CN114841438B - Method and device for pre-evaluating influence of emission source on air quality and electronic equipment - Google Patents
Method and device for pre-evaluating influence of emission source on air quality and electronic equipment Download PDFInfo
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Abstract
The invention provides a method, a device and electronic equipment for pre-evaluating the influence of an emission source on air quality, wherein the method comprises the following steps: acquiring a first gridding emission list based on a known emission list in a simulation area containing a forecast area; inputting the first gridding discharge list into a first air quality model for simulating air quality, adjusting the first gridding discharge list based on an error between a simulation result and a corresponding monitoring result, and repeating the simulation and adjustment until the error between the simulation result and the monitoring result is smaller than a preset value to obtain a second gridding discharge list in a simulation area; removing or adding a preset emission source in the forecast area in the second gridding emission list to obtain a third gridding emission list; and inputting the second and third gridding emission lists into a second air quality model respectively to forecast the air quality, and determining the influence of a preset emission source on the air quality based on a forecast result. The pre-evaluation of the influence of the emission source on the air quality is realized.
Description
Technical Field
The invention relates to the technical field of air quality monitoring, in particular to a method and a device for pre-evaluating the influence of an emission source on air quality and electronic equipment.
Background
The current simulation forecast of the heavy-spot source mainly uses an air quality model to simulate the air quality. In the related technology, a large amount of pollution source data is needed, and the pollution source data needs to be generally checked to obtain accurate and complete atmospheric pollution source emission data. If the emission data of the atmospheric pollution source in a certain city or region is incomplete or the standard year of the list is earlier, effective simulation forecast cannot be carried out.
Disclosure of Invention
According to an aspect of the present invention, there is provided a method of pre-evaluating an effect of an emission source on air quality, comprising: acquiring a first gridding emission list based on a known emission list in a simulation area containing a forecast area; inputting the first gridding discharge list into a first air quality model for simulating air quality, adjusting the first gridding discharge list based on an error between a simulation result and a corresponding monitoring result, and repeating the simulation and adjustment until the error between the simulation result and the monitoring result is smaller than a preset value to obtain a second gridding discharge list in a simulation area; removing or adding a preset emission source in the forecast area in the second gridding emission list to obtain a third gridding emission list; and respectively inputting the second gridding emission list and the third gridding emission list into a second air quality model for forecasting the air quality, and determining the influence of a preset emission source on the air quality based on a forecasting result.
In some embodiments, adjusting the first gridded emissions manifest based on an error between the simulation results and the corresponding monitoring results includes: for each region within the simulation region: in the case that the simulation result of the area is higher than the corresponding monitoring result, reducing the discharge amount of each grid in the area in the first gridded discharge list based on the error between the simulation result and the corresponding monitoring result; in the case where the simulation result of the area is lower than the monitoring result, the discharge amount of each grid in the area in the first gridded discharge list is increased based on an error between the simulation result and the corresponding monitoring result.
In some embodiments, reducing or increasing the emissions of each grid in the area in the first grid emissions list comprises: and reducing or increasing the discharge amount of each grid in the area in the first gridded discharge list in an equal proportion, wherein the reduced or increased proportion is positively correlated with the error.
In some embodiments, the simulation result of each area is an average value of grids corresponding to each monitored station in the area, and the corresponding monitoring result is an average value of each monitored station in the area.
In some embodiments, the removing or adding a preset emission source in the forecast area in the second gridding emission list to obtain a third gridding emission list comprises: generating a fourth gridding emission list of the preset emission source according to emission data of the preset emission source in the forecast area; and coupling the second gridding emission list and the fourth gridding emission list to obtain a third gridding emission list which is removed or added with the preset emission source.
In some embodiments, inputting the second gridded discharge list and the third gridded discharge list into the second air quality model respectively for forecasting the air quality comprises: and respectively inputting the parts corresponding to the forecast areas in the second gridding emission list and the third gridding emission list into a second air quality model to forecast the air quality.
In some embodiments, the above method further comprises: for each candidate emission source of a plurality of candidate emission sources within a forecast area: removing or adding the candidate emission source in the second gridding emission list to obtain a third gridding emission list corresponding to the candidate emission source; inputting a third gridding emission list corresponding to the candidate emission source into a first air quality model to simulate air quality, and obtaining a simulation result corresponding to the candidate emission source; determining the influence value of the candidate emission source on the air quality according to the simulation result corresponding to the candidate emission source and the simulation result obtained when the second gridding emission list is obtained; one or more candidate emission sources are selected from the plurality of candidate emission sources for pre-evaluation based on the impact value corresponding to each candidate emission source in the plurality of candidate emission sources.
In some embodiments, a plurality of influence values of each candidate emission source in a plurality of time periods and a plurality of years of historical synchronization are determined, and the influence values in the plurality of time periods and the years of historical synchronization are obtained through simulation based on the emission amount and meteorological data corresponding to the time periods; wherein, for each of the plurality of time periods: and if a plurality of influence values of the candidate emission source in the years of the historical synchronization of the time period meet the preset condition, selecting the candidate emission source for pre-evaluation in the time period.
According to another aspect of the present invention, there is provided an apparatus for pre-evaluating the impact of an emission source on air quality, comprising: the system comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for acquiring a first gridding emission list based on a known emission list in a simulation area containing a forecast area; the adjusting module is used for inputting the first gridding discharge list into a first air quality model for simulating air quality, adjusting the first gridding discharge list based on an error between a simulation result and a corresponding monitoring result, and repeatedly simulating and adjusting until the error between the simulation result and the monitoring result is smaller than a preset value to obtain a second gridding discharge list in a simulation area; the determining module is used for eliminating or adding a preset emission source in the forecast area in the second gridding emission list to obtain a third gridding emission list; and the pre-evaluation module is used for respectively inputting the second gridding emission list and the third gridding emission list into the second air quality model to forecast the air quality, and determining the influence of a preset emission source on the air quality based on a forecast result.
