate of pollutant emission sources and the like) and traffic structural optimization (increasing the duty ratio of electric vehicles and the like).
The intensity of the emission reduction measures is dynamically adjustable, the atmospheric pollution treatment measures correspond to the popularization rate of the corresponding treatment technology and the pollutant reduction rate thereof, the adjustment range of the popularization rate and the reduction rate is between 0 and 100 percent, and the pollution emission reduction intensity proportion (%) of various pollution source treatment technologies after application is calculated respectively; the structure optimization adjustment measures correspond to corresponding structure proportions, for example, the adjustment ranges of the fuel substitution rate and the surplus capacity elimination rate are also between 0 and 100 percent, and the pollution emission reduction intensity proportion (%) of various pollution sources after the structure optimization adjustment is calculated respectively.
Different pollution emission reduction scenarios are emission reduction measure combinations with different intensities, and examples are shown in table 1. The dynamic emission reduction scenes comprise initial scenes and preferred scenes, wherein when the initial scenes are the scenes, the initial scenes are intermediate possible scenes set between basic policy scenes and maximum potential scenes; the preferable scene is the final feasible scene after the optimization and regulation of the air pollution and the emission reduction through the air quality model simulation. And optimizing a regulation and control process, and increasing or reducing the pollution emission reduction intensity in the dynamic emission reduction scene according to the model simulation result and the optimization and control strategy.
TABLE 1 pollution abatement scenario and abatement measure intensity for the method
Further, the step S4 specifically includes:
based on a reference year atmospheric pollutant emission list, pollutant emission reduction amounts under corresponding scenes are calculated through emission reduction measure intensities (namely emission reduction ratios) under different emission reduction scenes, so that a pollutant emission list of the emission reduction scenes is formed, and a grid emission list required by an air quality model WRF-CMAQ is output by using a list processing model SMOKE. And inputting the grid emission list of each scene into an air quality model WRF-CMAQ to simulate to obtain the environmental air quality (including fine particulate matter PM2.5 or ozone concentration and the like) under different scenes, interpolating to estimate the pollutant concentration simulation value of a representative air quality monitoring station, and judging whether the air quality under each emission reduction scene reaches an expected target value.
In practical application, the air quality simulation result under the dynamic emission reduction situation is compared, and the air quality improvement effect of the combination of different emission reduction measures under the dynamic emission reduction situation is evaluated by increasing or reducing the pollution emission reduction intensity, so that the final feasible situation after the air quality model simulation and the air pollution emission reduction optimization regulation is obtained.
Further, the pollution emission reduction optimization regulation strategy in the step S5 comprises air pollution control regulation and structure optimization regulation.
Specifically, in the aspect of atmospheric pollution control, the application of the cost-effectiveness analysis and the optimization decision model are usually applied to the optimization selection of pollution control measures, but are often used singly or aiming at a certain pollutant such as SO2 and NOx, and few atmospheric multi-pollution emission reduction optimization and control methods are formed by simulation coupling with a scene analysis method and an air quality model; the objective function is that the pollution treatment cost of the main emission source atmospheric pollutants is minimum, and the decision variable is the popularization rate of each pollutant treatment technology and the reduction rate of the pollutants; the constraint condition is the allowable emission of the atmospheric pollutants, the application range of the popularization rate and the reduction rate of the atmospheric pollution treatment technology, and the like.
(1) Firstly, an objective function is established, wherein the objective function is the minimum cost of reducing the emission of the air pollution, and the objective function is expressed as:
minC T ＝∑ i ∑ p C i,p
wherein C is T For the total emission reduction cost (ten thousand yuan), the unit of C is the comprehensive emission reduction cost (yuan/unit product),i is industry; p is a contaminant.
The method is applied by various atmospheric pollution treatment technologies of pollution emission reduction generalization, focuses on the rule of influence of pollution reduction rate on emission reduction cost, builds a cost function of emission reduction cost-reduction rate aiming at a certain pollutant in a certain industry, and is expressed as:
c i,p ＝f i,p (η i,p )
wherein η is a reduction rate (%); f is a functional relationship between the unit emission reduction cost and the reduction rate.
