CN113688505A - Method, system and device for quickly optimizing air quality data - Google Patents
Method, system and device for quickly optimizing air quality data Download PDFInfo
- Publication number
- CN113688505A CN113688505A CN202110864661.9A CN202110864661A CN113688505A CN 113688505 A CN113688505 A CN 113688505A CN 202110864661 A CN202110864661 A CN 202110864661A CN 113688505 A CN113688505 A CN 113688505A
- Authority
- CN
- China
- Prior art keywords
- data
- emission
- pollution source
- concentration
- pollution
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000005457 optimization Methods 0.000 claims abstract description 24
- 238000004364 calculation method Methods 0.000 claims abstract description 7
- 239000003344 environmental pollutant Substances 0.000 claims description 25
- 231100000719 pollutant Toxicity 0.000 claims description 25
- 230000006870 function Effects 0.000 claims description 20
- 230000009467 reduction Effects 0.000 claims description 19
- 238000004088 simulation Methods 0.000 abstract description 16
- 230000008569 process Effects 0.000 abstract description 7
- 239000013618 particulate matter Substances 0.000 description 17
- 238000006243 chemical reaction Methods 0.000 description 7
- 230000001186 cumulative effect Effects 0.000 description 5
- 238000009826 distribution Methods 0.000 description 5
- 230000000694 effects Effects 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 4
- 238000009792 diffusion process Methods 0.000 description 4
- 238000003915 air pollution Methods 0.000 description 3
- 238000007599 discharging Methods 0.000 description 3
- 230000005611 electricity Effects 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 241000287828 Gallus gallus Species 0.000 description 2
- 239000000809 air pollutant Substances 0.000 description 2
- 231100001243 air pollutant Toxicity 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000003860 storage Methods 0.000 description 2
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 1
- 230000001133 acceleration Effects 0.000 description 1
- 238000001311 chemical methods and process Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 239000003546 flue gas Substances 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012067 mathematical method Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000001556 precipitation Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Chemical compound O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06395—Quality analysis or management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention also provides a method, a system and a device for quickly optimizing the air quality data, wherein the system comprises the following steps: the data input module is used for inputting original data; the annual average contribution concentration module is used for determining the annual average contribution concentration of each pollution source to the sensitive points based on the original data and ranking; the emission optimization module is used for acquiring the optimal emission data of each pollution source under the maximum emission target of all pollution sources under the constraint condition based on the determined air quality target, the annual emission list original data and the annual average contribution concentration; and the emission source data adjusting module determines an emission source data adjusting mode based on the optimal emission data of each pollution source. The method and the device can quickly give concrete contribution of each source, solve the problem that the contribution of each source under each scene cannot be accurately given due to the limitation of the number of pollution sources, the number of grids, the calculation period and the like on the simulation calculation time, and solve the problem that the modeling process of a plurality of simulation scenes is complicated.
Description
Technical Field
The invention relates to the field of atmospheric pollution control and the field of computer big data processing, in particular to a method, a system and a device for quickly optimizing air quality data.
Background
With the continuous acceleration of the industrialization process and the aggravation of daily industrial product consumption of society, air pollution has been developed in different regions of the world, and has become a great restriction influencing the social and economic development and human life, potential risk hazards including physical health, safe production and the like are formed, and the air pollution is one of important factors influencing the social and economic development at present. In the field of atmospheric pollution treatment, how to monitor the atmospheric quality and form an accurate and effective practical atmospheric quality model for rechecking an area is an important prerequisite for effectively treating the local atmospheric pollution.
The air quality model is a mathematical method to model the physical and chemical processes that affect the diffusion and reaction of atmospheric pollutants. These models can simulate primary pollutants directly discharged into the atmosphere and secondary pollutants formed due to complex chemical reactions based on input meteorological data and pollution source information such as emission rates, stack heights, etc. These models are very important for air quality management, as they are used by many organizations to measure source share rates, while helping to formulate effective policies to curtail pollutant emissions. For example, an air quality model may be used to predict that a new pollution source will not meet the emissions and, if so, may give appropriate control measures. In addition, the air quality model may also predict the concentration of pollutants after the implementation of future new policy and regulations. The effectiveness of policy regulations can be evaluated as well as reducing human and environmental exposure.
Meanwhile, the accuracy judgment of the air quality model is an important precondition for the parallelism of the air quality model. The judgment of the accuracy of the air quality model is an important ring for judging whether the air quality model is effective or not and even whether the technical route is correct or not. In the aspect of judging the effect of the air quality model, the prior art mainly adopts a scene analysis method, a plurality of scenes are designed firstly, and then the scenes are simulated one by one through the air quality model, so that the quick judgment cannot be realized; in the aspect of air quality model assistant decision-making research, the prior art is mainly based on regional air quality model assistant decision-making research developed by a response surface model, and can quickly search for a desired scene, but is limited by a regional model (CAMQ) and the like, and cannot analyze the contribution of each specific source.
