Intelligent emission source correction methods, devices and systems

Abstract

This application provides an intelligent emission source correction method, apparatus, and system. The method includes: acquiring the most recent year's basic emission source data from MEIC, the total emission data for each province, industry, and species over the years, and the total energy production and electricity consumption for each year from statistical yearbooks; preprocessing the most recent year's basic emission source data to obtain gridded emission source data; determining the first emission source correction coefficient for each province, industry, and species for each year based on the total emission data over the years and the total energy production and electricity consumption for each year, and performing gridded correction on the gridded emission source data to obtain the first corrected data; and performing adaptive correction processing on the first corrected data based on simulation forecasting test results to obtain the second corrected data, thereby determining the final emission source correction data for the current year. This application can improve the quantitative forecasting performance of key indicators and enhance the overall forecasting capability of the RuiTu-Chemical numerical forecasting system.

Description

Technical Field

[0001] This application relates to the field of meteorological-chemical numerical simulation, and in particular to an intelligent emission source correction method, apparatus and system. Background Technology

[0002] In the real atmosphere, air pollutants and their various components undergo highly complex physical and chemical processes in relation to meteorological conditions. The total amount and real-time intensity of emissions significantly impact PM2.5 levels. 2.5 The formation and forecasting of emission concentrations and visibility have a crucial impact. Due to the influence of industrial and agricultural production activities and people's daily lives, actual emission sources in various regions exhibit significant multi-scale variations, including interannual, monthly, seasonal, and daily cycles. Furthermore, in recent years, environmental protection and related management sectors in various regions have intensified their environmental control efforts, leading to highly flexible adjustments in the total amount and operating hours of many industrial production processes. This has resulted in greater uncertainty between the total emissions from pollution sources in various regions and previously known data. However, the collection, processing, and creation of usable emission source datasets (such as MEIC) at the national and even city levels require substantial human, material, and research team involvement and take a considerable amount of time. Therefore, there is currently no real-time updated, accurate emission source dataset, which significantly limits the rapid improvement of the forecasting performance of emission source-based atmospheric chemical numerical models. Summary of the Invention

[0003] The purpose of this application is to provide an intelligent emission source correction method, device and system. Through data gridding process, provincial, industry and species-specific gridded iterative correction process and adaptive iterative correction process based on real-time forecast performance, the time lag problem of MEIC basic emission sources is solved, the quantitative forecast performance of key indicators is significantly improved, and the overall forecast capability of the RuiTu-Chemical Numerical Prediction System is improved.

[0004] Firstly, this application provides an intelligent emission source correction method, which includes: acquiring basic emission source data for the most recent year from the Multi-Scale Emission Inventory Model (MEIC), total emission data for each province, industry, and species over the years, and total energy production and electricity consumption for each year from statistical yearbooks; each year includes all previous years and the years up to the current year; preprocessing the basic emission source data for the most recent year to obtain gridded emission source data; the gridded emission source data includes emission source data corresponding to each grid; determining the first emission source correction coefficient for each province, industry, and species over the years based on the total emission data for each province, industry, and species over the years and the total energy production and electricity consumption for each year; applying the first emission source correction coefficient for each province, industry, and species over the years to perform gridded correction on the gridded emission source data by province, industry, and species to obtain the first emission source correction data; performing adaptive correction processing on the first emission source correction data based on the simulation and forecasting test results of the target computer to obtain the second emission source correction data; and determining the final emission source correction data for the current year based on the second emission source correction data.

[0005] Furthermore, the above steps for preprocessing the basic emission source data of the most recent year to obtain gridded emission source data include: converting the format of the basic emission source data of the most recent year to obtain preliminary processed data at equal latitude and longitude intervals; performing model grid interpolation on the preliminary processed data to obtain model gridded emission source data at equal spatial distances; and performing emission source species matching and unit conversion processing under the RuiTu-Chemical model based on the model gridded emission source data to obtain gridded emission source data.

[0006] Furthermore, the steps described above for determining the correction coefficients for the first emission source under each province, industry, and species based on historical emission data for each province, industry, and species, and annual energy production and electricity consumption, include: for each current province, industry, and species, using historical emission data for the current province, industry, and species as the current historical emission data, performing the following regression calculation and coefficient calculation steps: determining the coefficients of the current regression equation based on the current historical emission data and the current historical energy production and electricity consumption; obtaining the emission data for the most recent year for the current province, industry, and species, and obtaining the energy production and electricity consumption for the next year from the statistical yearbook; based on the most recent year... Using the total emissions data for the current province, industry, and species in the current year, the total energy production and electricity consumption for the next year from the most recent year, and the current regression equation coefficients, calculate the total emissions data for the current province, industry, and species in the next year. Based on the total emissions data for the current province, industry, and species in the most recent year, and the total emissions data for the next year from the current province, industry, and species, calculate the first emission source correction coefficients for the current province, industry, and species in the next year. Based on the total emissions data for the current province, industry, and species in the next year, update the total emissions data for the current years, and continue the regression calculation steps until the next year is the current year, to obtain the first emission source correction coefficients for each year after the most recent year.

[0007] Furthermore, the steps described above for calculating the total emissions data for the current province, industry, and species in the next year based on the total emissions data for the current year, the total energy production and electricity consumption for the next year, and the coefficients of the current regression equation include: calculating the emission source data for the current province, industry, and species in the next year according to the following multiple regression equation:

[0008] ;

[0009] in, Indicates the current province's number t Year, No. s Industry, No. v Total emissions data for various pollutants; Then it represents the ( )th of the current province. t -1) Year, Number s Industry, No. v Total emissions data for various pollutants; and These represent the current province's number t Total annual energy production and total electricity consumption; For the constants in the regression equation, , , These are the coefficients of the regression equation for the corresponding variables.

[0010] Furthermore, the above-mentioned step of calculating the first emission source correction factor for the current province, current industry, and current species in the next year based on the total emission data for the current year, the current industry, and the current species, and the total emission data for the current year, the current province, the current industry, and the current species, includes: calculating the first emission source correction factor for the next year according to the following formula:

[0011] ;

[0012] in, This represents the percentage change in the total emissions of industry s and pollutant v in the province in year t compared to the previous year.