In practical situations, the pollution emission reduction cost is related to a certain pollution treatment technology application, and the pollution reduction rate is often an interval value, and the pollution treatment technology cost in the industry can be restrained by using a discrete method, which is expressed as follows:
C i,p ＝A i ×∑ e (c i,p,e ×λ i,p,e )
wherein A is activity level; lambda is the prevalence (%); and e is a pollution treatment technology.
In addition, the emission reduction cost data of the pollution control technology in specific practice includes the emission reduction cost of unit product (such as Yuan/kWh) and the emission reduction cost of unit pollutant (such as Yuan/tSO) 2 ) Two measurement modes can be converted by the following formula:
wherein, c w Emission reduction cost per unit pollutant (yuan per unit pollutant); c c The comprehensive emission reduction cost (yuan/unit product) of unit product; ef is the pollutant emission factor (pollutant amount per unit product) of the product production; η is the reduction rate (%) of the end technique.
The comprehensive cost of the pollution treatment technology consists of two parts, namely investment cost and operation cost, and is expressed as follows:
c i,p,e ＝cfix i,p,e +copt i,p,e
wherein cfix is annual average fixed cost (ten thousand yuan); cfix 0 Initial investment cost (ten thousand yuan); copt is the running cost (ten thousand yuan); a is the discount rate; t is the service life.
(2) And secondly, determining constraint conditions mainly comprising the allowable emission amount of atmospheric pollutants, the popularization rate of the atmospheric pollution treatment technology and the reduction rate of the atmospheric pollutants.
Atmospheric pollutants allow for emission constraints.
The method converts the atmospheric environmental quality constraint into the maximum allowable emission constraint of the pollution source, and is expressed as follows:
E p ＝∑ i E i,p ≤E p,g
wherein i is industry; p is a contaminant; e (E) p A final emission (t) of the pollutant p; e (E) i,p Final emission (t) of the industry i pollutant p; e (E) p,g The total allowable emissions determined for the target emissions limit of the pollutant p, i.e., the atmospheric environmental capacity of the pollutant p.
Similar to the expression for cost, the expression for allowable emissions is considered as a discrete form:
in ef i,p The emission factor of the pollutants p in the industry i is slightly different in the power industry, and the emission factor in the power industry is the emission factor of coal, so that the pollutant emission in the power industry also needs to be multiplied by activity intensity, namely the standard coal consumption of power supply, and represents the amount of coal (in g/kWh) required to be consumed per generation of 1kWh of electricity;the reduction rate (%) of the pollution control technology e for the pollution p of the industry i.
II, atmospheric pollution treatment technology popularity constraint conditions.
The sum of the popularity rates of all technologies in the same category does not exceed 1, namely the technology popularity rate constraint condition is expressed as follows:
0≤λ i,p ≤1
0≤λ i,p,e ≤1
0≤∑ e λ i,p,e ≤1
and III, limiting the pollutant reduction rate of the air pollution treatment technology.
The key industry cut-off rate constraint is expressed as:
in the formula, theta i,p The overall cut-off rate (%) for the reference year of the contaminant p is the key industry i.
In addition, the reduction rate of any one pollution control technology to a certain atmospheric pollutant is first (0, 1) constraint, but considering that the removal level cannot reach 100% in practical application, a maximum reduction rate is set, expressed as:
0＜η i,p ≤η i,p,BAT
wherein eta is i,p,BAT The optimal reduction rate (%) of the pollution control technology for controlling the pollutants p is the key industry i.
(3) Finally, solving the optimization model, and applying genetic algorithm iterative optimization to solve the single-target nonlinear constraint optimization model. The genetic algorithm is a random search algorithm which refers to natural selection and natural genetic mechanism of organisms, the search process is a process of 'population' generation-generation 'evolution', selection of the superior and inferior stages is carried out through an evaluation function, and the evolution of the organisms is simulated through intersection and variation. The superior and inferior elimination is the core of the search algorithm, and the final effect of the algorithm is different according to different strategies of the superior and inferior elimination. The genetic algorithm defines the solution of the problem as the individual of the evolution object, and carries out selection, crossing (hybridization) and mutation treatment on the population consisting of a plurality of individuals, and the population is 'evolved' for one generation every time the population is treated. As long as the evaluation and selection strategies are proper, individuals which are relatively close to the optimal solution can appear in the population after a plurality of times of evolution, and the approximate optimal solution of the problem is correspondingly obtained.