Therefore, at present, when environmental governance is increasingly important, how to quickly and accurately judge the air quality model and quickly provide contribution conditions of different pollution sources is a problem to be solved urgently.
Disclosure of Invention
In view of the above, the invention provides an accurate and rapid judgment scheme suitable for models with different scales and different air quality based on a basic technical route of linear programming, and can rapidly provide specific contribution of each source, thereby providing support for formulating an optimal scheme for atmospheric pollution emission reduction suitable for local management departments.
Specifically, the invention provides the following technical scheme:
in one aspect, the invention provides a method for quickly optimizing air quality data, which comprises the following steps:
s1, inputting original data, determining the annual average contribution concentration of each pollution source to the sensitive points, and ranking to obtain the annual average contribution concentration ranking of each pollution source to the sensitive points; the original data at least comprises annual emission list original data, ground data and sensitive point data;
s2, determining an air quality target, and solving the optimal emission data of each pollution source under the maximum emission target of all pollution sources under the constraint condition by combining the annual emission list original data and the annual average contribution concentration;
and S3, determining an emission source data adjusting mode based on the optimal emission data of each pollution source.
Preferably, the raw data further comprises pollution source data, ground meteorological data and high-altitude meteorological data; the original annual emission list data refer to emission data of each enterprise of the annual dimensional pollutant factors s.
Preferably, the determination of the annual average contribution concentration of each pollution source to the sensitive point is carried out by:
after the target area is determined, based on the annual emission list original data, the pollution source data, the ground data, the sensitive point data, the ground meteorological data and the high altitude meteorological data of the pollutant factors s of the pollution sources in the target area, the simulation is carried out through an air quality model, and the annual average contribution concentration of each pollution source to the sensitive points is obtained.
Preferably, the S2 further includes setting a target function based on the maximum target of the emission of all pollution sources, where the target function is:
wherein x isiAdjusting the proportion of i discharge of enterprises, alphaiThe discharge amount of a pollution source i is the discharge amount of the pollution source i, i is the pollution source set, and z is the discharge amount of all the pollution sources.
Preferably, the constraint conditions in S2 are:
the emission adjustment reduction proportion of each enterprise is not higher than gamma, and the value range of gamma is [0, 100% ]; and is
Annual average concentration contribution to sensitive points of not more than cj，cjIs the target concentration value of the air quality.
Preferably, the constraint condition of the objective function is:
wherein, i is a set of pollution sources i-1, 2, …, n, j is a set of sensitive points j-1, 2, …, m, αiIs the emission of a pollution source i, betai,jIs the unit emission contribution of the pollution source i to the sensitive point j, cjIs a target concentration value, x, of the determined air quality for the sensitive point jiIs the discharge amount adjusting proportion of the enterprise i.
Preferably, the emission adjustment ratio x has a value range of: x belongs to [0, gamma ], wherein gamma is the upper limit of the emission adjustment reduction proportion, and the value range of gamma is [0, 100% ].
Meanwhile, the invention also provides a system for quickly optimizing the air quality data, which comprises the following components:
the data input module is used for inputting original data, and the original data at least comprises original annual emission list data, ground data and sensitive point data;
the annual average contribution concentration module is used for determining the annual average contribution concentration of each pollution source to the sensitive points based on the original data, and ranking to obtain the annual average contribution concentration ranking of each pollution source to the sensitive points;
the emission optimization module is used for acquiring the optimal emission data of each pollution source under the maximum emission target of all pollution sources under the constraint condition based on the determined air quality target, the annual emission list original data and the annual average contribution concentration;
and the emission source data adjusting module determines an emission source data adjusting mode based on the optimal emission data of each pollution source.
Preferably, the annual average contribution concentration module determines the annual average contribution concentration of each pollution source to the sensitive point by the following steps:
after the target area is determined, based on the annual emission list original data, the pollution source data, the ground data, the sensitive point data, the ground meteorological data and the high altitude meteorological data of the pollutant factors s of the pollution sources in the target area, the simulation is carried out through an air quality model, and the annual average contribution concentration of each pollution source to the sensitive points is obtained.
Preferably, the emission amount optimization module further comprises a constraint condition unit, wherein the constraint condition unit is used for determining a constraint condition in the calculation of the optimal emission amount;
the constraint conditions are as follows: the emission adjustment reduction proportion of each enterprise is not higher than gamma, and the value range of gamma is (0, 100 percent)](ii) a And does not contribute more than c to the annual average concentration of sensitive pointsj，cjIs the target concentration value of the air quality.
