A method for atmospheric pollution source analysis based on the CALPUFF-CMB coupling model

Abstract

The present invention discloses an atmospheric pollution source parsing method based on a CALPUFF-CMB coupling model, which relates to the technical field of air pollution detection. The method comprises: dividing each source class in an emission inventory of a region into sub-source classes; simulating the emission and diffusion processes of the sub-source classes in each source class in the emission inventory of the region using a CALPUFF model to obtain the pollution contribution ratio of each sub-source class in the region to a receptor point; performing source parsing on the region using a CMB model using a source component spectrum and receptor component data to obtain the pollution contribution ratio of each source class in the region; and coupling the pollution contribution ratio of each sub-source class to the receptor point obtained by simulating the CALPUFF model with the pollution contribution ratio of each source class in the region obtained by the CMB model to establish a refined source parsing result.

Description

[0001] This application is a divisional application of the patent application entitled "Source analysis method, device, electronic device and storage medium for air pollution". The application date of the original application is March 6, 2019, and the application number is 201910168888.2. Technical Field

[0002] The present invention relates to the technical field of air pollution detection, and in particular to an atmospheric pollution source analysis method based on a CALPUFF-CMB coupling model. Background Art

[0003] Currently, the main approaches for attributing the sources of ambient particulate matter are receptor models, source inventory analysis, and air quality models. CALPUFF, a widely used mesoscale air quality model, conducts spatial simulations based on emission source data. CMB, one of the most widely used receptor models, performs source attribution based on PM2.5 component data from receptor point monitoring or sampling analysis.

[0004] International research has long been conducted on the combined use of air quality models and receptor models to apportion sources at specific receptor sites, comparing and validating the two models. For example, Amit M et al. (2005) used the CMAQ and CMB models to analyze PM2.5 source contributions at four sites in the United States. Comparing the results of the CMAQ and CMB models, they found that the simulated concentrations of the former were lower than those of the latter. The conclusions drawn included: the CMB model has disadvantages in spatial representation, while the CMAQ model has shortcomings in reflecting temporal variations. Therefore, the results of the two models should be comprehensively analyzed based on the research objectives.

[0005] Katsushige U et al. (2017) used the CMAQ and PMF models to perform source analysis on PM2.5 in a certain area of ​​Japan. By comparing the CMAQ model results with the PMF, they found which part of the emission data might have deviations, thereby causing errors in the air quality simulation results, paving the way for further optimization of the air quality model.

[0006] Swetha P et al. (2016) used AERMOD and CMB to conduct source analysis of PM10 at a certain point. The results showed that the AERMOD simulation results at most stations underestimated the contribution of pollution source concentrations. They proposed that the inaccuracy of emission inventories would have a significant impact on the simulation results, and suggested optimizing the air quality simulation results by improving the source inventory and refining the emission sources.

[0007] In summary, there are currently few studies on the coupling of multiple model results, and the research methods are not highly refined. Summary of the Invention

[0008] The purpose of the present invention is to provide an atmospheric pollution source analysis method based on the CALPUFF-CMB coupling model to finely study the contribution of each pollution source.

[0009] To achieve the above-mentioned objectives, the present invention provides a method for source apportionment of atmospheric pollution, the method comprising: dividing each source class in the emission inventory of a region into sub-source classes; simulating the emission and diffusion processes of the sub-source classes in each source class in the emission inventory of the region using a CALPUFF model to obtain the pollution contribution ratio of each sub-source class to a receptor point in the region; performing source apportionment of the region using a CMB model using a source component spectrum and receptor component data to obtain the pollution contribution ratio of each source class in the region; and coupling the pollution contribution ratio of each sub-source class to the receptor point simulated by the CALPUFF model with the pollution contribution ratio of each source class in the region obtained by the CMB model to establish a refined source apportionment result.

[0010] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0011] The CALPUFF simulation of major urban source categories in this paper divides each source category in a region's emission inventory into sub-source categories. For example, industrial sources can be subdivided into key enterprises and industries, civil sources can be divided into regions, and transportation sources can be refined to urban trunk roads. Compared with other studies, this is more comprehensive and refined. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0013] Figure 1 Schematic diagram of a method for source apportionment of air pollution according to an embodiment of the present invention;

[0014] Figure 2 Schematic diagram of the principle of the air pollution source apportionment method of the present invention;

[0015] Figure 3 Schematic diagram of the structure of the air pollution source analysis device of the present invention;

[0016] Figure 4 The figure is a schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0018] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] Figure 1 Schematic diagram of a method for source apportionment of air pollution according to an embodiment of the present invention; Figure 2 The schematic diagram of the principle of the atmospheric pollution source apportionment method of the present invention is described below in combination.