The regulation strategy in the aspect of structure optimization adjustment is as follows:
firstly, sorting according to the key emission objects identified in the step S1 and the pollution contributions of various sources determined in the step S2, and preferentially adjusting the pollution sources with large pollution contributions and large emission reduction potential.
Secondly, firstly, determining an industrial structure (such as reducing the capacity of the high pollution industry, improving the substitution rate of a pollution emission source, and the like), then optimizing an energy structure (such as reducing a coal-fired boiler, improving the energy efficiency of the key industry, and the like), and finally adjusting a transportation structure (such as increasing the revolution iron proportion, increasing the electric vehicle proportion, and the like).
Further, the step S6 specifically includes:
and (5) optimizing a regulation and control result according to the step (S5), and adjusting the pollution emission reduction intensity in the dynamic emission reduction scene, namely increasing or reducing the pollution emission reduction proportion. And S4, calculating pollutant emission reduction amounts corresponding to the emission reduction measure intensities (or emission reduction ratios) in the current emission reduction scene, forming an emission reduction scene list, inputting the emission reduction scene list into an air quality model, simulating to obtain the air quality (including the PM2.5 of fine particles or the concentration of ozone and the like) of the environment, and judging whether the air quality in the emission reduction scene reaches an expected target value or not. If the air quality does not reach the expected target value, repeating the step S5, increasing the optimization regulation and control force (including increasing the popularization rate of the BAT (best practice technology), increasing the elimination rate of surplus productivity and the like), increasing the pollution emission reduction intensity in the dynamic emission reduction scene, increasing the pollution emission reduction proportion, forming a new optimization regulation and control result, and entering the next simulation, wherein the technical route is shown in figure 3.
And (3) until the air quality reaches an expected target value, considering the dynamic emission reduction scene as an optimal emission reduction measure intensity combination meeting the air quality target requirement, and outputting the dynamic emission reduction scene as a final air pollution emission reduction optimal regulation and control scheme and a feasible recommended emission reduction measure.
According to the invention, through a scene analysis method, the intensity of the atmospheric pollution emission reduction measures (between 0 and 100 percent) is quantized, and a dynamic emission reduction scene is constructed; and the air quality simulation based on the emission reduction scene and the dynamic optimization and regulation of the emission reduction measure intensity form the best feasible emission reduction scene meeting the environmental air quality target and the emission reduction measure combination corresponding to the best feasible emission reduction scene, thereby providing scientific and quantitative method support for the accurate prevention and control of the atmospheric pollution.
Further, in the air quality scene simulation in the conventional scene analysis method, usually, whether 2 or 3 fixed scenes can achieve an air quality target is compared, and one scene meeting the conditions is selected as a feasible scene; the resulting scenario is often not a "minimal emissions reduction cost" scenario, but a "more suitable scenario" than other scenarios. Aiming at the dynamic emission reduction scene with the second characteristic, a scene list with different emission reduction intensities is formed through different source emission reduction measure intensities (namely emission reduction ratio), and an air quality model (WRF-CMAQ) is applied to simulate an air quality index (such as PM) 2.5 Or ozone) concentration value, and iterating circularly until the environmental air quality meets the target requirement, and outputting a feasible emission reduction scene and measures thereof. In the loop iteration, the atmospheric pollution optimization regulation model is coupled, the intensity variation amplitude of the emission reduction measures is controlled, and the feasible emission reduction scene of 'minimum emission reduction cost' and measures thereof can be obtained by optimizing. In addition, before the emission reduction scene simulation, the method inputs the reference emission list of the atmospheric pollutants into the air quality model simulation, so that the corresponding relation verification between the reference pollutant emission and the current environmental air quality is performed, and the credibility of the emission reduction scene simulation is ensured.