Preferably, the emission optimization module is further configured to set an objective function based on the maximum target of the emission of all pollution sources, where the objective function is:
wherein x isiAdjusting the I emission of enterprisesRatio, αiThe discharge amount of a pollution source i is the discharge amount of the pollution source i, i is the pollution source set, and z is the discharge amount of all the pollution sources.
Preferably, the constraint condition of the objective function is:
wherein, i is a set of pollution sources i-1, 2, …, n, j is a set of sensitive points j-1, 2, …, m, αiIs the emission of a pollution source i, betai,jIs the unit emission contribution of the pollution source i to the sensitive point j, cjIs a target concentration value, x, of the determined air quality for the sensitive point jiIs the discharge amount adjusting proportion of the enterprise i.
Preferably, the emission adjustment ratio x has a value range of: x belongs to [0, gamma ], wherein gamma is the upper limit of the emission adjustment reduction proportion, and the value range of gamma is [0, 100% ].
Meanwhile, the invention also provides a device for quickly optimizing the air quality data, which at least comprises a memory, a processor and an input device, wherein the processor can call instructions from the memory to execute the method for quickly optimizing the air quality data.
Compared with the prior art, the technical scheme of the invention is based on a linear programming method, develops an atmospheric pollution simulation rapid effect evaluation tool suitable for air quality models with different scales and different air quality models, and can rapidly provide specific contributions of each source. The problem that contribution of each source under each scene cannot be accurately given due to the limitation of the number of pollution sources, the number of grids, the calculation period and the like on the simulation calculation time is solved, meanwhile, the problem that the modeling process of a plurality of simulation scenes is complicated is solved, and a plurality of optimization schemes can be quickly evaluated; meanwhile, man-machine interaction can be realized, and the use by a user is facilitated.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow diagram of an overall implementation of an embodiment of the present invention;
FIG. 2 is a schematic diagram of a prediction and emission reduction planning process according to an embodiment of the present invention;
FIG. 3 shows PM of each city in a thermal power enterprise pairing area in a certain area in 2018 years in accordance with an embodiment of the present invention10A concentration contribution;
FIG. 4 shows PM of a thermal power enterprise in a certain region in 2018 years in accordance with an embodiment of the present invention10A contribution;
FIG. 5 shows a PM of an urban thermal power enterprise in a certain area obtained after model optimization according to an embodiment of the present invention10A contribution;
fig. 6 is a comparison of contributions of the thermal power situation scenario and the optimization scenario of a certain area to the concentration of each city according to the embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without any inventive step, are within the scope of the present invention.
It will be appreciated by those of skill in the art that the following specific examples or embodiments are a series of presently preferred arrangements of the invention to further explain specific summary of the invention, and that such arrangements may be combined or otherwise used in conjunction with one another unless it is specifically contemplated that some or some of the specific examples or embodiments may not be combined or used in conjunction with other examples or embodiments. Meanwhile, the following specific examples or embodiments are only provided as an optimized arrangement mode and are not to be understood as limiting the protection scope of the present invention.
Example 1
In a specific embodiment, the core scheme of the present invention is illustrated with reference to fig. 1.
1. Pollution source contribution analysis
First, the contribution of the pollution source is analyzed to obtain the basis for judgment. The process mainly comprises the following steps:
(1) inputting data
In one embodiment, the input data may preferably include the following:
discharging list original data in the year: a year-dimension pollutant factor s (s stands for SO)2，NOx，PM10CO, etc.) emission data, e.g. PM10One year of emissions;
pollution source (e.g. chimney) data: the coordinates of the pollution source, the height of the pollution source, the emission rate of the pollution source, the diameter of a chimney, the temperature of flue gas, the size of a building and the like; the specific data type of the part can be adjusted based on different main pollution sources, so that the pollution condition of the pollution sources can be accurately described or the data related to the pollution contribution degree is taken as the main data.
Third, ground data: terrain data (USGS), land use data, etc.; the terrain data can be selected to have a resolution of 90m, the land use data can be selected to have a resolution of 30m, and other resolution data can be selected according to actual data conditions, area ranges and the like;
fourthly, ground meteorological data: air temperature, air pressure, relative humidity, water vapor pressure, wind, precipitation and the like;
high altitude meteorological data: temperature, humidity, air pressure, wind direction, wind speed, etc.;
data of sensitive spots (also called receptor spots or predictive spots): coordinates, altitude, mountain height, etc.
(2) In a more preferred embodiment, the pollution source contribution analysis employed in the present scheme is as follows:
selecting an area, such as a certain city;
inputting original annual emission list data, pollution source data, ground meteorological data, high altitude meteorological data, sensitive point data and the like of the pollutant factors s of all pollution sources in the area into an air quality model to be tested for simulation, wherein the air quality model can adopt models such as AERMOD, CALPUFF, CMAQ, CAMx and the like;
thirdly, outputting the annual contribution concentration of each pollution source to the sensitive points;
and fourthly, ranking the annual average contribution concentration of each pollution source to the sensitive points to obtain the annual average contribution concentration ranking of the sensitive points.