[0013] Furthermore, the above-mentioned steps of applying the first emission source correction coefficient for each province, industry, and species annually to perform gridded correction on the gridded emission source data by province, industry, and species to obtain the first emission source correction data include: dividing the gridded emission source data into emission source data by province, industry, and species; and multiplying the divided emission source data by the first emission source correction coefficient for the corresponding province, industry, and species annually to obtain the first emission source correction data.

[0014] Furthermore, the step of adaptively correcting the first emission source correction data to obtain the second emission source correction data based on the simulation and forecasting test results of the target computer includes: taking the designated emission source data in the first emission source correction data as the current data to be corrected, and performing the following adaptive correction steps: sending the current data to be corrected to the target computer so that the target computer can perform a simulation and forecasting test based on the current data to be corrected and return the forecasting test results; obtaining multiple station observation values ​​corresponding to the designated emission source; dividing the multiple station observation values ​​corresponding to the designated emission source by the forecasting test results to obtain multiple intermediate correction coefficients; performing grid interpolation based on the multiple intermediate correction coefficients to obtain the current correction coefficients for grid point matching of the Ritu-Chemistry model; multiplying the current correction coefficients by the current data to be corrected to obtain the target correction data; taking the target correction data as the current data to be corrected again, and continuing to perform the adaptive correction steps until the number of corrections reaches a preset value; and replacing the designated emission source data in the first emission source correction data with the finally obtained target correction data to obtain the second emission source correction data.

[0015] Furthermore, the steps described above for determining the final emission source correction data for the current year based on the second emission source correction data include: determining whether the correction conditions of a specific correction module are met; if not, using the finally obtained second emission source correction data as the final emission source correction data for the current year; if yes, determining the specified correction coefficient output by the specific correction module; and applying the second emission source correction data multiplied by the specified correction coefficient to obtain the final emission source correction data for the current year.

[0016] Secondly, this application also provides an intelligent emission source correction device, comprising: a data acquisition module for acquiring basic emission source data for the most recent year, total emission data for each province, industry, and species over the years from the Multi-Scale Emission Inventory Model (MEIC), and total energy production and electricity consumption for each year from statistical yearbooks; the yearly data includes all previous years and the years up to the current year; a data preprocessing module for preprocessing the basic emission source data for the most recent year to obtain gridded emission source data; the gridded emission source data includes emission source data corresponding to each grid; and a correction coefficient determination module for determining the correction coefficient based on the data for each province, industry, and species over the years. Based on the total emissions data and the annual total energy production and electricity consumption, the system determines the correction coefficients for the first emission source under each province, industry, and species. A gridded correction module applies these correction coefficients to perform province-specific, industry-specific, and species-specific gridded corrections on the gridded emission source data, yielding corrected first emission source data. An adaptive correction module performs adaptive correction processing on the first emission source correction data based on the simulation and forecasting test results from the target computer, yielding corrected second emission source data. Finally, a correction data determination module determines the final emission source correction data for the current year based on the corrected second emission source data.

[0017] Thirdly, this application also provides an intelligent emission source correction system, including a correction server and a target server connected by communication; the target server is used to conduct a simulation forecast experiment based on the emission source data sent by the correction server, obtain the forecast experiment results, and send them to the correction server; the correction server is used to execute the method as described in the first aspect.

[0018] The intelligent emission source correction method, device, and system provided in this application acquire the basic emission source data for the most recent year from the Multi-Scale Emission Inventory Model (MEIC), the total emission data for each province, industry, and species over the years, and the total energy production and electricity consumption for each year from the statistical yearbook; each year includes all previous years and the years up to the current year; the basic emission source data for the most recent year is preprocessed to obtain gridded emission source data; the gridded emission source data includes emission source data corresponding to each grid; based on the total emission data for each province, industry, and species over the years and the total energy production and electricity consumption for each year, the first emission source correction coefficient for each province, industry, and species for each year is determined; the first emission source correction coefficient for each province, industry, and species for each year is applied to perform gridded correction on the gridded emission source data by province, industry, and species to obtain the first emission source correction data; based on the simulation and forecasting test results of the target computer, the first emission source correction data is adaptively corrected to obtain the second emission source correction data; based on the second emission source correction data, the final emission source correction data for the current year is determined. This application addresses the time lag problem of basic emission sources in MEIC through data gridding, province-by-province-by-industry-by-species gridding iterative correction, and adaptive iterative correction based on real-time forecast performance. This significantly improves the quantitative forecast performance of key indicators and, consequently, enhances the overall forecast capability of the RuiTu-Chemical Numerical Prediction System. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 A flowchart illustrating an intelligent emission source correction method provided in this application embodiment;

[0021] Figure 2 A schematic diagram of the technical logic structure of an intelligent emission source correction system provided in this application embodiment;

[0022] Figure 3 A cross-platform operation flowchart of an intelligent emission source correction system provided in this application embodiment;

[0023] Figure 4 A structural block diagram of an intelligent emission source correction device provided in an embodiment of this application;

[0024] Figure 5This is a schematic diagram of the structure of an intelligent emission source correction system provided in an embodiment of this application. Detailed Implementation

[0025] The technical solutions of this application will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] To address the current lack of real-time updated datasets of emission sources in existing technologies, which significantly limits the rapid improvement of the forecasting performance of emission source-based atmospheric chemical numerical models, this application provides an intelligent emission source correction method, device, and system. Through data gridding, iterative correction processes by province, industry, and species, and adaptive iterative correction processes based on real-time forecasting performance, the method solves the time lag problem of basic emission sources in MEIC, significantly improves the quantitative forecasting performance of key indicators, and thus enhances the overall forecasting capability of the RETOUR-Chemical Numerical Prediction System.

[0027] To facilitate understanding of this embodiment, a detailed description of an intelligent emission source correction method disclosed in this application embodiment will be provided first.