In another exemplary embodiment, an atmospheric multi-pollutant emission reduction optimization regulation system based on dynamic scenario simulation is provided, comprising:
the reference emission list programming module is configured to program a reference emission list of main pollution source atmospheric pollutants and identify key emission objects;
the pollution contribution calculation module is configured to input a reference emission list into the air quality model, verify the corresponding relation between pollutant emission and ambient air quality and determine pollution contributions of various emission sources to the ambient air;
the emission reduction scene design module is configured to design emission reduction measures with different intensities aiming at various emission sources to form a dynamic emission reduction scene;
the air quality target accessibility judging module is configured to estimate an atmospheric pollutant emission list corresponding to the emission reduction scene, input an air quality model and judge the air quality target accessibility;
the emission reduction scene dynamic adjustment module is configured to adjust the emission reduction measure intensity under the dynamic emission reduction scene according to the pollution emission reduction optimization regulation strategy until the air quality target is reached.
In another exemplary embodiment, the invention provides a computer storage medium having stored thereon computer instructions that, when executed, perform the steps associated with the method for optimizing regulation of atmospheric multi-pollutant emission reduction based on dynamic scenario simulation.
Based on such understanding, the technical solution of the present embodiment may be essentially or a part contributing to the prior art or a part of the technical solution may be embodied in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another exemplary embodiment, the invention provides a terminal, which comprises a memory and a processor, wherein the memory stores computer instructions capable of running on the processor, and the processor executes relevant steps in the atmospheric multi-pollutant emission reduction optimization regulation method based on dynamic scene simulation when running the computer instructions.
The processor may be a single or multi-core central processing unit or a specific integrated circuit, or one or more integrated circuits configured to implement the invention.
Embodiments of the subject matter and the functional operations described in this specification can be implemented in: tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and structural equivalents thereof, or a combination of one or more of them. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier for execution by, or to control the operation of, data processing apparatus. Alternatively or additionally, the program instructions may be encoded on a manually-generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode and transmit information to suitable receiver apparatus for execution by data processing apparatus.
The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform corresponding functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).
Processors suitable for the execution of a computer program include, by way of example, general and/or special purpose microprocessors, or any other type of central processing unit. Typically, the central processing unit will receive instructions and data from a read only memory and/or a random access memory. The essential elements of a computer include a central processing unit for carrying out or executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks, etc. However, a computer does not have to have such a device. Furthermore, the computer may be embedded in another device, such as a mobile phone, a Personal Digital Assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device such as a Universal Serial Bus (USB) flash drive, to name a few.
While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any invention or of what may be claimed, but rather as descriptions of features of specific embodiments of particular inventions. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. On the other hand, the various features described in the individual embodiments may also be implemented separately in the various embodiments or in any suitable subcombination. Furthermore, although features may be acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.
Similarly, although operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In some cases, multitasking and parallel processing may be advantageous. Moreover, the separation of various system modules and components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.
The foregoing detailed description of the invention is provided for illustration, and it is not to be construed that the detailed description of the invention is limited to only those illustration, but that several simple deductions and substitutions can be made by those skilled in the art without departing from the spirit of the invention, and are to be considered as falling within the scope of the invention.
Claims (8)
1. The atmospheric multi-pollutant emission reduction optimization regulation method based on dynamic scene simulation is characterized by comprising the following steps of:
s1, compiling a reference emission list of main pollution source atmospheric pollutants, and identifying key emission objects;
s2, inputting a reference emission list into an air quality model, verifying the corresponding relation between pollutant emission and ambient air quality, and calculating pollution contribution of various emission sources to ambient air;
s3, aiming at various emission sources, designing emission reduction measures with different intensities to form a dynamic emission reduction scene;
s4, checking an atmospheric pollutant emission list corresponding to the emission reduction scene in the step S3, inputting an air quality model, and judging whether an air quality target is reachable;
s5, if the air quality target in the step S4 is not reachable, adjusting the strength of the emission reduction measures under the dynamic emission reduction scene according to the pollution emission reduction optimization regulation strategy;
s6, forming a new dynamic emission reduction scene according to the step S5, and repeating the steps S4-S5 until the air quality target in the step S4 is reached, and outputting the current dynamic emission reduction scene and measures thereof.