2. Atmospheric pollution simulation rapid reaction decision model
And forming a reaction decision model aiming at the atmospheric pollution on the basis of the pollution source contribution analysis result data. In a preferred embodiment, the reaction decision model may be constructed by:
(1) inputting data
Discharging list original data in the year: a year-dimension pollutant factor s (s stands for SO)2，NOx，PM10CO, etc.);
contribution concentration data of each pollution source: each pollution source contributes to the concentration of the pollution factors s of the sensitive points every year;
③ air quality target: i.e. target concentration value, unit: mu g/m3。
(2) In one embodiment, the reaction decision model is constructed as follows:
setting an objective function: under the determined concentration target value, the discharge z of all pollution sources is maximum;
determining constraint conditions:
a. the emission adjustment reduction proportion of each enterprise is not higher than gamma, gamma can be set by a user based on actual emission requirements, enterprise equipment conditions and the like, and the value range [0, 100% ] is preferably selected;
b. annual average concentration contribution to sensitive points of not more than cj，cjμ g/m in unit as target concentration value of air quality3；
③ the concrete formula:
x∈[0，γ] (3)
wherein: formula (1) is expressed by an objective function, and the pollutant discharge amount z is maximized under the determined target concentration value;
formula (2) is a constraint representing each pollution and a contribution constraint to each sensitive point;
the value range of the decision variable is represented by a formula (3);
the reference numerals in the above formulas (1) to (3) are shown in Table 1.
TABLE 1 model parameters
(3) Based on the above model, referring to the embodiment of fig. 2, the following steps are specifically performed:
selecting pollutant factor s (s stands for SO) of selected area2，NOx，PM10CO, etc.), the annual average contribution concentration of each pollution source to the pollutant factor s of the sensitive point, and a target concentration value input model of the air quality determined by the sensitive point j;
secondly, outputting the optimal discharge amount of the pollution source i;
and thirdly, determining an emission source group adjusting scheme, namely the emission amount of each pollution source to be reduced.
Example 2
In yet another embodiment, the solution of the present invention can also be implemented by means of a system module or an electronic device.
The system comprises:
the data input module is used for inputting original data, and the original data at least comprises original annual emission list data, ground data and sensitive point data;
the annual average contribution concentration module is used for determining the annual average contribution concentration of each pollution source to the sensitive points based on the original data, and ranking to obtain the annual average contribution concentration ranking of each pollution source to the sensitive points;
the emission optimization module is used for acquiring the optimal emission data of each pollution source under the maximum emission target of all pollution sources under the constraint condition based on the determined air quality target, the annual emission list original data and the annual average contribution concentration;
and the emission source data adjusting module determines an emission source data adjusting mode based on the optimal emission data of each pollution source.
In a more specific embodiment, the annual average contribution concentration module determines the annual average contribution concentration of each pollution source to the sensitive point by:
after the target area is determined, a pollutant factor s (s stands for SO) based on a pollution source in the target area2， NOx，PM10CO, etc.) to obtain the annual average contribution concentration of each pollution source to the sensitive points through simulation by an air quality model.
In a more specific embodiment, the optimization of the emission amount generally needs to match with actual emission data, expected data values, and the like in the area, which requires setting a constraint condition that meets the characteristics of local atmospheric pollutants, so as to obtain an optimal data configuration scheme under the constraint condition. Therefore, in the system, the emission optimization module further comprises a constraint condition unit, wherein the constraint condition unit is used for determining a constraint condition in the calculation of the optimal emission;
the constraint conditions are as follows: the emission adjustment reduction proportion of each enterprise is not higher than gamma, and the value range of gamma is (0, 100 percent)](ii) a And does not contribute more than c to the annual average concentration of sensitive pointsj，cjIs the target concentration value of the air quality.
In a more preferable mode, the emission optimization module is further configured to set an objective function based on the maximum emission targets of all pollution sources, and the relationship between the data can be simplified into a mathematical relationship with the main target data element through the setting of the objective function, and the objective function can be preferably set as follows:
wherein x isiAdjusting the proportion of i discharge of enterprises, alphaiThe discharge amount of a pollution source i is the discharge amount of the pollution source i, i is the pollution source set, and z is the discharge amount of all the pollution sources.
Setting corresponding constraints on the objective function based on the objective function and the actual pollutant emission data condition and the data conditions of geography, climate and the like of the area, wherein the mathematical expression of the constraint conditions of the objective function is preferably set as follows:
wherein, i is a set of pollution sources i-1, 2, …, n, j is a set of sensitive points j-1, 2, …, m, αiIs the emission of a pollution source i, betai,jIs the unit emission contribution of the pollution source i to the sensitive point j, cjIs a target concentration value, x, of the determined air quality for the sensitive point jiIs the discharge amount adjusting proportion of the enterprise i.