[0028] Figure 1 A flowchart of an intelligent emission source correction method provided in this application embodiment is included, the method comprising the following steps:

[0029] Step S102: Obtain the basic emission source data for the most recent year in the multi-scale emission inventory model MEIC, the total emission data for each province, industry and species over the years, and the total energy production and electricity consumption for each year in the statistical yearbook; each year includes the previous year and the year after that up to the current year.

[0030] Multiscale emission inventory models (MEICs) typically include baseline emission source data from 1990 to 2020; however, baseline emission source data for the most recent year, 2020, is required. Furthermore, based on this emission source data, total emissions data for each province, industry, and species can be calculated annually; for example, total emissions data for each province, industry (such as power, industry, transportation, residential, and agriculture), and species (such as SO2, NOx, CO, VOC, NH3, PM2.5, BC, and OC) in 1990.

[0031] The term "year by year" includes all previous years and the years that follow up to the current year, which is the period from 1990 to 2024.

[0032] Step S104: Preprocess the basic emission source data for the most recent year to obtain gridded emission source data; the gridded emission source data includes emission source data corresponding to each grid.

[0033] This includes processing methods such as format conversion, unit conversion, and data interpolation, which will be explained in detail later.

[0034] Step S106: Based on the total emission data of each province, industry and species over the years and the total energy production and electricity consumption over the years, determine the correction coefficient of the first emission source for each province, industry and species over the years.

[0035] By using the total emissions data of each province, industry, and species over the years, along with the total energy production and electricity consumption each year, a regression equation can be constructed. Through annual iterations, the correction coefficients for the primary emission sources of each province, industry, and species can be determined for each year from 2021 to 2024.

[0036] Step S108: Apply the first emission source correction coefficients for each province, industry, and species annually to perform gridded correction on the gridded emission source data by province, industry, and species to obtain the first emission source correction data.

[0037] Step S110: Based on the simulation and forecast test results of the target computer, adaptive correction processing is performed on the correction data of the first emission source to obtain the correction data of the second emission source.

[0038] Here, an adaptive correction process with different iteration counts can be set according to the actual situation. The specific implementation method will be detailed later.

[0039] Step S112: Based on the second emission source correction data, determine the final emission source correction data for the current year.

[0040] The intelligent emission source correction method provided in this application solves the time lag problem of MEIC basic emission sources through the intelligent gridded iterative correction function of real-time forecast performance, and significantly improves the monitoring of PM2.5. 2.5 The quantitative forecasting performance of key indicators such as concentration and visibility has improved the overall forecasting capability of the RuiTu-Chemical numerical weather prediction system.

[0041] This application also provides another intelligent emission source correction method, which is implemented based on the above embodiments. This embodiment focuses on describing the data preprocessing process, the correction coefficient determination process, the province-by-province-by-industry-by-species gridded correction process, the adaptive correction process, and the special scenario correction process. The entire process can be referred to [reference needed]. Figure 2 The diagram shows the technical logic structure of the intelligent emission source correction system.

[0042] 1. The data preprocessing process is as follows:

[0043] (1) Convert the format of the basic emission source data of the most recent year to obtain preliminary processed data under the same latitude and longitude interval grid;

[0044] The initial data for the most recent year downloaded from MEIC is in nc format, with each feature as a vector data point (1 column, 64,000 rows), ranging from 10ºN to 60ºN in latitude and from 70ºE to 150ºE in longitude, with a spatial resolution of 0.25º × 0.25º. First, the single-column vector data for each feature is read. Second, the vector data is converted into a two-dimensional matrix data point (200 × 320 grid points). The east-west direction has 320 grid points (320 columns), and the north-south direction has 200 grid points (200 rows). During the conversion from one-dimensional vector data to two-dimensional matrix data, it is crucial to ensure correspondence with the actual latitude and longitude information.

[0045] The initial dataset processed is RADM2_MEIC, a four-dimensional matrix (5, 31, 200, 320). The first dimension, 5, indicates that MEIC emissions data mainly come from five sectors: agriculture, industry, power, residential, and transportation. The second dimension represents 31 species, with nine being direct emitters: SO2, NOx, CO, NMVOC, NH3, and PM2.5. 10 PM 2.5 BC and OC; the chemical mechanism of RADM2 outputs 23 VOC species, including ALD, CH4, CSL, ETH, GLY, HC3, HC5, HC8, HCHO, ISO, KET, MACR, MGLY, MVK, NR, NVOL, OL2, OLI, OLT, ORA1, ORA2, TOL, and XYL.

[0046] (2) Perform pattern grid interpolation on the preliminary processed data to obtain pattern grid emission source data with equal spatial distance; that is, the interpolation process from RADM2_MEIC data to pattern grid CBMZ_RCHEM.

[0047] The initially gridded emission source data RADM2_MEIC consists of spatial grids with equal latitude and longitude intervals, while the CBMZ_RCHEM dataset required for the Reitu-Chemistry model consists of spatial grids with equal spacing. Therefore, further spatial interpolation is required. CBMZ_RCHEM is a four-dimensional matrix of size (5,31,505,618). Therefore, the third and fourth dimensions (200,320) of RADM2_MEIC (5,31,200,320) need to be spatially interpolated to a matrix of (505,618). By performing cyclic interpolation using the grid-based interpolation method, the gridded emission source data CBMZ_RCHEM (5,31,505,618) suitable for the Reitu-Chemistry model can be obtained.

[0048] (3) Based on the gridded emission source data, perform emission source species matching and unit conversion processing under the RuiTu-Chemical model to obtain gridded emission source data.