According to still another aspect of the present invention, there is provided an electronic apparatus including: a processor; and a memory storing a program, wherein the program comprises instructions which, when executed by the processor, cause the processor to perform the method of one or more embodiments of the invention.
According to yet another aspect of the present invention, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform a method of one or more embodiments of the present invention.
According to one or more technical schemes provided by the embodiment of the invention, the emission list of the forecast area is obtained by adjusting based on the known emission list and the monitoring result of the air quality, then the preset emission source in the forecast area is added or removed in the emission list, so that the emission list considering the preset emission source and not considering the preset emission source is obtained, the influence of the preset emission source on the air quality is pre-evaluated based on the emission list considering the preset emission source and not considering the preset emission source, the evaluation of the influence of the emission source on the air quality can be realized under the condition that the emission list compiling is not carried out on the forecast area, and the pre-evaluation cost can be reduced.
Drawings
Further details, features and advantages of the invention are disclosed in the following description of exemplary embodiments with reference to the accompanying drawings, in which:
FIG. 1 illustrates a flow chart of a method of pre-evaluating the impact of emission sources on air quality in accordance with an exemplary embodiment of the present invention;
FIG. 2 illustrates a flow chart of a method of determining an emission source for pre-evaluation in accordance with an exemplary embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating the structure of an apparatus for pre-evaluating the impact of emissions sources on air quality in accordance with an exemplary embodiment of the present invention;
FIG. 4 illustrates a block diagram of an exemplary electronic device that can be used to implement an embodiment of the invention.
Detailed Description
Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present invention. It should be understood that the drawings and the embodiments of the invention are for illustration purposes only and are not intended to limit the scope of the invention.
It should be understood that the various steps recited in the method embodiments of the present invention may be performed in a different order and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the invention is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description. It should be noted that the terms "first", "second", and the like in the present invention are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in the present invention are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that reference to "one or more" unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present invention are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
For a more complete and thorough understanding of the present invention, the terminology herein is set forth below.
The term "emissions manifest" refers to a collection of quantities of atmospheric pollutants emitted into the atmosphere by various emission sources over a span of time and a spatial region. The administrative district division subject for the emission manifest compilation may be a county (district), a city, or a province (a direct municipality, an autonomous district), etc.
The term "air quality model" refers to a mathematical tool for simulating air quality in a large scale range from horizontal and vertical directions by applying meteorological principles and mathematical methods to reproduce the processes of pollutant transportation, reaction, removal and the like in the atmosphere. The Air Quality Model may include, but is not limited to, a Nested Air Quality Prediction Mode System (NAQPMS), a third Generation Air Quality Prediction and assessment System (CMAQ), weather and chemical mode coupling (Weather Research and monitoring Model coupled with chemistry, WRF-Chem), or an extended integrated Air Quality Model (CAMx), among others.
Embodiments of the present invention provide a solution for pre-evaluating the impact of emissions sources on air quality, which is described below with reference to the accompanying figures.
Fig. 1 shows a flowchart of a method of pre-evaluating the influence of emission sources on air quality according to an exemplary embodiment of the present invention, which includes steps S101 to S104, as shown in fig. 1.
Step S101, acquiring a first gridded emission list based on a known emission list in a simulation area containing a forecast area.
In the present embodiment, the forecast area may include geographical ranges divided by administrative districts, such as counties (districts), cities, or provinces (prefectures, municipalities), and the like. It should be understood that in the present embodiment, the forecast area may also include geographical areas divided in other ways.
As one embodiment, the simulated area may include only the forecast area. For example, the forecast area is a county (district), city, or province, etc. As another embodiment, the simulated region may include the forecast region and a perimeter region of the forecast region, thereby taking into account the transmission of the perimeter region to the forecast region. As an example, the forecast area may be a city, and the surrounding areas may include one or more cities that are adjacent to the forecast area. As another example, the simulated area includes the forecast area and a geographic area within a preset distance around the forecast area.
There are known emission lists in the related art, which may include regional, national, continent, global emission lists on a regional scale. By way of example, known emissions manifests may include, but are not limited to: china Multi-resolution Emission Inventory for China, MEIC, asian regional Emission Inventory (MIX), global atmosphere Research Emission Database (EDGAR), etc. The discharge lists of the county (district), the city and the province are compiled in partial county (district), city, province and other areas.
In the present embodiment, performing a gridding process on the emission list may obtain a gridded emission list (simply referred to as a gridded emission list). In general, a grid emission list refers to an emission list formed by distributing the emission amount thereof in a geographical grid on the basis of an established emission list. Taking the MEIC as an example, corresponding emission lists can be provided according to different years, model simulation areas, industries and space-time resolutions, and the emission lists comprise multi-layer nested high space-time resolution emission lists for air quality models such as NAQPMS, CMAQ, WRF-Chem or CAMx.