In the aspect of adjusting the emission amount of an individual pollution enterprise and the like, the emission amount needs to be determined according to the actual emission data volume in the area, and the preferable value range of the emission amount adjustment ratio x is as follows: x belongs to [0, gamma ], wherein gamma is the upper limit of the emission adjustment reduction proportion, and the value range of gamma is [0, 100% ].
In the setting mode of the system, the setting of each module can be adjusted based on the overall architecture of the system, so as to perform corresponding data fast optimization processing, and the conventional module structure adjustment should be considered to fall within the protection scope of the present invention.
In addition, the technical solution of the present invention can also be implemented by an air quality data fast optimization apparatus, which at least includes a memory, a processor, and an input device, where the processor can call instructions from the memory to execute the air quality data fast optimization method described above, or the apparatus can be equipped with the air quality data fast optimization system described above to execute corresponding system functions to implement fast optimization of air quality data.
Example 3
In this embodiment, the core scheme of the present invention is further described by taking an example of optimizing the scheme of actual air pollution emission in a certain area. In the present embodiment, the local PM is used10Emission reduction of pollution is exemplified.
Taking a certain regional thermal power enterprise as an example, an emission list of the certain regional thermal power industry in 2018 is adopted to perform atmospheric diffusion simulation on the region. PM of thermal power industry in 2018 area10The discharge amount is 0.58 ten thousand t.a-1. The thermal power industry emissions for this area vary widely from area to area.
In the embodiment, the air quality model CALPUFF is adopted to measure the PM of the thermoelectric enterprises in the area10The PM of each thermal power enterprise is tracked by developing and simulating the current situation10The contribution to the concentration of each city of the fenwei plains was discharged. And adopting a meteorological reference year of 2018, adopting LCC projection in all modes, and acquiring ground data and sounding data by a WRF model. The region simulation range is divided into 960km multiplied by 920km, the pixel size is 10km multiplied by 10km, the number of grid points is 96 multiplied by 92, information such as a discharge source of each thermal power enterprise needs to be input into the model, dry and wet settlement is considered, chemical reaction is not considered, and the concentration ratio of each thermal power enterprise to pollutants in each city is simulated and calculated.
Input parameters of the CALPUFF model comprise topographic data, meteorological data, emission source and other data: (1) obtaining terrain data (USGS) with the resolution of 90m and land data with the resolution of 30 m; (2) acquiring meteorological data and simulating a meteorological field, wherein ground data and sounding data are simulated by WRF; (3) pollutant emission concentration in a research area of pollution source parameters mainly considering PM of chimney of thermal power plant10Discharging, inputting in the form of point source, calculating parameters of models required by various sourcesIncluding location, stack height, stack diameter, discharge rate, and temperature, among others.
On the basis, the coordinates of each predicted point in the area are obtained, in this embodiment, the city center longitude and latitude coordinates of each city in the area are used as the coordinates of the predicted point.
Next, the present embodiment performs simulation and comparison according to the atmospheric emission situation in the current situation and the atmospheric emission situation in the optimized situation.
TABLE 2 prediction scheme statistics
Referring to fig. 3, in the present embodiment, 11 cities are used as receptors, and the PM in the source pair area is emitted to 13 adjacent cities10The annual average contribution concentration of emissions was simulated. The results show that the annual average cumulative contribution concentrations of the west salty new district, the fortune city and the Luoyang city in the region are 119.17 mu g m-3、118.98μg·m-3、95.47μg·m-3。PM10The main reason why the annual cumulative contribution concentration of emissions varies greatly is the significant variation in emissions. Specifically, the activity level is a main emission driving factor, and the emission of the thermal power industry is generally consistent with the spatial distribution of the activity level, wherein the total amount of Luoyang and Yuancheng accounts for nearly 29.6% of the total power generation amount of the region, the emission amount of the west salty new region is not large, but the influence of diffusion of other regions is large, so that the annual cumulative contribution concentration is high.
And based on the simulation result, further analyzing and obtaining the accumulated contribution concentration ranking condition of each enterprise discharge to each city year.
FIG. 4 shows PM of thermal power industry in the area of 201810The distribution condition of the cumulative contribution concentration of the emission years and the PM of the thermal power industry10The annual average contribution concentration of the emission is consistent with the distribution of the thermal power emission list of the region in 2018, wherein PM of Weinan city, Weian city and peripheral regions10The annual average contribution concentration is higher and is 13.84 mu g.m at most-3Mainly caused by diffusion influence in other areas such as the areas of the great town, the salty yang and the like around the area.