2. The method for optimizing and controlling the emission reduction of multiple pollutants in the atmosphere based on dynamic scenario simulation according to claim 1, wherein the main pollution sources comprise a fixed combustion source, a process source, a solvent use source, a mobile source and other sources.
3. The method for optimizing and controlling the emission reduction of multiple pollutants in the atmosphere based on dynamic scenario simulation according to claim 1, wherein the step of inputting a reference emission list into an air quality model to verify the correspondence between the pollutant emission amount and the environmental air quality comprises the following steps:
and outputting a grid emission list required by the air quality model WRF-CMAQ by using the list processing model SMOKE, inputting the grid emission list into the WRF-CMAQ model to obtain an air quality simulation result, and comparing and verifying the air quality simulation result with an air quality monitoring result.
4. The method for optimizing and controlling the emission reduction of multiple pollutants in the atmosphere based on dynamic scenario simulation according to claim 3, wherein the step S2 is characterized by defining the pollution contribution of various emission sources to the ambient air, and comprises the following steps:
based on an air quality model WRF-CMAQ, a classical Brute-Force response evaluation method is applied to simulate different emission situations, so that pollution contributions of different key emission sources to the quality of the ambient air are obtained.
5. The method for optimizing and controlling the emission reduction of multiple pollutants in the atmosphere based on dynamic scenario simulation according to claim 1, wherein the emission reduction measures comprise air pollution control and structure optimization adjustment.
6. The method for optimizing and controlling the emission reduction of multiple pollutants in the atmosphere based on dynamic scenario simulation according to claim 1, wherein the step S3 of designing emission reduction measures with different intensities comprises the following steps:
and quantifying the intensity of the atmospheric pollution emission reduction measures to between 0 and 100 percent by a scene analysis method.
The intensity of the air pollution treatment measures is related to the popularization rate of the treatment technology and the reduction rate of pollutants, and the regulation and control range of the popularization rate and the reduction rate is between 0 and 100 percent; the structural optimization adjustment measure is related to the structural proportion of energy, industry and transportation, and the structural proportion regulation range is between 0 and 100 percent.
7. The method for optimizing and controlling the emission reduction of multiple pollutants in the atmosphere based on dynamic scenario simulation according to claim 1, wherein the pollution emission reduction optimizing and controlling strategy comprises the following steps:
in the aspect of atmospheric pollution treatment, a single-target nonlinear constraint optimization model is constructed by applying cost analysis, and pollution emission reduction intensity and target requirements are coupled to realize the optimization regulation and control of atmospheric pollution emission reduction; the objective function is that the atmospheric pollution treatment cost is minimum, and the decision variable is the popularity of each atmospheric pollution treatment technology and the reduction rate of pollutants; the constraint condition is the allowable emission of the atmospheric pollutants, and the application range of the popularization rate and the reduction rate of the atmospheric pollution treatment technology.
In the aspect of structural optimization adjustment, the current and possible future environmental policies are considered, and the emission reduction strength of different structural optimization adjustment measures is enhanced; and (3) based on the ranking of the contribution sizes of the pollution sources identified by the S2, firstly determining an industrial structure, then optimizing an energy structure, and finally adjusting a traffic and transportation structure.
8. Atmospheric multi-pollutant emission reduction optimization regulation and control system based on dynamic scene simulation is characterized by comprising:
the reference emission list programming module is configured to program a reference emission list of main pollution source atmospheric pollutants and identify key emission objects;
the pollution contribution calculation module is configured to input a reference emission list into the air quality model, verify the corresponding relation between pollutant emission and ambient air quality and determine pollution contributions of various emission sources to the ambient air;
the emission reduction scene design module is configured to design emission reduction measures with different intensities aiming at various emission sources to form a dynamic emission reduction scene;
the air quality target accessibility judging module is configured to estimate an atmospheric pollutant emission list corresponding to the emission reduction scene, input an air quality model and judge the air quality target accessibility;
the emission reduction scene dynamic adjustment module is configured to adjust the emission reduction measure intensity under the dynamic emission reduction scene according to the pollution emission reduction optimization regulation strategy until the air quality target is reached.
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