[0020] A method for source apportionment of air pollution, comprising:

[0021] Step 11: Divide each source category in the emission inventory of a region into sub-source categories; wherein the source categories include: industrial sources, transportation sources, coal-burning sources, and dust sources.

[0022] The sub-source categories of industrial sources include: key enterprises and key industries; the key industries include: steel, petrochemical, and foundry industries;

[0023] The sub-source categories of the traffic sources include: road mobile sources and non-road mobile sources, among which road mobile sources are divided according to urban trunk roads;

[0024] The coal sources include: civil coal sources and non-civil coal sources, among which civil coal sources are divided according to various geographical regions;

[0025] The sub-source categories of the dust source include: road dust, construction dust, and yard dust.

[0026] Step 12, using the CALPUFF model, simulate the emission and diffusion process of the sub-source categories in each source category in the emission inventory of a region to obtain the pollution contribution ratio of each sub-source category in the region to the receptor point; the CALPUFF model is a three-dimensional non-steady-state Lagrangian diffusion model, including: CALMET module, CALPUFF module, CALPOST module. The CALPUFF model simulation takes into account the comprehensive influence of terrain, meteorological and chemical transformation factors. The CALPUFF model includes 5 chemical reaction mechanisms: ① MESOPUFF II; ② RIVAD; ③ RIVAD+ISORROPIA; ④ SOA; ⑤ RIVAD+ISORROPIA+CalTechSOA;

[0027] Among them, the first three chemical mechanisms are used to calculate the generation of inorganic aerosols; the fourth chemical mechanism is used to calculate the generation of organic aerosols; and the fifth chemical mechanism is used to calculate the chemical transformation of inorganic and organic aerosols.

[0028] Step 13, using the source component spectrum and receptor component data, perform source analysis of the CMB model on the region to obtain the pollution contribution ratio of various sources in the region;

[0029] Step 14: Couple the pollution contribution ratios of each sub-source class to the receptor point obtained by the CALPUFF model simulation with the pollution contribution ratios of each source class in the region obtained by the CMB model to establish a refined source analysis result.

[0030] The following describes the application scenarios of the present invention.

[0031] In order to deeply study the pollution sources of urban-scale receptor points and refine the contributions of various pollution sources in the traditional CMB source parsing results, this paper proposes the CALPUFF-CMB composite model technology. Through the CALPUFF model, the contribution of each pollution source to the receptor point in the urban emission source inventory is simulated (the model takes into account factors such as terrain, meteorology, and transmission conditions), and the results are incorporated into the CMB model source parsing. A refined source parsing method specific to industrial sectors and combustion types is established, providing scientific support for the effective control and precise emission reduction of urban air pollution, and laying the foundation for the further development of source parsing technology.

[0032] The present invention conducts a relatively comprehensive emission source inventory diffusion simulation for a certain city, couples the simulation results with the CMB source apportionment results, and ultimately obtains a refined source apportionment for the city.

[0033] like Figure 2 As shown, the CALPUFF-CMB composite simulation process constructed by the present invention is mainly divided into three steps:

[0034] Step 1: Use the CALPUFF model to simulate the emissions and diffusion processes of multiple sub-source categories within the main source categories in the emission inventory, and determine the concentration and proportion of each sub-source contribution to the receptor site. Industrial sources can be broken down into key enterprises and industries, residential sources can be broken down into regions, and transportation sources can be broken down into urban arterial roads.

[0035] The CALPUFF model is a three-dimensional, unsteady Lagrangian diffusion model that includes modules such as CALMET, CALPUFF, and CALPOST. CALMET is a three-dimensional meteorological module that includes a sea breeze program to simulate the impact of sea and land breezes on coastal cities. CALPUFF is a pollution prediction module that can simulate unsteady conditions (such as calm winds, smoke, circulation, and coastal effects).

[0036] The present invention is based on a city's pollution source inventory, using stationary combustion sources, process sources, mobile sources, agricultural sources, dust sources, biomass combustion sources, and other emission sources as model inputs, covering a comprehensive range of pollution source categories in the city. Based on the inventory, industrial sources can be subdivided into key enterprises and industries, civilian sources can be divided into regions, and transportation sources can be refined to the city's main roads. The CALPUFF model simulates the pollution contribution of each sub-source category in the source inventory to the receiving point, ultimately outputting the contribution concentration and calculating the contribution ratio of each sub-source category.

[0037] The CALPUFF model includes five chemical reaction mechanisms: ① MESO PUFF II; ② RIVAD; ③ RI VAD+ISORROPIA; ④ SOA; ⑤ RIVAD+ISORROPIA+CalTechSOA. The first three chemical mechanisms are used to calculate the generation of inorganic aerosols, and the fourth is used to calculate the generation of organic aerosols. RIVAD+ISORROPIA+CalTechSOA can simultaneously consider the chemical transformation of inorganic and organic aerosols. Since the contribution of secondary particles to PM2.5 in my country's cities occupies an important position, the present invention adopts the RIVAD+ISORROPIA+CalTechSOA mechanism, which includes four VOCs components and their conversion products based on the consideration of the conversion of SO2 to sulfate and NO / NO2 to nitrate. The present invention distributes VOCs emissions to four VOCs components (toluene, xylene, long-chain alkanes and polycyclic aromatic hydrocarbons), while considering the chemical transformation of SNA and SOA. Since the contribution of SOA generated by the conversion of VOCs to PM2.5 cannot be ignored, it is very necessary to use this mechanism.

[0038] Step 2: Use the source component spectrum and receptor component data to conduct CMB source analysis and obtain the pollution contribution concentration and proportion of the main sources.

[0039] The CMB model is the most important model in the technical methods for the source analysis of atmospheric particulate matter. It is recommended for use by the US EPA and is mainly used to study the sources and contributions of pollutants such as TSP, PM10, PM2.5 and VOC.

[0040] The CMB model must meet six conditions for source analysis:

[0041] (1) All sources contributing to the receptor can be identified, and the chemical composition of the particulate matter they emit can be accurately known through analysis;

[0042] (2) The chemical composition of particulate matter emitted from various sources is relatively stable;

[0043] (3) There is no mutual influence between particulate matter emitted from various sources;

[0044] (4) The chemical composition of particulate matter emitted from various sources varies significantly;

[0045] (5) The number of analysis elements must be greater than or equal to the number of sources, which is determined by the equation;

[0046] (6) The error of the sampling method is random and conforms to the normal distribution law.

[0047] The CMB model consists of a set of linear equations. The concentration of each chemical element in the receptor is equal to the linear sum of the product of the element content value of the source component spectrum and the source contribution concentration value. Its mathematical expression is:

[0048]

[0049] Where C ij is the ambient atmospheric concentration of component j in sample i measured at the receptor site; p is the number of sources; g ik is the total contribution of k emission sources to sample i; f kj is the concentration of component j emitted by emission source k, representing the composition of the source.

[0050] The algorithm used by the CMB model is the effective variance least squares method, which minimizes the sum of the squares of the differences between the weighted element measurements and the calculated values.

[0051] The basic formula of the effective variance least squares method is as follows:

[0052]

[0053] Where: C i —The concentration of VOCs chemical component i in the environmental receptor;

[0054] F ij — measured value of the content of chemical component i in source type j;

[0055] S j —Calculated value of the concentration contributed by the jth type of source;

[0056] V eff，i —Effective variance, weight value.

[0057]

[0058] Where: σ—standard deviation of the corresponding value.

[0059] The CMB model inputs include the mass fractions of each chemical component at each emission source (source composition spectrum), the concentrations of each chemical component at the receptor site, and the uncertainty values ​​for each component at the emission source and receptor site. Outputs include the contribution of the emission source to the receptor site, the corresponding source contribution ratio, and diagnostic parameters to validate the model output. These parameters typically include the regression coefficient of the fitting equation, the sum of squared residuals, and the percent mass. Ideal ranges for these diagnostic parameters are 0.8-1, 0-4.0, and 80%-120%, respectively.

[0060] Step 3: Combine the sub-source contributions from the CALPUFF simulation with the CMB results to create a refined source attribution. In other words, combine the CALPUFF and CMB model results to create a refined source attribution pie chart.

[0061] The present invention mainly has the following characteristics:

[0062] 1. The CALPUFF simulation takes into account the combined effects of factors such as topography, meteorology, and chemical transformation, and is more reasonable and scientific than the optimization of CMB results based on source inventories.

[0063] 2. The CALPUFF simulation of the main source categories in the city in this paper is more comprehensive and detailed than other studies, in which industrial sources are subdivided into key enterprises and industries, civilian coal sources can be divided into regions, and transportation sources can be refined to urban main roads.

[0064] 3. The CALPUFF-CMB model nesting method is different from existing research on the comprehensive application of multiple models and the mutual comparison and verification of different model results. Instead, it establishes a method and approach to optimize the source analysis of the receptor model based on the results of the diffusion model, combining the advantages of the two models to improve the regional source analysis research.

[0065] like Figure 3 As shown, the present invention also provides a source analysis device for urban air pollution, comprising:

[0066] A division unit 31 divides each source category in the emission inventory of a region into sub-source categories;

[0067] The CALPUFF model processing unit 32 simulates the emission and diffusion processes of the sub-source classes in each source class in the emission inventory of a region using the CALPUFF model to obtain the pollution contribution ratio of each sub-source class in the region to the receptor point;

[0068] The CMB model processing unit 33 performs source analysis of the CMB model on the region using the source component spectrum and the receptor component data to obtain the pollution contribution ratio of various sources in the region;

[0069] The coupling unit 34 couples the pollution contribution ratio of each sub-source class to the receptor point obtained by the CALPUFF model simulation with the pollution contribution ratio of each source class in the region obtained by the CMB model to establish a refined source analysis result.

[0070] The device of this embodiment can be used to perform Figure 1 or Figure 2 The technical solution of the method embodiment shown has similar implementation principles and technical effects, which will not be repeated here.

[0071] The present invention also provides an electronic device, Figure 4 This is a schematic diagram of the structure of an embodiment of the electronic device of the present invention, which can realize the present invention. Figure 1-2 The process of the embodiment shown is as follows: Figure 4 As shown, the above-mentioned electronic device may include: a shell 41, a processor 42, a memory 43, a circuit board 44 and a power supply circuit 45, wherein the circuit board 44 is placed inside the space enclosed by the shell 41, and the processor 42 and the memory 43 are arranged on the circuit board 44; the power supply circuit 45 is used to supply power to various circuits or devices of the above-mentioned electronic device; the memory 43 is used to store executable program code; the processor 42 runs the program corresponding to the executable program code by reading the executable program code stored in the memory 43, so as to execute any of the above-mentioned embodiments.

[0072] For details on the specific execution process of the above steps by the processor 42 and the steps further executed by the processor 42 by running the executable program code, please refer to the present invention. Figure 1-3 The description of the illustrated embodiment will not be repeated here.

[0073] This electronic device exists in many forms, including but not limited to:

[0074] (1) Mobile communication devices: These devices are characterized by their mobile communication capabilities and are primarily designed to provide voice and data communications. These terminals include smartphones (e.g., iPhones), multimedia phones, feature phones, and low-end phones.

[0075] (2) Ultra-mobile personal computer devices: These devices fall under the category of personal computers, have computing and processing capabilities, and generally also have mobile Internet access. These terminals include PDAs, MIDs, and UMPCs, such as the iPad.

[0076] (3) Portable entertainment devices: These devices can display and play multimedia content. These devices include audio and video players (such as iPods), handheld game consoles, e-books, smart toys, and portable car navigation devices.

[0077] (4) Server: A device that provides computing services. The server consists of a processor, hard disk, memory, system bus, etc. The server is similar to a general computer architecture, but because it needs to provide highly reliable services, it has higher requirements in terms of processing power, stability, reliability, security, scalability, and manageability.

[0078] (5) Other electronic devices with data interaction functions.

[0079] An embodiment of the present invention further provides an application program, which is executed to implement the method provided by any embodiment of the present invention.

[0080] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0081] Each embodiment in this specification is described in a related manner. The same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0082] In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0083] For the convenience of description, the above device is described as being divided into various units / modules based on their functions. Of course, when implementing the present invention, the functions of each unit / module can be implemented in the same or multiple software and / or hardware.

[0084] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0085] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for atmospheric pollution source identification based on the CALPUFF-CMB coupling model, characterized in that: The method comprises: Step 11: Divide each source category in the emission inventory of a region into sub-source categories; The various source categories include: industrial sources, transportation sources, coal burning sources and dust sources; The sub-source categories of industrial sources include: key enterprises and key industries; the key industries include: steel, petrochemical and foundry industries; The sub-source categories of the traffic sources include: road mobile sources and non-road mobile sources, among which road mobile sources are divided according to urban trunk roads; The coal sources include: civil coal sources and non-civil coal sources, among which civil coal sources are divided according to various geographical regions; The sub-source categories of the dust source include: road dust, construction dust and yard dust; Step 12: Using the CALPUFF model, simulate the emission and diffusion processes of the sub-source classes in each source class in the emission inventory of a region to obtain the pollution contribution ratio of each sub-source class in the region to the receptor point; The CALPUFF model is a three-dimensional unsteady Lagrangian diffusion model, comprising the CALMET module, the CALPUFF module, and the CALPOST module. The CALPUFF model simulates the combined effects of topography, meteorology, and chemical transformation factors. CALMET is a three-dimensional meteorological module that includes a sea breeze program to simulate the effects of sea and land breezes on coastal cities. CALPUFF is a pollution prediction module that simulates unsteady conditions, including calm winds, smoke, circulation, and coastal effects. Based on the city's pollution source list, the model inputs include stationary combustion sources, process sources, mobile sources, agricultural sources, dust sources, and biomass combustion sources. Based on the pollution source list, industrial sources are subdivided into key enterprises and key industries, civilian sources are divided into regions, and transportation sources are refined to urban trunk roads. The CALPUFF model simulates the pollution contribution of each sub-source category in the pollution source list to the receiving point, and ultimately outputs the contribution concentration, calculating the contribution ratio of each sub-source category. The CALPUFF model includes five chemical reaction mechanisms: ① MESOPUFF II; ② RIVAD; ③ RIVAD+ISORROPIA; ④ S0A; ⑤ RIVAD+ISORROPIA+CaITechS0A; Among them, the first three chemical reaction mechanisms are used to calculate the generation of inorganic aerosols; the fourth chemical reaction mechanism is used to calculate the generation of organic aerosols; the fifth chemical reaction mechanism is used to calculate the chemical transformation of inorganic and organic aerosols; Because secondary particles contribute significantly to urban PM2.5, the RIVAD+ISORROPIA+CaITechSOA mechanism was adopted. This mechanism includes four VOC components and their conversion products, taking into account the conversion of SO2 to sulfate and NO / NO2 to nitrate. VOC emissions were distributed among the four VOC components: toluene, xylene, long-chain alkanes, and polycyclic aromatic hydrocarbons (PAHs), while also considering the chemical conversion of SNA and SOA. Step 13: Using the source component spectrum and receptor component data, perform source analysis of the CMB model on the region to obtain the pollution contribution ratio of various sources in the region; The CMB model consists of a set of linear equations. The concentration of each chemical element in the receptor is equal to the linear sum of the product of the element content value of the source component spectrum and the source contribution concentration value. The mathematical expression is: Where C ij is the ambient atmospheric concentration of component j in sample i measured at the receptor site; p is the number of sources; g ik is the total contribution of k emission sources to sample i; f kj is the concentration of component j emitted by emission source k, representing the composition of the source; The algorithm used in the CMB model is the effective variance least squares method, which is to minimize the sum of the squares of the differences between the weighted element measurements and the calculated values; The basic formula of the effective variance least squares method is as follows: Where: C i is the concentration detection value of VOCs chemical component i in the environmental receptor; F i is the measured value of the content of chemical component i in the jth source; S j The calculated value of the concentration contributed by the jth source; V eff，i is the effective variance, weight value; Where: σ is the standard deviation of the corresponding value; The CMB model's input data includes the mass fraction of each chemical component at each emission source, i.e., the source composition spectrum; the concentration of each chemical component at the receptor site; and the uncertainty values ​​of each component measured at the emission source and receptor site. The output includes the contribution value of the emission source to the receptor site, the corresponding source contribution rate, and diagnostic parameters to verify the validity of the model output. The diagnostic parameters include the regression coefficient of the fitting equation, the residual sum of squares, and the percent mass. The ranges of the diagnostic parameters are 0.8-1, 0-4.0, and 80%-120%, respectively. Step 14: Couple the pollution contribution ratios of each sub-source class to the receptor point obtained by the CALPUFF model simulation with the pollution contribution ratios of various sources in the region obtained by the CMB model to establish a refined source apportionment result; Through the CALPUFF model, we simulate the contribution of each pollution source in the urban emission source list to the receptor point. The model takes into account factors such as terrain, meteorology, and transmission conditions, and incorporates their results into the source analysis of the CMB model. We establish a refined source analysis method specific to industrial sectors and combustion types, providing scientific support for the effective control of urban air pollution and precise emission reduction.

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