The results show that in 2018, PM of thermal power enterprises in New Western and western salty New districts, fortune cities and Luoyang cities in the region10The emission contributes to the maximum annual accumulated contribution concentration of each city, wherein in the emission sources of the fire and electricity enterprises in the region, the contribution of some enterprises in the west and salty new regions and the transport cities is large, so that corresponding control measures for the emission sources are emphatically enhanced, and the pollution contribution of the fire and electricity enterprises to the Fenwei plain can be effectively reduced.
And optimizing the emission plan further through a model based on the simulation data condition. The model is optimized here, i.e. the model in example 1 is used:
setting an objective function: under the determined concentration target value, the discharge z of all pollution sources is maximum;
determining constraint conditions:
a. the emission adjustment reduction proportion of each enterprise is not higher than gamma, gamma can be set by a user based on actual emission requirements, enterprise equipment conditions and the like, and the value range [0, 100% ] is preferably selected;
b. annual average concentration contribution to sensitive points of not more than cj，cjμ g/m in unit as target concentration value of air quality3；
③ the concrete formula:
an objective function:
constraint conditions are as follows:
decision variable value range: x is an element [0, gamma ]
In combination with the optimization model, in order to guarantee production, the PM of the thermoelectric enterprises in the area10The emission reduction proportion does not exceed 50 percent, namely the emission is controlled to be not less than 50 percent of the total emission of the original enterprise, and the emission reduction proportion of each emission source is planned.
Under the current situation, the thermal power grid in the areaThe total discharge amount of the discharge source is 0.58 ten thousand t.a-1The total contribution concentration of 791.07 μ g m is accumulated for each city year-3. And (4) solving the emission reduction proportion of each enterprise under the constraint condition by using the model to obtain an enterprise emission reduction scheme. The results show that the optimal scheme is to control the discharge of 9 of the Xixian New district, Weinan, Bao chicken and the like, and the other sources are normally discharged by combining the table 3.
Table 3 contribution of Fenwei plain thermal power enterprises to city concentration
With reference to fig. 5, the annual average contribution concentration of the atmospheric pollutants discharged by the thermal power industry under the optimized scenario is consistent with the contribution concentration distribution trend under the current scenario, wherein PM in peripheral areas such as west ' an ' and Weinan ' city is relatively consistent10The annual average contribution concentration is higher and is reduced by 0.14 percent compared with the highest value in 2018.
And analyzing to obtain the ranking condition of the annual accumulated contribution concentration of each enterprise to each city under the optimization scene based on the model optimization result.
The emission of the optimized thermal power industry in the region is estimated as follows: PM (particulate matter)10About 0.57 million t.a-1The annual contribution of the thermoelectric industry in the area to the annual contribution concentration of the major air pollutants is shown in figure 6. The current scenario shows a significant downward trend in the contribution concentration compared to the present scenario. The results of reducing emissions in this area will focus on two issues: the displacement and the proportion are reduced.
(1) The current situation shows that on one hand, the emission of the thermal power industry in the region is greatly different from region to region, namely PM10Among the discharge contributions, the contribution of luck city is the largest, followed by lulian and salted yang. The main cause of uneven discharge is significant variation in the amount of electricity generated. In particular, activity level is a major driver, and emissions from the thermal power industry are commonConsistent with the spatial distribution of power production.
(2) And (4) optimizing scene display, and adjusting the thermoelectric enterprises in the area to be optimal solutions under the target condition. At this time, the emission amount was the least changed, and the cumulative contribution concentration was reduced by 54.56. mu.g.m-3The total discharge amount after adjustment is 5701.08t · a-1. The salty new region, Weinan and Bao chicken in the Chinese and western have great potential for emission reduction, and the emission of the region needs to be controlled seriously in the next step. Therefore, the three cities provide targeted opportunities for optimizing and adjusting the structure and the layout of the regional thermal power industry so as to reduce the emission of air pollutants.
In summary, the invention introduces a high-resolution emission list to systematically analyze the contribution of pollutant emissions to the air quality, establishes a model based on a linear programming idea, analyzes the contribution concentration of thermal power emissions in a certain area to each city year in 2018 by using the model, and provides an optimal thermal power emission reduction scheme in the area. The simulation results show that: the contribution of the air quality is highly concentrated in Shanxi and Shaanxi high-power generation areas, and reference basis is provided for the industrial structure adjustment of the thermoelectric enterprises in the areas through optimized layout.
As described above for the specific embodiments of the present invention, it can be understood by those skilled in the art that all or part of the processes in the methods of the embodiments described above can be implemented by using a computer program to instruct related hardware, where the program can be stored in a computer readable storage medium, and when executed, the program can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A method for quickly optimizing air quality data is characterized by comprising the following steps:
s1, inputting original data, determining the annual average contribution concentration of each pollution source to the sensitive points, and ranking to obtain the annual average contribution concentration ranking of each pollution source to the sensitive points; the original data at least comprises annual emission list original data, ground data and sensitive point data;
s2, determining an air quality target, and solving the optimal emission data of each pollution source under the maximum emission target of all pollution sources under the constraint condition by combining the original annual emission list data and the annual average contribution concentration;
and S3, determining an emission source data adjusting mode based on the optimal emission data of each pollution source.