[0049] The Ritu-Chemistry model requires 32 emission source species to participate in the chemical integral calculation. First, the 31 emission species in MEIC need to be arranged sequentially according to the order required by the Ritu-Chemistry model. Specifically, in the four-dimensional matrix (sector, species, east-west grid, north-south grid), the second dimension 1, 2, ..., 31 correspond to SO2, NO, ALD, HCHO, ORA2, NH3, HC3, HC5, HC8, ETH, CO, OL2, OLT, OLI, TOL, XYL, KET, CSL, ISO, E_PM25i, E_PM25j, E_SO4i, E_SO4j, E_NO3i, E_NO3j, ORGi, ORGj, ECi, ECj, PM10, and NO2. Some of the emissions required by the model do not have direct corresponding species in the original MIEC emission sources and require further calculations. For example:

[0050] CBMZ_RCHEM(:,2,:,:) = RADM2_MEIC(:,29,:,:) ×0.9, indicating that the second required NO is NOx × 0.9;

[0051] CBMZ_RCHEM(:,20,:,:) =(RADM2_MEIC(:,29,:,:)- RADM2_MEIC (:,28,:,:)-RADM2_MEIC (:,27,:,:)) ×0.13, indicating that the required E_PM25i for the 20th element is (PM2.5-OC-BC) ×0.13;

[0052] This is just one example; there are actually many more data points that need to be processed.

[0053] The 32nd species in CBMZ_RCHEM is HONO. There is no direct corresponding species in RADM2_MEIC; it is estimated based on NOx emissions. The coefficient is 0.008 in the four sectors of agriculture, industry, electricity, and residential, while the coefficient is 0.023 in the transportation sector. The specific calculation formulas are: CBMZ_RCHEM(1:4,32,:,:)=RADM2_MEIC(1:4,26,:,:) ×0.08; CBMZ_RCHEM(5,32,:,:)=RADM2_MEIC(5,26,:,:) ×0.08.

[0054] Furthermore, there are two units for emission sources from MEIC: ton / (grid month) and mmol / (grid month); however, for the RITROIT-Chemical Model integration calculation, the unit for all emission sources must be mol / (km²). 2 (day), that is, each unit area (km²) 2 The emissions per unit time (day) are used to calculate the total emissions, so unit conversion is necessary. It's important to note that while each grid point in RADM2_MEIC is 0.25º × 0.25º, the corresponding actual ground area varies significantly. The higher the latitude, the smaller the ground area corresponding to each grid point. Therefore, area conversion is crucial during unit conversion. In practice, this is achieved using the area-weighted operator S.

[0055] 2. Process for determining correction coefficients:

[0056] In practice, for each current province, industry, and species, regression analysis is performed using historical data on total emissions for that province, industry, and species against historical data on total energy production and electricity consumption for that province. The following regression calculation and coefficient calculation steps are executed:

[0057] (1) Determine the coefficients of the current regression equation based on the current total emissions data and the current total energy production and electricity consumption data.

[0058] Specifically, a multiple regression equation is established based on historical emission data and corresponding total energy production and electricity consumption. The current annual total provincial energy production and electricity consumption are used as explanatory factors (e.g., annual total energy production and electricity consumption of Hebei Province from 1990 to 2020 from statistical yearbooks). The current annual emissions from the current province, industry, and species are used as the explained factors (e.g., annual industrial SO2 emissions of Hebei Province from 1990 to 2020 from MEIC emission sources). The regression coefficients of the regression equation are estimated and calculated using the least squares method. That is, the coefficients of the current regression equation include... , , , .

[0059] (2) Obtain the total emissions data for the current province, industry and species in the most recent year, and obtain the total energy production and total electricity consumption for the following year from the statistical yearbook;

[0060] For example, obtain data on the total SO2 emissions from industry in Hebei Province in 2020, as well as the total energy production and electricity consumption in 2021.

[0061] (3) Based on the total emissions data for the current province, industry and species in the most recent year, the total energy production and electricity consumption for the next year in the most recent year, and the current regression equation coefficients, calculate the total emissions data for the current province, industry and species in the next year;

[0062] The following multiple regression equation is used to calculate the emission source data for the current province, industry, and species for the next year:

[0063] ;

[0064] in, Indicates the current province's number t Year, No. s Industry, No. v Total emissions data for various pollutants; Then it represents the ( )th of the current province. t -1) Year, Number s Industry, No. v Total emissions data for various pollutants; and These represent the current province's number t Total annual energy production and total electricity consumption; For the constants in the regression equation, , , These are the coefficients of the regression equation for the corresponding variables.

[0065] By substituting the total industrial SO2 emissions data of Hebei Province in 2020, as well as the total energy production and electricity consumption data of 2021, into the formula above, the total industrial SO2 emissions data of Hebei Province in 2021 can be calculated.

[0066] (4) Based on the total emissions data for the current province, industry, and species in the most recent year, and the total emissions data for the current province, industry, and species in the next year, calculate the correction factor for the first emission source corresponding to the current province, industry, and species in the next year;

[0067] Calculate the correction factor for the first emission source in the following year using the following formula:

[0068] ;

[0069] in, This represents the percentage change in the total emissions of industry s and pollutant v in the province in year t compared to the previous year.

[0070] By substituting the total industrial SO2 emissions data of Hebei Province in 2020 and 2021 into the above formula, the first emission source correction coefficient of industrial SO2 in Hebei Province in 2021 can be calculated.

[0071] The calculation process is the same for other provincial industry categories, so it will not be repeated here.

[0072] (5) Based on the total emissions data of the current province, current industry and current species in the next year, update the total emissions data of the current years, continue to perform the regression calculation steps until the next year is the current year, and obtain the first emission source correction coefficient for each year after the most recent year.

[0073] For example, after calculating the total industrial SO2 emissions data for Hebei Province in 2021, the total industrial SO2 emissions data for Hebei Province from 1990 to 2021 are used as the current annual total emissions data to continue determining the regression coefficients and subsequent calculation processes until the first emission source correction coefficient for the current year 2024 is obtained.

[0074] By performing the above process for each province, each industry, and each species, the correction coefficients for the first emission source under each province, industry, and species can be obtained year by year.

[0075] 3. Grid-based correction process by province, industry, and species:

[0076] The gridded emission source data is divided into emission source data by province, industry, and species. Based on the divided emission source data, the data is multiplied by the first emission source correction coefficient for the corresponding province, industry, and species year by year to obtain the first emission source correction data. .