Generally, the accuracy of an emission list having a large area scale is not as high as that of an emission list having a small area scale. As an example, the national-level emission list covers a plurality of cities, but the part of the national-level emission list corresponding to each city has a large error with the actual emission list of the city, so the emission list of a smaller-scale area such as the city is compiled. The emission list is large relative to the area size, wherein the emission list of the local area is also called local emission list. Some areas (e.g., counties (districts), cities) do not have localized emissions lists compiled. In step S101, a grid emission list (referred to as a first grid emission list) based on a known emission list in a simulation area including a forecast area is obtained, and in a subsequent step, the emission list of the forecast area is adjusted based on the first grid emission list. The list of known emissions within the simulation area may be from one or more data sources. When the known emission list in the simulation area comes from a plurality of data sources, the emission lists of the plurality of data sources are coupled to obtain the known emission list in the simulation area. As an example, a grid emission list of city A and cities surrounding city A is obtained from the MEIC. As another example, if the forecast area and the area surrounding the forecast area are made up of at least two data sources, the emissions manifest for the simulated area is a coupling of the at least two data sources.
And S102, inputting the first gridding discharge list into a first air quality model to simulate air quality, adjusting the first gridding discharge list based on an error between a simulation result and a corresponding monitoring result, and repeating the simulation and adjustment until the error between the simulation result and the monitoring result is less than a preset value to obtain a second gridding discharge list in a simulation area.
The air quality model may simulate or forecast air quality based on the gridded emissions manifest. Herein, the air quality model outputting future air quality is referred to as forecast, and the air quality model outputting past air quality is referred to as simulation.
In step S102, the first air quality model may be NAQPMS, CMAQ, WRF-Chem, CAMx, or the like. In steps S101 and S102, the first gridded emissions manifest is in a data format recognizable by the first air quality model. This embodiment does not limit this. As an example, CMAQ relies on external programs to estimate the amount of pollution sources' emissions, location, and time variations when processing the emissions source information. The inputs in the first gridded emissions list are on the same horizontal and vertical spatial scales and cover the same time period as used in the air quality model simulation.
Air quality models typically simulate air quality using a gridded emissions list and an meteorological model. In step S102, the meteorological ambient field corresponding to the first grid emission schedule is also input into the first air quality model. In step S102, the weather pattern is a historical weather ambient field. As an example, the historical weather ambient field may be obtained based on known techniques. As an example, topographic data of a forecast area and meteorological data during a preset history period (e.g., three years of history) are acquired, and a meteorological background field during the preset history period is generated using a meteorological pattern (e.g., WRF).
In step S102, the monitoring result may be an actual measurement value of the monitored site. Based on the comparison between the monitoring results and the simulation results, the difference between the first gridded emissions list and the actual or localized emissions list may be evaluated, the error between the monitoring results and the simulation results may be used to adjust the first gridded emissions list, and the above process may be repeated to obtain new simulation results based on the adjusted first gridded emissions list until the error between the monitoring results and the simulation results is less than or equal to a predetermined value (e.g., 5%). When the error between the monitoring result and the simulation result is less than or equal to the preset value, the adjusted first gridded emission list can be regarded as a localized emission list of the simulation area, and is referred to as a second gridded emission list in the invention.
As an embodiment, in step S102, adjusting the first gridded emissions list based on an error between the simulation result and the corresponding monitoring result includes: for each zone within the simulation zone: in the case that the simulation result of the area is higher than the corresponding monitoring result, reducing the discharge amount of each grid in the area in the first grid discharge list based on the error between the simulation result and the corresponding monitoring result; in the case where the simulation result of the area is lower than the monitoring result, the discharge amount of each grid in the area in the first gridded discharge list is increased based on an error between the simulation result and the corresponding monitoring result.
As an embodiment, the reducing or increasing the emission amount of each grid in the area in the first gridded emission list comprises: and reducing or increasing the discharge amount of each grid in the area in the first gridding discharge list in an equal proportion, wherein the reduced or increased proportion is positively correlated with the error. As an example, the simulation results are 15% higher (error) than the monitoring results, reducing the emissions of each grid in the area by 15%. As another example, the simulation results are 10% lower (error) than the monitoring results, increasing the emissions of each grid in the area by 10%. It should be understood that other ways of adjusting the data based on errors are possible, e.g., non-proportional decreases or increases; the error may also be determined based on different statistical methods, which are not limited in this embodiment. Different adjustment methods and error evaluation methods may have slight differences in precision, but are more favorable for pre-evaluating precision compared with the method of directly using a known emission list, a localized emission list does not need to be compiled, and time, labor and material resources are saved.
As an embodiment, the simulation result of each area is a mean value of a grid corresponding to each monitored station in the area, and the corresponding monitoring result is a mean value of each monitored station in the area. The grid is associated with the monitored site based on the geographic range covered by the grid and the geographic location of the monitored site. As an example, a region includes 10 monitored sites, the mean of the monitoring within the region may be an arithmetic average of the monitored values of the 10 monitored sites, and the mean of the simulation within the region may be an arithmetic average of the simulated values of the grid associated with the 10 monitored sites.
In step S102, the simulation result may include the concentration of one or more atmospheric pollutants, and the like. Accordingly, the monitoring results include the concentration of one or more atmospheric pollutants corresponding to the simulation results, and the like. In the case of multiple atmospheric pollutants, the error between the simulation result and the monitoring result corresponding to each pollutant can be determined separately, and the overall error is determined based on multiple errors, but the method is not limited thereto, and the error calculation can be performed by using a known statistical method.
And step S103, eliminating or adding a preset emission source in the forecast area in the second gridding emission list to obtain a third gridding emission list.
For a preset emission source in a forecast area, in order to evaluate the influence of the preset emission source on the air quality of the preset area, an emission list considering the preset emission source and an emission list not considering the preset emission source are determined. As an example, in step S103, when the preset emission source is included in the second meshed emission list obtained in the foregoing step S102, the second meshed emission list is an emission list considering the preset emission source, the preset emission source is removed from the second meshed emission list, and a third meshed emission list corresponding to the preset emission source is obtained, and the third meshed emission list corresponding to the preset emission source is an emission list not considering the preset emission source. As another example, in step S103, when the second gridded emission list obtained in the foregoing step S102 does not include the preset emission source, the second gridded emission list is an emission list without considering the preset emission source, the preset emission source is added to the second gridded emission list, and a third gridded emission list corresponding to the preset emission source is obtained, and the third gridded emission list corresponding to the preset emission source is an emission list with considering the preset emission source.
As an embodiment, the removing or adding the preset emission source in the forecast area in the second gridding emission list to obtain a third gridding emission list corresponding to the preset emission source includes: generating a fourth gridding emission list of the preset emission source according to emission data of the preset emission source in the forecast area; and coupling the second gridding emission list and the fourth gridding emission list to obtain a third gridding emission list which is removed or added with the preset emission source.
Based on the foregoing steps S101 to S103, an emission list in consideration of the preset emission source and an emission list in consideration of the preset emission source in the simulation area are obtained. Thus, the influence of the preset emission source on the air quality of the forecast area may be pre-evaluated based on the emission list considering the preset emission source and the emission list not considering the preset emission source.
And step S104, inputting the second gridding emission list and the third gridding emission list into a second air quality model respectively to forecast the air quality, and determining the influence of a preset emission source on the air quality based on a forecast result.
In order to reduce the computational resources required for processing, in step S104, the parts corresponding to the prediction regions in the second and third grid emission lists are respectively input into the second air quality model for air quality prediction.
In this embodiment, the second air quality model may be NAQPMS, CMAQ, WRF-Chem, CAMx, or the like.
In one embodiment, the first air quality model in step S102 is the same as the second air quality model in step S104, which is beneficial to improve the accuracy. For example, the first air quality model and the second air quality model may both be WRF-Chem. When the WRF-Chem is used for pre-evaluation, the requirement on a pollution source is high, complete pollution source emission data needs to be provided, if the pollution source emission data is inaccurate, and the evaluation result has a large error, the data based on the preset emission source can be used for high-precision pre-evaluation through the gridding emission list determined in the steps from S101 to S103, and the WRF-Chem is used for secondary conversion fully considered when the WRF-Chem is used for environment influence evaluation, so that the evaluation result is accurate.
As another embodiment, the first air quality model and the second air quality model may be different, and the data format of the grid emissions manifest is adjusted to fit the corresponding air quality model when processing the grid emissions manifest. Is beneficial to improving the application range.
In this embodiment, the preset emission source may include one or more emission sources. When the preset emission source is one emission source, the emission list considering the single emission source and the emission list not considering the single emission source can be obtained, and the influence of the single emission source on the air quality of the forecast area can be pre-evaluated. When the preset emission source is a plurality of emission sources (i.e., a combination of emission sources), the emission list considering the plurality of emission sources and the emission list not considering the plurality of emission sources may be obtained to pre-evaluate the influence of the plurality of emission sources on the air quality of the forecast area.
In the present embodiment, the foregoing steps S101 to S103 are data processing stages, and based on the known emission list, the historical data (the monitoring result of the historical air quality, the historical meteorological pattern) and the emission data of the preset emission source, the emission list considering the preset emission source and the emission list not considering the preset emission source are obtained. The aforementioned step S104 is a pre-evaluation phase for pre-evaluating the influence of the preset emission source on the air quality of the forecast area based on the emission list considering the preset emission source and the emission list not considering the preset emission source.
Typically, a plurality of emission sources are included in the forecast area, and pre-evaluating all of the emission sources may require more computational resources. Therefore, in some embodiments, the emission source is selected from a plurality of emission sources in the forecast area, and the selected emission source is pre-evaluated to reduce computational resources and improve forecast timeliness. Emission source selection is described below.
Fig. 2 shows a flowchart of a method of determining an emission source for pre-evaluation according to an exemplary embodiment of the present invention, which includes steps S201 to S204, as shown in fig. 2.
The process of steps S201 to S203 is performed for each of a plurality of candidate emission sources within the forecast area.
Step S201, removing or adding the candidate emission source from the second gridding emission list to obtain a third gridding emission list corresponding to the candidate emission source.
In step S201, a second gridded emissions list is obtained in step S102.
As an example, when the candidate emission source is included in the second gridded emission list obtained in the foregoing step S102, the second gridded emission list is an emission list considering the candidate emission source, the candidate emission source is removed from the second gridded emission list, and a third gridded emission list corresponding to the candidate emission source is obtained, and the third gridded emission list is an emission list not considering the candidate emission source. As another example, when the candidate emission source is not included in the second gridded emission list obtained in the foregoing step S102, the second gridded emission list is an emission list without considering the candidate emission source, and the candidate emission source is added to the second gridded emission list to obtain a third gridded emission list, which is an emission list with considering the candidate emission source.
Step S202, inputting the third gridding emission list corresponding to the candidate emission source into the first air quality model to simulate the air quality, and obtaining a simulation result corresponding to the candidate emission source.
And step S203, determining the influence value of the candidate emission source on the air quality according to the simulation result corresponding to the candidate emission source and the simulation result when the second gridding emission list is obtained.
In step S203, the simulation result in the second gridded emission list is obtained in step S102.
As an embodiment, the influence value may be determined based on a magnitude of a difference between the simulation result of the candidate emission source and the simulation result in which the error is smaller than a preset value. The impact value indicates the magnitude of the impact of the preferred emissions source on air quality. In this embodiment, the magnitude of the difference may be determined based on various statistical methods. As an example, a method of determining an error between the monitoring result and the simulation result in the aforementioned step S102 may be adopted.
And step S204, selecting one or more candidate emission sources from the candidate emission sources for pre-evaluation based on the influence value corresponding to each candidate emission source in the candidate emission sources.
In step S204, as an embodiment, candidate emission sources having an impact value greater than or equal to a preset size may be selected for the pre-evaluation in step S104. As another example, the candidate emissions sources may be ranked according to impact values, with the top candidate emissions sources selected.
In this embodiment, the candidate emissions sources may include a combination of one or more emissions sources. When the candidate emission source is one emission source, the emission list considering the single emission source and the emission list not considering the single emission source can be obtained, and the influence value of the single emission source on the air quality of the forecast area can be determined. When the candidate emission source is a combination of a plurality of emission sources, the emission lists considering the plurality of emission sources and the emission lists not considering the plurality of emission sources can be obtained, and the influence values of the plurality of emission sources on the air quality of the forecast area can be determined.
The preset emission source according to the embodiment of the present invention may include one or more emission sources selected based on the above steps, and may also include an artificially set emission source. As an example, the aforementioned preset discharge sources are discharge sources A, B and C selected based on the above steps. As another example, the aforementioned preset discharge sources are discharge sources A, B and C selected based on the above steps, while including the manually set discharge source D.
As one embodiment, the time periods are divided, and the emission sources for pre-evaluation in each time period are respectively determined. Specifically, a plurality of influence values of each candidate emission source in a plurality of time periods and a plurality of years of historical synchronization are determined, and the influence values of the years of historical synchronization in each time period are obtained by simulation based on the emission amount and meteorological data corresponding to the time periods; wherein, for each of the plurality of time periods: and if a plurality of influence values of the candidate emission source in the years of the historical synchronization of the time period meet the preset condition, selecting the candidate emission source for pre-evaluation in the time period. According to the embodiment, the periodic change of the meteorological conditions and the periodic change of the emission amount of the emission source are considered, so that the emission source which is subjected to pre-evaluation and corresponds to each time period is determined, the emission source which is subjected to pre-evaluation is the emission source which has a large influence in the time period, and the pre-evaluation is the emission source which has a large influence on each time period. Exemplary periodic changes in meteorological conditions may include: the prevailing wind direction and the atmospheric layer junction stability of each month or season have periodic changes in one year, the prevailing wind directions of 1 month and 7 months have great difference, northwest wind prevails in 1 month, the discharge source in the northwest direction of the site has great influence on the site, southeast wind prevails in 7 months, and the discharge source in the southeast direction of the site has great influence on the site. On the premise that the emission source emission amount is not changed, the emission sources having large influence on the station have large difference. The periodic variation in the amount of emissions of the exemplary emissions source may include: seasonal variation of enterprise emissions, seasonal variation of straw combustion emissions, and the like.
As an example, each year is divided into 12 months, each month is used as one time period, and one year includes 12 time periods. For each month, a plurality of impact values for each candidate emission source over the years of historical synchronization for that month are determined, e.g., 3 or 5 years of historical synchronization. For each month, if a plurality of influence values of the candidate emission source in the years of the month are all larger (or partially larger) than a preset value or are ranked in the front of a plurality of candidate emission sources, determining that the candidate emission source is pre-evaluated in the month. Thus, each month may determine the emission source for the month for which the pre-evaluation is made. It should be understood that the time periods may be divided according to seasons, and this embodiment is not limited thereto.
An example of the present invention is described below.
At present, along with the deepening of atmospheric pollution treatment in various cities, the comprehensive treatment strength of dust source treatment, movable source control, non-point source treatment and the like is increased, the pollutant emission reduction space is gradually reduced, and the traditional rough treatment mode cannot be continued. Therefore, scientific and effective support is urgently needed in various cities, and more accurate and refined control measures are implemented. In particular, as a life line of economic development in each city, industrial enterprises have a great influence on local economic development due to conventional 'one-crop-type' production stoppage and production limitation. The example takes a city as a forecast area.
In this example, the discharge data (latitude and longitude, discharge base data) of the local heavy point pollution source is collectedDischarge amount), screening out important pollution sources based on historical meteorological data, screening out important pollution sources which have great influence on cities or sites in each month, using global prediction system (GFS) meteorological forecast data for a certain time (for example, 7 days) in the future, and forecasting the important pollution sources of the cities or the sites SO for the 7 days in the future by using a meteorological model and an air quality model 2 、NO 2 、PM 2.5 The concentration of pollutants such as ozone, etc. (contribution amount, contribution proportion, contribution ranking, etc.). The method provides reference for the local environmental protection department to the control of the atmospheric pollution source in a certain time (or a heavy pollution process) in the future, and has great practical significance for improving the local atmospheric quality and coordinating the environment and the sustainable development of social economy.
In this example, by using the reverse inventory correction technology and the emission inventory coupling technology, the contribution of pollutant concentrations such as national control sites, provincial control sites, small micro-stations and the like can be forecasted based on the emission source data of the gas-related gravity source. The method considers sufficient secondary conversion and has high credibility. Meanwhile, the method screens the gravity source based on historical meteorological data and then performs simulation prediction, so that computing resources can be greatly saved.
The example takes a city as a forecast area, and comprises the following processes:
(1) Topographic data of a forecast area and meteorological data in a preset historical period (for example, three years of history) are acquired, and a meteorological background field in the preset historical period is generated by using a meteorological pattern.
(2) And generating a gridded atmospheric pollution source emission list of the simulation area by using a global emission database, and generating a gridded atmospheric emission source list of the Chinese area in the simulation area by using a Chinese multi-scale emission list (MEIC). And performing coupling replacement on the two atmospheric pollution source emission lists, replacing the gridding emission list generated by using the MEIC list in the Chinese area with the gridding emission list generated by using the global emission database, and not replacing the emission list outside the Chinese area.
(3) And (3) inputting the meteorological background field generated in the meteorological model in the step (1) and the grid emission list in the simulation area generated in the step (2) into an air quality model for simulation to obtain the simulation value of each urban pollutant in the simulation area.
(4) Comparing the simulation value and the monitoring value of the pollutants in the simulation area, iteratively adjusting the emission list of each city, if the simulation value is higher than the monitoring value, reducing the emission of the pollutants of the corresponding city (considering the transmission of the surrounding cities to the city), and if the simulation value is lower than the monitoring value, increasing the emission of the corresponding city until the error between the simulation value and the monitoring value of each city in the simulation area is within 5%; the emission list of the atmospheric pollution source in the simulated area can basically represent the emission characteristics of the simulated area, and the emission list is used as a background emission list.
(5) And (3) performing list gridding on the key source emission data of the forecast area one by using a list gridding technology to generate a gridding list of simulation areas only considering a single key point source, wherein the number of the key point sources to be selected in the step generates a plurality of gridding lists of simulation areas only considering a single key point source.
(6) And (4) coupling the background list generated in the step (4) with the gridding list which is generated in the step (5) and only considers a single heavy point source, eliminating the emission amount of the single heavy point source during coupling, and generating the gridding emission list which does not consider the single heavy point source.
In some cases, the background list generated in step (4) does not include a heavy point source, the amount of emission of the heavy point source is added during coupling, and a gridded emission list considering the heavy point source is generated. At this time, the background list is a gridded emission list without considering the key source.
(7) And (3) simulating the gridding emission list generated in the step (6) and the historical meteorological background field input air quality mode generated in the step (1) to obtain a simulation result that a single heavy point source is not considered for years in history.
(8) And (4) comparing the simulation result obtained in the step (7) with the simulation result obtained in the step (4) when the simulation error is small, and obtaining the influence value of the historical perennial key source on the city or the site.
(9) Ranking the influence values of each month according to the contribution values, and screening out the heavy point sources with larger influence of each month, wherein the heavy point sources with larger influence have larger difference under the influence of the prevailing wind direction of each month. Because the heavy point sources need to be simulated and forecasted one by one, the required computing resources are more, the heavy point sources with smaller influence in each month are removed after screening, and the computing resources of the system can be greatly saved.
(10) GFS weather forecast data of 7 days (or other time duration) in the future is collected, and weather patterns are used for predicting weather fields of 7 days in the future.
(11) And (3) respectively inputting a third-generation air quality model for simulation prediction by respectively using the background emission list generated in the step (4), the emission list without considering a single heavy point source generated in the step (6) screened in the step (9) and the meteorological background field generated in the step (10) for 7 days in the future, and predicting the influence of each heavy point source on the air quality of the city or the site through comparison of simulation results.
The example uses the emission source data for the airborne heavy point source through the manifest reverse correction technique and the manifest coupling technique, and can use the third generation air quality type to forecast the influence on the heavy point source without the need of complete pollution source census data. The method and the system screen the hotspot sources based on historical meteorological data, and the sources with small influence on cities or sites can save the computing resources of a forecasting system and improve the timeliness of forecasting. The considered chemical mechanism is mature, the secondary transformation is fully considered, and the PM can be forecasted 2.5 And ozone with better forecasting effect. Does not need complete air pollution source general survey data, can fully consider secondary conversion, and carries out PM (particulate matter) treatment 2.5 And the ozone has better forecasting effect.
The invention also provides a device for pre-evaluating the influence of the emission source on the air quality.
Fig. 3 is a schematic structural diagram illustrating an apparatus for pre-evaluating an influence of an emission source on air quality according to an exemplary embodiment of the present invention, and as shown in fig. 3, the apparatus for pre-evaluating an influence of an emission source on air quality includes: an obtaining module 310, configured to obtain a first gridded emission list based on a known emission list in a simulation area including a forecast area; an adjusting module 320, connected to the obtaining module 310, configured to input the first grid emission list into the first air quality model for air quality simulation, adjust the first grid emission list based on an error between a simulation result and a corresponding monitoring result, and repeat the simulation and the adjustment until the error between the simulation result and the monitoring result is smaller than a preset value, so as to obtain a second grid emission list in the simulation area; the determining module 330 is connected to the adjusting module 320, and configured to remove or add a preset emission source in the forecast area in the second gridding emission list to obtain a third gridding emission list; and the pre-evaluation module 340 is connected with the determination module 330, and is configured to input the second and third gridded emission lists into the second air quality model respectively for air quality prediction, and determine, based on a prediction result, an influence of a preset emission source on the air quality.
As an embodiment, the adjusting module 320 adjusts the first gridded emissions list based on an error between the simulation result and the corresponding monitoring result, specifically to: for each region within the simulation region: in the case that the simulation result of the area is higher than the corresponding monitoring result, reducing the discharge amount of each grid in the area in the first gridded discharge list based on the error between the simulation result and the corresponding monitoring result; in the case where the simulation result of the area is lower than the monitoring result, the discharge amount of each grid in the area in the first gridded discharge list is increased based on an error between the simulation result and the corresponding monitoring result.
As an embodiment, the adjusting module 320 is configured to reduce or increase the emission amount of each grid in the area in the first grid emission list, specifically: and reducing or increasing the discharge amount of each grid in the area in the first gridding discharge list in an equal proportion, wherein the reduced or increased proportion is positively correlated with the error.
As an embodiment, the simulation result of each area is a mean value of a grid corresponding to each monitored station in the area, and the corresponding monitoring result is a mean value of each monitored station in the area.
As an implementation manner, the determining module 330 is configured to remove or add a preset emission source in the forecast area in the second grid emission list to obtain a third grid emission list, and is specifically configured to: generating a fourth gridding emission list of the preset emission source according to the emission data of the preset emission source in the forecast area; and coupling the second gridding emission list and the fourth gridding emission list to obtain a third gridding emission list which is removed or added with the preset emission source.
As an embodiment, the pre-evaluation module 340 inputs the second grid emission list and the third grid emission list into the second air quality model respectively for air quality prediction, specifically to: and respectively inputting the parts corresponding to the forecasting areas in the second gridding discharge list and the third gridding discharge list into a second air quality model to forecast the air quality.
In some embodiments, further comprising: a selection module to, for each candidate emission source of a plurality of candidate emission sources within a forecast area: removing or adding the candidate emission source in the second gridding emission list to obtain a third gridding emission list corresponding to the candidate emission source; inputting a third gridding emission list corresponding to the candidate emission source into a first air quality model to simulate air quality, and obtaining a simulation result corresponding to the candidate emission source; determining the influence value of the candidate emission source on the air quality according to the simulation result of the candidate emission source and the simulation result when the second gridding emission list is obtained; one or more candidate emission sources are selected from the plurality of candidate emission sources for pre-evaluation based on the impact value corresponding to each candidate emission source in the plurality of candidate emission sources.
An exemplary embodiment of the present invention also provides an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor. The memory stores a computer program executable by the at least one processor, the computer program, when executed by the at least one processor, is for causing the electronic device to perform a method according to an embodiment of the invention.
Exemplary embodiments of the present invention also provide a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is operable to cause the computer to perform a method according to an embodiment of the present invention.
Exemplary embodiments of the present invention also provide a computer program product comprising a computer program, wherein the computer program is operative, when executed by a processor of a computer, to cause the computer to perform a method according to an embodiment of the present invention.
Referring to fig. 4, a block diagram of a structure of an electronic device 400, which may be a server or a client of the present invention, which is an example of a hardware device that may be applied to aspects of the present invention, will now be described. Electronic device is intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 400 includes a computing unit 401 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 402 or a computer program loaded from a storage unit 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for the operation of the device 400 can also be stored. The computing unit 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
A number of components in the electronic device 400 are connected to the I/O interface 405, including: an input unit 406, an output unit 407, a storage unit 408, and a communication unit 409. The input unit 406 may be any type of device capable of inputting information to the electronic device 400, and the input unit 406 may receive input numeric or character information and generate key signal inputs related to user settings and/or function controls of the electronic device. Output unit 407 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, a video/audio output terminal, a vibrator, and/or a printer. Storage unit 408 may include, but is not limited to, magnetic or optical disks. The communication unit 409 allows the electronic device 400 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and/or chipsets, such as bluetooth devices, wiFi devices, wiMax devices, cellular communication devices, and/or the like.
Program code for implementing the methods of the present invention may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
Claims (9)
1. A method of pre-evaluating the impact of an emission source on air quality, comprising:
acquiring a first gridding emission list based on a known emission list in a simulation area containing a forecast area;
inputting the first gridding discharge list into a first air quality model for simulating air quality, adjusting the first gridding discharge list based on an error between a simulation result and a corresponding monitoring result, and repeating the simulation and adjustment until the error between the simulation result and the monitoring result is smaller than a preset value to obtain a second gridding discharge list in the simulation area;
for each candidate emission source of a plurality of candidate emission sources within the forecast area: removing or adding the candidate emission source in the second gridding emission list to obtain a third gridding emission list corresponding to the candidate emission source; inputting a third gridding emission list corresponding to the candidate emission source into the first air quality model to simulate the air quality, so as to obtain a simulation result corresponding to the candidate emission source; determining the influence value of the candidate emission source on the air quality according to the simulation result corresponding to the candidate emission source and the simulation result when the second gridding emission list is obtained, wherein a plurality of influence values of each candidate emission source in a plurality of time periods and a plurality of years of historical synchronization are determined, and the influence value of each time period and a plurality of years of historical synchronization is obtained by simulation based on the emission amount corresponding to the time periods and meteorological data; for each of the plurality of time periods: if a plurality of influence values of candidate emission sources in the years of historical synchronization of the time period meet preset conditions, selecting the candidate emission sources for pre-evaluation in the time period;
removing or adding preset emission sources in the forecast area in the second gridding emission list to obtain a third gridding emission list for pre-evaluation, wherein the preset emission sources comprise selected candidate emission sources;
and respectively inputting the second gridding emission list and the third gridding emission list for pre-evaluation into a second air quality model for air quality prediction, and determining the influence of the preset emission source on the air quality based on the prediction result.
2. The method of claim 1, wherein adjusting the first gridded emissions list based on an error between a simulation result and a corresponding monitoring result comprises:
for each region within the simulation region:
in the case that the simulation result of the area is higher than the corresponding monitoring result, reducing the discharge amount of each grid in the area in the first gridded discharge list based on the error between the simulation result and the corresponding monitoring result;
and in the case that the simulation result of the area is lower than the monitoring result, increasing the discharge amount of each grid in the area in the first gridded discharge list based on the error between the simulation result and the corresponding monitoring result.
3. The method of claim 2, wherein reducing or increasing the emissions of each grid within the area in the first gridded emissions list comprises:
and reducing or increasing the emission amount of each grid in the area in the first gridded emission list in an equal proportion, wherein the reduced or increased proportion is positively correlated with the error.
4. The method according to claim 2 or 3, wherein the simulation result of each area is an average value of grids corresponding to each monitored site in the area, and the corresponding monitoring result is an average value of each monitored site in the area.
5. The method according to any one of claims 1 to 3, wherein eliminating or adding preset emission sources in the forecast area in the second gridded emission list to obtain a third gridded emission list comprises:
generating a fourth gridding discharge list of a preset discharge source according to discharge data of the preset discharge source in the forecast area;
and coupling the second gridding discharge list and the fourth gridding discharge list to obtain a third gridding discharge list which is removed or added with the preset discharge source.
6. The method of any one of claims 1 to 3, wherein inputting the second and third latticed emissions manifests into a second air quality model for air quality forecasting, respectively, comprises:
and respectively inputting the parts corresponding to the forecasting regions in the second gridding discharge list and the third gridding discharge list into a second air quality model to forecast the air quality.
7. An apparatus for pre-evaluating the impact of an emission source on air quality, comprising:
the system comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for acquiring a first gridding emission list based on a known emission list in a simulation area containing a forecast area;
the adjusting module is used for inputting the first gridding discharge list into a first air quality model for simulating air quality, adjusting the first gridding discharge list based on an error between a simulation result and a corresponding monitoring result, and repeating the simulation and adjustment until the error between the simulation result and the monitoring result is smaller than a preset value, so as to obtain a second gridding discharge list in the simulation area;
a selection module to, for each of a plurality of candidate emission sources within the forecast area: removing or adding the candidate emission source in the second gridding emission list to obtain a third gridding emission list corresponding to the candidate emission source; inputting a third gridding emission list corresponding to the candidate emission source into the first air quality model to simulate the air quality, so as to obtain a simulation result corresponding to the candidate emission source; determining an influence value of the candidate emission source on the air quality according to a simulation result corresponding to the candidate emission source and a simulation result obtained when the second gridding emission list is obtained, wherein a plurality of influence values of each candidate emission source in a plurality of time periods and a plurality of years of history are determined, and the influence value in the years of history is obtained by simulation based on the emission amount corresponding to the time periods and meteorological data; for each of the plurality of time periods: if a plurality of influence values of candidate emission sources in the years of historical synchronization of the time period meet preset conditions, selecting the candidate emission sources for pre-evaluation in the time period;
the determining module is used for eliminating or adding a preset emission source in the forecast area in the second gridding emission list to obtain a third gridding emission list for pre-evaluation, wherein the preset emission source comprises a selected candidate emission source;
and the pre-evaluation module is used for inputting the second gridding emission list and the third gridding emission list for pre-evaluation into a second air quality model respectively to forecast the air quality, and determining the influence of the preset emission source on the air quality based on a forecast result.
8. An electronic device, comprising:
a processor; and
a memory for storing the program, wherein the program is stored in the memory,
wherein the program comprises instructions which, when executed by the processor, cause the processor to carry out the method according to any one of claims 1-6.
9. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-6.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003094036A (en) * | 2001-09-26 | 2003-04-02 | Mitsubishi Heavy Ind Ltd | Soil contamination evaluation method and soil purifying treatment method |
| JP2011065257A (en) * | 2009-09-15 | 2011-03-31 | Takenaka Komuten Co Ltd | Soil contamination assessment support device and program |
| KR101668077B1 (en) * | 2015-04-24 | 2016-10-28 | 국방과학연구소 | Disaster response simulation system and method thereof |
| CN110348746A (en) * | 2019-07-15 | 2019-10-18 | 北京三易思创科技有限公司 | Air quality impact evaluation method and apparatus based on single pollution sources |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102628852B (en) * | 2012-03-13 | 2014-12-31 | 北京工业大学 | Atmospheric pollution source grading method based on pollutant source identification technology |
| CN105403664B (en) * | 2015-10-19 | 2017-12-15 | 电力规划设计总院 | A kind of large-scale point pollution source Atmospheric Environmental Impact Assessment method based on WRF CHEM |
| CN106407714A (en) * | 2016-10-14 | 2017-02-15 | 珠海富鸿科技有限公司 | Air pollution assessment method and device based on CALPUFF system |
| CN108537251A (en) * | 2018-03-16 | 2018-09-14 | 广东奥博信息产业股份有限公司 | The determination method and system of pollution sources |
| CN110909483B (en) * | 2019-12-03 | 2023-09-08 | 河北先河环保科技股份有限公司 | Point source atmospheric pollutant emission list verification method based on gridding data |
-
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- 2022-05-07 CN CN202210493678.2A patent/CN114841438B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003094036A (en) * | 2001-09-26 | 2003-04-02 | Mitsubishi Heavy Ind Ltd | Soil contamination evaluation method and soil purifying treatment method |
| JP2011065257A (en) * | 2009-09-15 | 2011-03-31 | Takenaka Komuten Co Ltd | Soil contamination assessment support device and program |
| KR101668077B1 (en) * | 2015-04-24 | 2016-10-28 | 국방과학연구소 | Disaster response simulation system and method thereof |
| CN110348746A (en) * | 2019-07-15 | 2019-10-18 | 北京三易思创科技有限公司 | Air quality impact evaluation method and apparatus based on single pollution sources |
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