2. The method of claim 1, wherein the raw data further comprises pollution source data, ground meteorological data, and high altitude meteorological data; the original annual emission list data refer to emission data of each enterprise of the annual dimensional pollutant factors s.
3. The method of claim 1, wherein the determination of the annual average contribution concentration of each pollution source to the susceptible site is performed by:
after the target area is determined, the annual emission list original data, the pollution source data, the ground data, the sensitive point data, the ground meteorological data and the high altitude meteorological data of the pollutant factors s of the pollution sources in the target area are simulated through an air quality model, and the annual average contribution concentration of each pollution source to the sensitive points is obtained.
4. The method according to claim 1, wherein the S2 further comprises setting an objective function based on the maximum target of all pollutant emissions, the objective function being:
wherein x isiAdjusting the proportion of i discharge of enterprises, alphaiThe discharge amount of the pollution source i is the discharge amount of the pollution source set i, and the discharge amount of all the pollution sources z.
5. The method according to claim 1, wherein the constraint conditions in S2 are:
the emission adjustment reduction proportion of each enterprise is not higher than gamma, and the value range of gamma is [0, 100% ]; and is
Annual average concentration contribution to sensitive points of not more than cj，cjIs the target concentration value of the air quality.
6. The method of claim 4, wherein the constraint of the objective function is:
wherein, i is a set of pollution sources i-1, 2, …, n, j is a set of sensitive points j-1, 2, …, m, αiIs the emission of a pollution source i, betai,jIs the unit emission contribution of the pollution source i to the sensitive point j, cjIs a target concentration value, x, of the determined air quality for the sensitive point jiIs the discharge amount adjusting proportion of the enterprise i.
7. A system for rapid optimization of air quality data, the system comprising:
the data input module is used for inputting original data, and the original data at least comprises annual emission list original data, ground data and sensitive point data;
the annual average contribution concentration module is used for determining the annual average contribution concentration of each pollution source to the sensitive points based on the original data, and ranking to obtain the annual average contribution concentration ranking of each pollution source to the sensitive points;
the emission optimization module is used for acquiring the optimal emission data of each pollution source under the maximum emission target of all pollution sources under the constraint condition based on the determined air quality target, the annual emission list original data and the annual average contribution concentration;
and the emission source data adjusting module determines an emission source data adjusting mode based on the optimal emission data of each pollution source.
8. The system of claim 7, wherein the annual average contribution concentration module determines the annual average contribution concentration of each pollution source to the sensitive point by:
after the target area is determined, the annual emission list original data, the pollution source data, the ground data, the sensitive point data, the ground meteorological data and the high altitude meteorological data of the pollutant factors s of the pollution sources in the target area are simulated through an air quality model, and the annual average contribution concentration of each pollution source to the sensitive points is obtained.
9. The system of claim 7, wherein the emissions optimization module further comprises a constraint unit for determining constraints in the calculation of the optimal emissions;
the constraint conditions are as follows: the emission adjustment reduction proportion of each enterprise is not higher than gamma, and the value range of gamma is (0, 100 percent)](ii) a And does not contribute more than c to the annual average concentration of sensitive pointsj，cjIs the target concentration value of the air quality.