[0077] The correction coefficients for the first emission source under each province, industry, and species obtained by the above formula are the correction coefficients for each province / city, industry, and species from 2021 to 2024. In gridded data, the spatial boundaries of each province / city are irregular, so it is necessary to correct the emission sources for each province / city separately. This requires spatial masking for each province / city within the numerical model space. For example, to correct the variation coefficient of SO2 emission sources from industry in Anhui Province, all areas outside Anhui Province must first be masked, and only the grid points within the Anhui boundary line should be multiplied by the corresponding proportional coefficient for that province.

[0078] In practice, n iterations are required, such as n=34. The final result is a preliminarily corrected gridded emission source.

[0079] Due to significant differences in economic activities and industrial structures, the coefficients of change for the same species, industry / sector, and year t vary considerably among provinces compared to the previous year or any other year. For example, the proportionality coefficients of industrial NOx emissions in 2021 differ significantly among provinces compared to 2016.

[0080] 4. Adaptive correction process:

[0081] In practice, the designated emission source data in the first emission source correction data is used as the current data to be corrected, and the following adaptive correction steps are performed:

[0082] (1) Send the current data to be corrected to the target computer so that the target computer can perform a simulation forecast experiment based on the current data to be corrected and return the forecast experiment results;

[0083] (2) Obtain observations from multiple stations corresponding to the specified emission source; divide the observations from multiple stations corresponding to the specified emission source by the forecast test results to obtain multiple intermediate correction coefficients; perform grid interpolation based on the multiple intermediate correction coefficients to obtain the current correction coefficients for grid point matching of the Ruito-Chemistry model;

[0084] (3) Multiply the current correction factor by the current data to be corrected to obtain the target correction data;

[0085] (4) The target correction data is used as the current data to be corrected, and the adaptive correction steps are continued until the number of corrections reaches the preset value. The target correction data obtained at the end is used to replace the specified emission source data in the first emission source correction data to obtain the second emission source correction data.

[0086] The following is a specific example. In this embodiment, the preset value is 2, meaning the above process is repeated twice, completing two correction processes. Therefore, the final result is the third emission source correction data. After completing the above-mentioned province-by-province, industry-by-industry, and species-by-species grid-based emission source correction, the estimated emission source for the corresponding month is used to conduct a real-world prediction experiment. The emission source is then corrected again by comparing the forecast results with the real-world verification results. Specifically, the second emission source correction data... :

[0087] ;

[0088] In the formula The second correction coefficient is obtained by interpolating the ratio of observations from multiple stations to the results of the first forecast experiment.

[0089] ;

[0090] In the formula, This is the intermediate correction factor. This represents the station observation values ​​corresponding to the six elements of a ground observation station, namely:

[0091] ; same Consistency refers to the predicted values ​​of the six elements in the first forecast experiment results. This is a correction for total emissions from different species sources, no longer considering differences in emission sources from industries such as industry, power, and transportation. Therefore, the relevant subscripts "t,v,s" in the calculation formula become "t,v". It is important to note that the emission sources constrained by the ratios of different elements are different when correcting for emission sources, as detailed below: PM 2.5 The concentration ratio is used to correct the following elements in the emission source:

[0092] {'E_PM 25 I','E_PM 25 J','E_ECI','E_ECJ','E_ORGI','E_ORGJ','E_SO4I','E_SO4J','E_NO3I','E_NO3J'} total 10 elements; PM 10 It is used only to constrain 'E_PM_10'; SO2 is used only to constrain 'E_SO2'; CO is used only to constrain 'E_CO'; NO2 is used only to constrain 'E_NO', but needs to be multiplied by 90% because the total NO is approximately 90% of NO2; the ratio of O3 concentration is used to correct the following elements:

[0093] The list contains 15 VOC components: {'E_ISO','E_ETH','E_HC3','E_HC5','E_HC8','E_XYL','E_OL2','E_OLT','E_OLI','E_TOL','E_CSL','E_HCHO','E_ALD','E_KET','E_ORA2'}.

[0094] There are 1700 environmental monitoring stations nationwide. For each station, by comparing the station's observed values ​​with the results of the first forecast experiment using the formula above, 1700 intermediate correction coefficients can be obtained. The intermediate correction coefficients for these 1700 sites are unevenly distributed. Using the 'natural' interpolation method, a second correction coefficient matching the Ritu-Chemistry model grid point (505, 618) can be obtained. .

[0095] When the second emission source correction data based on the results of the first forecast experiment and the observation station values ​​were obtained A second round of iterative numerical simulation prediction experiments was conducted, and the third correction coefficient was obtained using the same method. The revised emission sources are:

[0096] ;

[0097] This refers to the third emission source correction data used in the Ruito-Chem business model.

[0098] 5. Correction process for special scenarios:

[0099] Specifically, it determines whether the correction conditions of a specific correction module are met; if not, the final third emission source correction data is used as the final emission source correction data for the current year; if yes, the specified correction coefficient output by the specific correction module is determined; the third emission source correction data is multiplied by the specified correction coefficient to obtain the final emission source correction data for the current year.

[0100] For example, in special circumstances such as major events or large-scale control of persistent smog pollution, it is necessary to revise emission source reductions based on current environmental control measures. For instance, when one or more cities implement odd-even license plate restrictions, it will inevitably significantly impact the amount of traffic-related emissions. Therefore, it is necessary to make appropriate special emission reduction revisions, especially for NOx species, in these cities or regions' traffic sources. Thus, the emission source correction system also includes a specific correction module, whose function is to perform special corrections of emission sources under special scenarios, with the correction factor denoted as... The final emission source it corrected is denoted as :

[0101]

[0102] This is the emission source correction data ultimately used by the Ritu-Chem numerical model forecasting system. If there are no specific triggering conditions or events for the correction module in actual operation, then by default:

[0103] .

[0104] Due to the large computational load and numerous steps involved in chemical numerical simulation, each emission source correction operation takes 1-2 days, requiring multiple cross-platform calculations and the running of dozens of programs, which is extremely cumbersome. To improve the operational efficiency of emission source correction and reduce manual monitoring, this embodiment employs a complete intelligent scheduling, operation, and monitoring system for the entire emission source correction process. Throughout the entire operation, the process has been transformed from requiring constant manual monitoring to operating without any manual intervention.

[0105] In addition to the collection and processing of statistical yearbook data in the early stage, the entire emission source determination and correction operation involves multiple server systems and is a complex cross-system platform operation. Therefore, intelligent operation scheduling and monitoring greatly improve the efficiency and stability of the operation. Figure 3 The flowchart of the intelligent emission source correction system's cross-platform operation is shown, involving multiple exchange operations on Server 11 and the high-performance computer (Ruitu). Currently, in addition to the monthly emission source switching scheduled start operation, various operation start mechanisms are added for special scenarios and forecast application feedback scenarios, demonstrating stable performance. Practice shows that the Ruitu-Chemistry intelligent emission source correction system effectively ensures the efficient operation of the Ruitu-Chemistry numerical model forecasting system, thus providing extensive and in-depth services for routine atmospheric environmental governance in the Beijing-Tianjin-Hebei region and surrounding areas, as well as air quality assurance work for major events.

[0106] Through parallel experiments using short-term and medium-term models of the Ruito-Chemical subsystem—specifically, the medium-term model using 2019 emission sources processed based on annual emission variation coefficient estimation and provincial gridded emission source correction methods, while the short-term model directly used 2019 emission sources—significant differences were observed after a one-week comparative experiment. The average deviation in the short-term model was 11.1 ug / m³. 3 The mid-term model is -3.4 ug / m 3 The absolute deviation was reduced by 69.4%; the root mean square error for the short-term model was 30.6 ug / m. 3 The mid-term model is 17 ug / m 3Compared to the short-term model, PM2.5 levels decreased by 44.4%; the spatial correlation coefficient was 0.75 for the short-term model and increased by 0.81 for the medium-term model. Regionally, this resulted in a decrease in PM2.5 levels during the recent heavy pollution episodes in North China. 2.5 Concentration forecasts have also improved by at least 10-20%. Overall, the provincial gridded emission source correction method based on annual emission variation coefficients can effectively reduce the systematic forecast bias caused by the lag of basic emission sources, which is conducive to improving forecast accuracy in the short term.

[0107] The intelligent emission source correction method provided in this application can effectively improve the accuracy of PM2.5 correction even when the basic emission source inventory dataset is outdated or contains errors. 2.5 This method improves the forecasting performance of near-surface atmospheric pollutant concentrations and visibility, including O3. It is based on a multi-scale emission inventory model (MEIC) dataset of basic emission sources, statistical yearbook data, a meteorological-chemical coupled numerical simulation system, and real-time observation data of six surface elements from national environmental monitoring stations. It integrates a series of automated mathematical statistical analyses, numerical simulation experiments, simulation result verification and evaluation, correction coefficient calculation and gridded interpolation, and intelligent iterative techniques. The emission source forecasting system generated based on the technology described in this application exhibits higher accuracy and stability than forecasts based on basic emission sources.

[0108] Based on the above method embodiments, this application also provides an intelligent emission source correction device, see [link to relevant documentation]. Figure 4 As shown, the device includes: a data acquisition module 602, used to acquire the most recent year's basic emission source data, the total emission data of each province, industry, and species over the years from the Multi-Scale Emission Inventory Model (MEIC), and the total energy production and electricity consumption for each year from the statistical yearbook; the yearbook includes the previous year and the year after that up to the current year; a data preprocessing module 604, used to preprocess the most recent year's basic emission source data to obtain gridded emission source data; the gridded emission source data includes the emission source data corresponding to each grid; and a correction coefficient determination module 606, used to determine the correction coefficient based on the total emission data of each province, industry, and species over the years and the total energy production and electricity consumption for each year from the statistical yearbook. The system calculates the total energy production and electricity consumption, and determines the correction coefficients for the first emission source under each province, industry, and species annually. A gridded correction module 608 is used to apply the correction coefficients for the first emission source under each province, industry, and species annually to perform gridded correction on the gridded emission source data by province, industry, and species, obtaining the first emission source correction data. An adaptive correction module 610 is used to perform adaptive correction processing on the first emission source correction data based on the simulation and forecast test results of the target computer, obtaining the second emission source correction data. A correction data determination module 612 is used to determine the final emission source correction data for the current year based on the second emission source correction data.

[0109] Furthermore, the aforementioned data preprocessing module 604 is used to convert the format of the basic emission source data of the most recent year to obtain preliminary processed data under grid points with equal latitude and longitude intervals; to perform pattern grid point interpolation processing on the preliminary processed data to obtain pattern grid point emission source data with equal spatial distances; and to perform emission source species matching processing and unit conversion processing under the RuiTu-Chemical model based on the pattern grid point emission source data to obtain gridded emission source data.

[0110] Furthermore, the aforementioned correction coefficient determination module 606 is used to perform the following regression calculation and coefficient calculation steps for each current province, current industry, and current species, using the total emissions data of the current province, current industry, and current species over the years as the current total emissions data: determining the coefficients of the current regression equation based on the current total emissions data; obtaining the total emissions data of the current province, current industry, and current species for the most recent year, and obtaining the total energy production and total electricity consumption for the following year from the statistical yearbook; and based on the total emissions data of the current province, current industry, and current species for the most recent year, and the total energy production for the following year... Based on the total amount of electricity and energy consumption, and the current regression equation coefficients, calculate the total emissions data for the current province, industry, and species for the next year; based on the total emissions data for the current province, industry, and species for the most recent year, and the total emissions data for the current province, industry, and species for the next year, calculate the first emission source correction coefficients for the current province, industry, and species for the next year; based on the total emissions data for the current province, industry, and species for the next year, update the total emissions data for the current years, and continue to perform the regression calculation steps until the next year is the current year, to obtain the first emission source correction coefficients for each year after the most recent year.

[0111] Furthermore, the aforementioned correction coefficient determination module 606 is used to calculate the emission source data for the current province, current industry, and current species for the next year according to the following multiple regression equation:

[0112] ;

[0113] in, Indicates the current province's number t Year, No. s Industry, No. v Total emissions data for various pollutants; Then it represents the ( )th of the current province. t -1) Year, Number s Industry, No. v Total emissions data for various pollutants; and These represent the current province's number t Total annual energy production and total electricity consumption; For the constants in the regression equation, , , These are the coefficients of the regression equation for the corresponding variables.

[0114] Furthermore, the aforementioned correction factor determination module 606 is used to calculate the correction factor for the first emission source corresponding to the following year according to the following formula:

[0115] ;

[0116] in, This represents the percentage change in the total emissions of industry s and pollutant v in the province in year t compared to the previous year.

[0117] Furthermore, the aforementioned gridded correction module 608 is used to divide the gridded emission source data into emission source data by province, industry, and species; based on the divided emission source data, it is multiplied by the first emission source correction coefficient under the corresponding province, industry, and species year by year to obtain the first emission source correction data.

[0118] Furthermore, the aforementioned adaptive correction module 610 is used to take the specified emission source data in the first emission source correction data as the current data to be corrected, and perform the following adaptive correction steps: send the current data to be corrected to the target computer so that the target computer can perform a simulation forecast experiment based on the current data to be corrected and return the forecast experiment results; obtain multiple station observation values ​​corresponding to the specified emission source; divide the multiple station observation values ​​corresponding to the specified emission source by the forecast experiment results to obtain multiple intermediate correction coefficients; perform grid interpolation processing based on the multiple intermediate correction coefficients to obtain the current correction coefficients for grid point matching of the Ruito-Chemistry model; multiply the current correction coefficients by the current data to be corrected to obtain the target correction data; take the target correction data as the current data to be corrected again, and continue to perform the adaptive correction steps until the number of corrections reaches a preset value; replace the specified emission source data in the first emission source correction data with the finally obtained target correction data to obtain the second emission source correction data.

[0119] Furthermore, the aforementioned correction data determination module 612 is used to determine whether the correction conditions of a specific correction module are met; if not, the final second emission source correction data is used as the final emission source correction data for the current year; if yes, the specified correction coefficient output by the specific correction module is determined; the second emission source correction data is multiplied by the specified correction coefficient to obtain the final emission source correction data for the current year.

[0120] The device provided in this application embodiment has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts of the device embodiment not mentioned can be referred to the corresponding content in the aforementioned method embodiment.

[0121] This application also provides an intelligent emission source correction system, such as... Figure 5 As shown, it includes a correction server 71 and a target server 72 connected by communication; the target server 72 is used to conduct a simulation forecast experiment based on the emission source data sent by the correction server 71, obtain the forecast experiment results, and send them to the correction server 71; the correction server 71 is used to execute the method described in the foregoing method embodiments.

[0122] The system provided in this application embodiment has the same implementation principle and technical effects as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the system embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0123] This application also provides a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions cause the processor to implement the above-described method. For specific implementation details, please refer to the foregoing method embodiments, which will not be repeated here.

[0124] The computer program products of the methods, apparatus and systems provided in the embodiments of this application include a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.

[0125] Unless otherwise specifically stated, the relative steps, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of this application.

[0126] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0127] In the description of this application, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0128] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the technical scope disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

Claims

1. An intelligent emission source correction method, characterized in that, The method includes: Obtain the most recent year's basic emission source data from the Multiscale Emission Inventory Model (MEIC), the total emission data for each province, industry, and species over the years, and the total energy production and electricity consumption for each year from the statistical yearbook; the "each year" includes all previous years and the years up to the current year. The basic emission source data for the most recent year is preprocessed to obtain gridded emission source data; the gridded emission source data includes emission source data corresponding to each grid. Based on the total emission data of each province, industry and species over the years and the total energy production and electricity consumption over the years, the correction coefficient of the first emission source under each province, industry and species is determined for each year. By applying the first emission source correction coefficients for each province, industry, and species year by year, the gridded emission source data is gridded and corrected by province, industry, and species to obtain the first emission source correction data. Based on the simulation and forecast test results of the target computer, adaptive correction processing is performed on the correction data of the first emission source to obtain the correction data of the second emission source. Based on the second emission source correction data, the final emission source correction data for the current year is determined; Based on the historical emission totals data for each province, industry, and species, and the annual total energy production and electricity consumption, the steps for determining the first emission source correction coefficient for each province, industry, and species include: for each current province, industry, and species, using the historical emission totals data for the current province, industry, and species as the current historical emission totals data, performing the following regression calculation and coefficient calculation steps: determining the current regression equation coefficients based on the current historical emission totals data and the current historical total energy production and electricity consumption; obtaining the emission totals data for the current province, industry, and species for the most recent year, and obtaining the energy production and electricity consumption data for the next year from the statistical yearbook; and based on the emission totals data for the current province, industry, and species for the most recent year... Using the total emissions data for the current province, industry, and species, the total energy production and electricity consumption for the next year of the most recent year, and the current regression equation coefficients, calculate the total emissions data for the current province, industry, and species for the next year; based on the total emissions data for the current province, industry, and species for the most recent year, and the total emissions data for the next year of the same category, calculate the first emission source correction coefficients for the current province, industry, and species for the next year; based on the total emissions data for the current province, industry, and species for the next year, update the total emissions data for the current historical years, and continue the regression calculation steps until the next year is the current year, obtaining the first emission source correction coefficients for each year after the most recent year; The steps for calculating the total emissions data for the next year under the current province, industry, and species, based on the total emissions data for the current year, the current industry, and species in the most recent year, the total energy production and electricity consumption for the next year, and the current regression equation coefficients, include: calculating the emission source data for the next year under the current province, industry, and species according to the following multiple regression equation: ； in, Indicates the current province's number t Year, No. s Industry, No. v Total emissions data for various pollutants; Then it represents the ( )th of the current province. t -1) Year, Number s Industry, No. v Total emissions data for various pollutants; and These represent the current province's number t Total annual energy production and total electricity consumption; For the constants in the regression equation, , , These are the coefficients of the regression equation for the corresponding variables.

2. The method according to claim 1, characterized in that, The steps for preprocessing the baseline emission source data of the most recent year to obtain gridded emission source data include: The basic emission source data of the most recent year is converted into a format to obtain preliminary processed data with equal latitude and longitude intervals. The preliminary processed data is subjected to pattern grid interpolation to obtain pattern grid emission source data with equal spatial spacing; Based on the gridded emission source data of the aforementioned model, emission source species matching and unit conversion processing are performed under the RuiTu-Chemical model to obtain gridded emission source data.

3. The method according to claim 1, characterized in that, The step of calculating the first emission source correction coefficient for the current province, industry, and species in the next year, based on the total emission data for the current year, current industry, and current species, and the total emission data for the current year, current industry, and current species, includes: Calculate the correction factor for the first emission source in the following year using the following formula: ； in, This represents the percentage change in the total emissions of industry s and pollutant v in the province in year t compared to the previous year.

4. The method according to claim 1, characterized in that, The steps of applying the first emission source correction coefficients for each province, industry, and species annually to perform gridded correction on the gridded emission source data by province, industry, and species to obtain the first emission source correction data include: The gridded emission source data is then divided into emission source data by province, industry, and species; Based on the segmented emission source data, the first emission source correction data is obtained by multiplying it by the first emission source correction coefficient under the corresponding province, industry and species year by year.

5. The method according to claim 1, characterized in that, The steps for adaptively correcting the first emission source correction data to obtain the second emission source correction data based on the simulation and forecasting test results of the target computer include: Using the specified emission source data in the first emission source correction data as the current data to be corrected, the following adaptive correction steps are performed: The current data to be corrected is sent to the target computer, so that the target computer can perform a simulation forecast experiment based on the current data to be corrected and return the forecast experiment results; Obtain observations from multiple stations corresponding to the specified emission source; divide the observations from multiple stations corresponding to the specified emission source by the forecast experiment results to obtain multiple intermediate correction coefficients; perform grid interpolation based on the multiple intermediate correction coefficients to obtain the current correction coefficients for grid point matching of the Ruito-Chemistry model; The target corrected data is obtained by multiplying the current correction coefficient by the current data to be corrected. The target correction data is used again as the current data to be corrected, and the adaptive correction step is continued until the number of corrections reaches a preset value. The target correction data is then used to replace the specified emission source data in the first emission source correction data to obtain the second emission source correction data.

6. The method according to claim 1, characterized in that, The steps for determining the final emission source correction data for the current year based on the second emission source correction data include: Determine whether the correction conditions of a specific correction module are met; If not, the second emission source correction data obtained last will be used as the final emission source correction data for the current year; If so, determine the specified correction factor output by the specific correction module; apply the second emission source correction data by multiplying the specified correction factor to obtain the final emission source correction data for the current year.

7. An intelligent emission source correction device, characterized in that, The device includes: The data acquisition module is used to acquire the basic emission source data for the most recent year in the Multi-Scale Emission Inventory Model (MEIC), the total emission data for each province, industry, and species over the years, and the total energy production and electricity consumption for each year in the statistical yearbook; the "each year" includes the previous year and the year after that up to the current year. The data preprocessing module is used to preprocess the basic emission source data of the most recent year to obtain gridded emission source data; the gridded emission source data includes emission source data corresponding to each grid. The correction coefficient determination module is used to determine the first emission source correction coefficient for each province, industry, and species based on the total emission data for each province, industry, and species over the years and the total energy production and electricity consumption for each year. The gridded correction module is used to apply the first emission source correction coefficients for each province, industry and species year by year to perform gridded correction on the gridded emission source data by province, industry and species to obtain the first emission source correction data. An adaptive correction module is used to adaptively correct the first emission source correction data based on the simulation and forecast test results of the target computer to obtain the second emission source correction data. The correction data determination module is used to determine the final emission source correction data for the current year based on the second emission source correction data; The correction coefficient determination module is further configured to: for each current province, current industry, and current species, using the total emissions data for the current province, current industry, and current species over the years as the current total emissions data for the current years, and perform the following regression calculation and coefficient calculation steps: determine the coefficients of the current regression equation based on the current total emissions data and the current total energy production and electricity consumption; obtain the total emissions data for the current province, current industry, and current species for the most recent year, and obtain the total energy production and electricity consumption for the next year from the statistical yearbook; and based on the total emissions data for the current province, current industry, and current species for the most recent year, and the total energy production and electricity consumption for the next year from the statistical yearbook... Using total production and total electricity consumption, along with the coefficients of the current regression equation, calculate the total emissions data for the current province, industry, and species for the next year; based on the total emissions data for the current province, industry, and species for the most recent year, and the total emissions data for the current province, industry, and species for the next year, calculate the first emission source correction coefficients for the current province, industry, and species for the next year; based on the total emissions data for the current province, industry, and species for the next year, update the total emissions data for the current historical years, and continue executing the regression calculation steps until the next year is the current year, obtaining the first emission source correction coefficients for each year after the most recent year; The steps for calculating the total emissions data for the next year under the current province, industry, and species, based on the total emissions data for the current year, the current industry, and species in the most recent year, the total energy production and electricity consumption for the next year, and the current regression equation coefficients, include: calculating the emission source data for the next year under the current province, industry, and species according to the following multiple regression equation: ； in, Indicates the current province's number t Year, No. s Industry, No. v Total emissions data for various pollutants; Then it represents the ( )th of the current province. t -1) Year, Number s Industry, No. v Total emissions data for various pollutants; and These represent the current province's number t Total annual energy production and total electricity consumption; For the constants in the regression equation, , , These are the coefficients of the regression equation for the corresponding variables.

8. An intelligent emission source correction system, characterized in that, The system includes a correction server and a target server connected by communication; the target server is used to conduct a simulation forecast experiment based on the emission source data sent by the correction server, obtain the forecast experiment results, and send them to the correction server; the correction server is used to perform the method as described in any one of claims 1 to 6.

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