10. An air quality data fast optimization apparatus, characterized in that the apparatus at least comprises a memory, a processor and an input device, the processor can call instructions from the memory to execute the air quality data fast optimization method according to claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110864661.9A CN113688505A (en) | 2021-07-29 | 2021-07-29 | Method, system and device for quickly optimizing air quality data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110864661.9A CN113688505A (en) | 2021-07-29 | 2021-07-29 | Method, system and device for quickly optimizing air quality data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113688505A true CN113688505A (en) | 2021-11-23 |
Family
ID=78578355
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110864661.9A Pending CN113688505A (en) | 2021-07-29 | 2021-07-29 | Method, system and device for quickly optimizing air quality data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113688505A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114841437A (en) * | 2022-05-07 | 2022-08-02 | 中科三清科技有限公司 | Method and device for pre-evaluating contribution of emission source to air quality and electronic equipment |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106649960A (en) * | 2016-10-12 | 2017-05-10 | 环境保护部环境规划院 | Three-dimensional iterative computing method for environment capacity of multiple atmospheric pollutants |
| CN108458337A (en) * | 2017-07-28 | 2018-08-28 | 北京化工大学 | A kind of gas combustion apparatus of near zero pollutant discharge, method and its application |
| CN111815178A (en) * | 2020-07-10 | 2020-10-23 | 中科三清科技有限公司 | Air quality standard-reaching analysis method and device, electronic equipment and storage medium |
| CN112801422A (en) * | 2021-03-23 | 2021-05-14 | 南京智汇环境气象产业研究院有限公司 | Atmospheric pollution cooperative control method combining numerical model and linear programming model |
-
2021
- 2021-07-29 CN CN202110864661.9A patent/CN113688505A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106649960A (en) * | 2016-10-12 | 2017-05-10 | 环境保护部环境规划院 | Three-dimensional iterative computing method for environment capacity of multiple atmospheric pollutants |
| CN108458337A (en) * | 2017-07-28 | 2018-08-28 | 北京化工大学 | A kind of gas combustion apparatus of near zero pollutant discharge, method and its application |
| CN111815178A (en) * | 2020-07-10 | 2020-10-23 | 中科三清科技有限公司 | Air quality standard-reaching analysis method and device, electronic equipment and storage medium |
| CN112801422A (en) * | 2021-03-23 | 2021-05-14 | 南京智汇环境气象产业研究院有限公司 | Atmospheric pollution cooperative control method combining numerical model and linear programming model |
Non-Patent Citations (3)
| Title |
|---|
| 汤铃 等: "中国钢铁行业排放清单及大气环境影响研究", 《中国环境科学》 * |
| 马岩 等: "沧州主城区重点企业大气环境影响研究", 《环境影响评价》, vol. 41, no. 2, pages 16 - 19 * |
| 黄敬: "中国工业行业绿色发展效率评价分析", 《中国优秀硕士学位论文全文数据库基础科学辑》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114841437A (en) * | 2022-05-07 | 2022-08-02 | 中科三清科技有限公司 | Method and device for pre-evaluating contribution of emission source to air quality and electronic equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wu et al. | Dynamic assessment of urban economy-environment-energy system using system dynamics model: A case study in Beijing | |
| Wang et al. | Air pollution terrain nexus: A review considering energy generation and consumption | |
| Zhou et al. | A hybrid model for PM2. 5 forecasting based on ensemble empirical mode decomposition and a general regression neural network | |
| Lu et al. | Investigating the spatiotemporal non-stationary relationships between urban spatial form and land surface temperature: A case study of Wuhan, China | |
| CN110909483A (en) | Point source atmospheric pollutant emission list verification method based on gridding data | |
| CN114418442A (en) | Method for selecting pollutant treatment measures | |
| Haichao et al. | Atmospheric environmental impact assessment of a combined district heating system | |
| Liu et al. | Comprehensive quantitative evaluation of the water resource carrying capacity in Wuhan City based on the “human–water–city” framework: Past, present and future | |
| Ashok et al. | Adjoint-based computation of US nationwide ozone exposure isopleths | |
| CN115420854A (en) | Atmospheric pollutant tracing method based on forward and backward model combination | |
| Herrera et al. | Environmental impact of decentralized power generation in Santa Clara City, Cuba: An integrated assessment based on technological and human health risk indicators | |
| CN115601520A (en) | Fire simulation method and device based on two-dimensional prediction and multi-scale grid | |
| Zhang et al. | Incorporating health impacts into a differentiated pollution tax rate system: A case study in the Beijing-Tianjin-Hebei region in China | |
| Zhang et al. | Can new-type urbanization realize low-carbon development? A spatiotemporal heterogeneous analysis in 288 cities and 18 urban agglomerations in China | |
| Lan et al. | The future of China's urban heat island effects: A machine learning based scenario analysis on climatic-socioeconomic policies | |
| CN113298439A (en) | Population distribution-based environmental risk assessment method and device and computer equipment | |
| Tseng et al. | Assessing relocation strategies of urban air quality monitoring stations by GA-based compromise programming | |
| CN113688505A (en) | Method, system and device for quickly optimizing air quality data | |
| CN116611547A (en) | Atmospheric pollutant source forecasting method, device, equipment and storage medium | |
| Fan et al. | Spatiotemporal evolution characteristics, influencing factors of land use carbon emissions, and low-carbon development in Hubei Province, China | |
| Wang et al. | Effects of urban form on air quality: A case study from China comparing years with normal and reduced human activity due to the COVID-19 pandemic | |
| Zhang et al. | Machine learning assesses drivers of PM2. 5 air pollution trend in the Tibetan Plateau from 2015 to 2022 | |
| Zhang et al. | Natural and human factors influencing urban particulate matter concentrations in central heating areas with long-term wearable monitoring devices | |
| CN117314023B (en) | Atmospheric pollution data analysis method, system and computer storage medium | |
| Croitoru et al. | A state of the art regarding urban air quality prediction models |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |