A PM based on the CMB model 2.5 Online source resolution methods and equipment

Abstract

This invention discloses a PM based on the CMB model. 2.5 An online source apportionment method and device can automatically identify pollution sources without requiring a large amount of receptor data. The method includes the following steps: acquiring historical observation data of particulate matter and its chemical components to be monitored at the monitoring site; selecting historical component concentration data for constructing a PMF model based on the historical observation data, using mass spectrometry information m / z 44 as the SOA source identifier component, and calculating the uncertainty corresponding to each concentration data based on a predetermined uncertainty calculation method; inputting the data into the PMF model and performing calculations to obtain the component spectrum data of each pollution source at the monitoring site and its corresponding uncertainty; acquiring real-time observation data of the chemical components of particulate matter at the monitoring site; filtering the real-time observation data and performing calculations based on a predetermined uncertainty calculation method to obtain real-time component concentration data and its corresponding uncertainty; inputting the data into the CMB model and performing calculations to obtain the impact of each pollution source on PM2.5. 2.5 Contribution results.

Description

Technical Field

[0001] This invention belongs to the field of air pollution control, and particularly relates to a PM2.5 model based on the CMB model. 2.5 Online source resolution methods and equipment. Background Technology

[0002] PM 2.5 PM2.5 has a significant impact on climate change, human health, and visibility, and is the primary pollutant affecting air quality in my country. 2.5 Pollution is a rapidly changing dynamic process, requiring rapid identification of PM2.5. 2.5 The main source could be PM 2.5 The formulation of pollution prevention and control plans provides a scientific basis for the refined management of PM2.5. 2.5 A necessary condition. Real-time monitoring of PM2.5 using online monitoring methods. 2.5 Conduct comprehensive chemical component monitoring and establish PM2.5 receptor model. 2.5 Online source apportionment technology can quickly identify and quantify the contribution of various pollution sources to particulate matter, providing a basis for PM2.5 analysis. 2.5 It provides technical support for precise pollution control.

[0003] Chemical mass balance (CMB) models and positive definite matrix factorization (PMF) models are two mainstream receptor models. The CMB model, based on the component spectrum information of each pollution source and receptor data, quantitatively assesses the contribution of each pollution source to pollutants, offering advantages such as requiring less receptor data and clearly defined pollution sources. The PMF model, however, does not require input pollution source component spectrum data. It uses weighted least squares to decompose the observed receptor data matrix into a factor spectrum matrix and a factor contribution matrix, identifying pollution sources based on the factor spectrum matrix and quantifying the contribution of each pollution source to pollutants based on the factor contributions. Due to the numerous and complex sources of particulate matter pollution, obtaining representative pollution source component spectrum information is difficult. Currently, PMF models based on receptor models... 2.5 Online source apportionment techniques primarily utilize the Particulate Matrix (PMF) model for analysis. However, the PMF model also has its limitations. First, it requires research experience to identify the sources of each factor and cannot automatically determine the pollution source of particulate matter. Second, due to the collinearity problem among pollution sources—that is, the compositional profiles of pollution sources are quite similar—the PMF model cannot distinguish between collinear source classes, resulting in confounding factors. In particular, secondary organic aerosols (SOAs) are easily mixed with primary and secondary sources and cannot be independently and accurately assessed by the PMF model. Third, the PMF model requires a large amount of receptor data to identify and quantify pollution sources. To obtain the source apportionment results for particulate matter at a specific moment, a large amount of historical observation data is needed, and the source apportionment results at that moment are affected by the input receptor data, leading to significant uncertainty in the apportionment results. Summary of the Invention

[0004] To address the aforementioned issues, a PM based on the CMB model is proposed. 2.5 The online source analysis method and equipment can automatically identify the source of contamination without requiring a large amount of receptor data. The present invention adopts the following technical solution:

[0005] This invention provides a PM based on the CMB model. 2.5 The online source apportionment method includes the following steps: Step 1, acquiring historical observation data of particulate matter and its chemical components to be monitored at the monitoring site; Step 2, selecting historical component concentration data for constructing the PMF model based on the historical observation data, and calculating the corresponding uncertainty for each concentration data based on a predetermined uncertainty calculation method; Step 3, inputting the PMF input data into the PMF model and performing calculations to obtain the component spectrum data and corresponding uncertainties of each pollution source at the monitoring site. The PMF input data includes historical component concentration data and uncertainties; Step 4, acquiring real-time observation data of the chemical components of particulate matter at the monitoring site; Step 5, filtering the real-time observation data and performing calculations based on a predetermined uncertainty calculation method to obtain real-time component concentration data and corresponding uncertainties; Step 6, inputting the component spectrum data and its corresponding uncertainties, and the real-time component concentration data and its corresponding uncertainties into the CMB model, and performing calculations to obtain the impact of each pollution source on PM2.5. 2.5 The results of the contribution.

[0006] PM based on CMB model provided by the present invention 2.5 Online source apportionment methods can also have the following technical features: after selecting historical component concentration data for constructing the PMF model based on historical observation data, organic fragments with a mass-to-charge ratio of 44 are also selected as SOA source identifier components. The PMF input data also includes the identifier components of each pollution source. When the PMF model is calculated, it first obtains the factor component spectrum matrix, and then identifies the pollution source type represented by each factor based on the identifier components of each factor to obtain the component spectrum data of each pollution source. Furthermore, it evaluates the uncertainty of the component spectrum data based on the bootstrap function of the PMF model to obtain the corresponding uncertainty.

[0007] PM based on CMB model provided by the present invention 2.5 Online source resolution methods can also have the following technical characteristics, where the number of factors is 8-12.

[0008] PM based on CMB model provided by the present invention 2.5 Online source analysis methods can also have the following technical characteristics, in which the number of factors is 9, including biomass combustion, secondary sulfate, coal combustion, secondary nitrate, industrial emissions, SOA, ship emissions, motor vehicle emissions, and dust.

[0009] PM based on CMB model provided by the present invention 2.5 Online source resolution methods can also have the following technical features: the predetermined uncertainty calculation method is based on formula U. ij =k j ×C ij Calculate the uncertainty, where C ij For concentration data, k j For the relative uncertainty of the j-th chemical component, U ij C ij The uncertainty.

[0010] PM based on CMB model provided by the present invention 2.5 Online source analysis methods can also have the following technical characteristics: the number of chemical components is 13, including: OM, BC, Cl. - NO3 - SO4 2- NH4 + , K, Si, Ca, Fe, Zn, V, m / z 44.

[0011] PM based on CMB model provided by the present invention 2.5 Online source analysis methods may also have the following technical features, in which the number of chemical components is 16, including: OM, NO3-, SO42-, NH4+, BC, Si, K, Ca, V, Cr, Mn, Fe, Ni, Zn, As, and m / z 44.

[0012] PM based on CMB model provided by the present invention 2.5 Online source analysis methods can also have the following technical characteristics: real-time observation data for PM 2.5 Data on water-soluble ions, organic matter, black carbon, and atmospheric metallic elements.

[0013] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores executable code, and the processor executes the executable code to implement the above-described method.

[0014] Invention Function and Effect

[0015] A PM based on the CMB model provided by the present invention 2.5 The online source apportionment method and equipment, by combining the PMF model to construct localized source composition spectra and using organic fragments with a mass-to-charge ratio of 44 (m / z 44) as SOA source identifier components to obtain SOA composition spectra, uses the localized source composition spectra obtained from the PMF model and real-time online observation data as input conditions for the CMB model, and establishes a PMF model-based system.2.5 Online source resolution technology. Therefore, the online source resolution technology established in this invention has the following characteristics:

[0016] 1. A representative localized source composition spectrum was constructed, which can automatically identify pollution sources and the physical meaning of pollution sources is clear;

[0017] 2. Fully utilize mass spectrometry information to independently identify and quantify SOA source contributions;

[0018] 3. No need to input a large amount of receptor data, the source apportionment result at a certain moment does not depend on previous historical observation data, reducing the uncertainty of the source apportionment result.

[0019] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0021] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below.

[0022] Figure 1 PM in Embodiment 1 of the present invention 2.5 A flowchart illustrating the online source resolution method;

[0023] Figure 2 Hardware structure diagram of a computer device provided in an embodiment of the present invention; Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Furthermore, the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, such that a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or apparatus.

[0026] <Example 1>

[0027] Figure 1 PM in Embodiment 1 of the present invention 2.5 A flowchart illustrating the online source resolution method.

[0028] like Figure 1 As shown, PM based on the CMB model 2.5 The online source resolution method specifically includes steps one through six:

[0029] Step 1: Obtain historical observation data of the particulate matter and its chemical components to be monitored at the monitoring points.

[0030] In this first embodiment, four online monitoring instruments—a real-time particulate matter mass monitor, a particulate matter chemical composition online monitor, a black carbon analyzer, and an atmospheric multi-metal element online monitor—were used to conduct observations at a location in Shenzhen, obtaining PM2.5 concentration data from September 27, 2019 to October 31, 2019. 2.5 Historical hourly observation data of its chemical components, including PM2.5, are used as historical observation data. 2.5 OM, SO4 2- NO3 - NH4 + Cl - , BC, Si, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Mo, Cd, Sn, Ba, Pb, mass spectrometry information m / z 44.

[0031] Step 2: Constructing PMF Input Data: Based on historical observation data, historical component concentration data with accurate measurements, low data missing rates, and pollution source identification functions are selected to construct the PMF model. Uncertainties corresponding to each concentration data are calculated using a predetermined uncertainty calculation method. Additionally, after selecting historical component concentration data for PMF model construction, organic debris with a mass-to-charge ratio of 44 (m / z 44) is selected as the labeling component for secondary organic aerosols (SOA) (i.e., SOA source labeling component). This results in PMF input data including historical component concentration data, corresponding uncertainties, and labeling components. The historical component concentration data includes labeling components for each pollution source. The SOA source labeling component is the one selected above; the labeling components for other pollution sources are existing labeling components and will not be elaborated upon.

[0032] Specifically, based on the historical observation data collected in the first step, components whose concentration data were below the detection limit accounted for more than 40% of all observation data were removed, as were components with low correlation to all other components (r). 2 Components with concentrations ≤0.4% were excluded, as were those with no tracer effect and low concentrations. Ultimately, 13 components were selected to construct the PMF model input component concentration data, including OM, BC, and Cl. - NO3 - SO4 2- NH4 + , K, Si, Ca, Fe, Zn, V, m / z 44.

[0033] Next, according to formula U ij =k j ×C ij Uncertainty data required to construct the PMF model, where U ij C ij The uncertainty, C ij The concentration data input to the PMF model, k j The relative uncertainty of the j-th component is set based on sampling and chemical composition analysis, with the relative uncertainty of each component ranging from 8% to 30%.

[0034] Step 3: Obtain the composition profile of local pollution sources.

[0035] Specifically, the PMF input data constructed in step two is input into the PMF model for calculation and the factor component spectrum matrix is ​​obtained. Based on the characteristics of the identifier components of each factor, the pollution source type represented by each factor is identified.

[0036] In this embodiment, 8-12 factors were selected to determine the optimal analysis results. Possible pollution sources included secondary sulfates, secondary nitrates, SOA, vehicle emissions, dust, biomass combustion, coal combustion, industrial emissions, ship emissions, construction dust, sea salt, and brake wear. The analysis focused on the physical meaning of the source component spectra, the correlation between the time series of source contributions and their source identifiers, and the model's impact on PM2.5. 2.5 Based on the goodness of fit with chemical substance concentrations, nine factors were identified as the optimal source apportionment results. The pollution source types represented by each factor were identified based on the characteristics of its identifying components, including biomass combustion, secondary sulfate, coal combustion, secondary nitrate, industrial emissions, SOA, ship emissions, motor vehicle emissions, and dust.

[0037] Next, the bootstrap function of the PMF model is used to assess the uncertainty of the source composition spectrum, and finally the composition spectrum data of each pollution source and its corresponding uncertainty are obtained.

[0038] Finally, the compositional spectral data of each pollution source and its corresponding uncertainty were obtained. The compositional spectral data of each pollution source constructed in this embodiment are shown in Table 1 below.

[0039] Table 1. Pollution source composition profile (abundance ± uncertainty, g / g)

[0040]

[0041] Step 4: Obtain real-time observation data of the chemical composition of particulate matter at the monitoring points.

[0042] In this first embodiment, four online monitoring instruments—a real-time particulate matter mass monitor, a particulate matter chemical composition online monitor, a black carbon analyzer, and an atmospheric multi-metal element online monitor—were used to conduct observations at the same location in Shenzhen, obtaining PM2.5 concentration data from 12:00 to 21:00 on January 27, 2021. 2.5 Its historical hourly observation data on chemical components, including PM 2.5 OM, SO4 2- NO3 - NH4 + Cl - , BC, Si, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Mo, Cd, Sn, Ba, Pb, mass spectrometry information m / z 44.

[0043] Step 5: Filter the real-time observation data and calculate the real-time component concentration data and its corresponding uncertainty based on the predetermined uncertainty calculation method.

[0044] Referring to the component screening and uncertainty calculation process in step two, the real-time observed component concentrations and their uncertainty datasets required to construct the CMB model are shown in Table 2:

[0045] Table 2 Input data for the CMB model constructed based on online observation data (concentration ± uncertainty, μg / m³) 3 )

[0046]

[0047] The real-time component concentration data and their corresponding uncertainties, as well as the pollution source component spectrum data and their uncertainties obtained in step three, are used as input data for the CMB model.

[0048] Step 6: Input the component spectrum data and its corresponding uncertainty, and the real-time component concentration data and its corresponding uncertainty into the CMB model, and perform calculations to obtain the PM2.5 concentrations for each pollution source, including SOA sources. 2.5 Source analysis results, i.e., PM 2.5 Real-time monitoring of PM2.5 from various pollution sources 2.5 Contributions. January 27, 2021, 12:00 PM - 9:00 PM 2.5 The source analysis results are shown in Table 3:

[0049] Table 3 PM based on CMB model 2.5 PM obtained by online source resolution method 2.5 Source analysis results (μg / m 3 )

[0050]

[0051]

[0052] Therefore, the PM based on the CMB model provided by this invention can be seen. 2.5 Online source apportionment methods can identify particulate matter sources in real time without relying on previous historical observation data, accurately quantify SOA contributions, and reduce uncertainty in source apportionment results.

[0053] This invention also provides a computer device, including a memory and a processor. The memory stores executable code, and when the processor executes the executable code, it implements the method described in any of the above embodiments. Specifically, refer to... Figure 2 .

[0054] The computer device of this invention may include: a processor, a memory, an input / output interface, a communication interface, and a bus. The processor, memory, input / output interface, and communication interface are interconnected internally via the bus. The processor executes executable modules stored in the memory, such as... Figure 1 The computer program corresponding to the method embodiment shown.

[0055] The processor can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0056] The memory can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage devices, dynamic storage devices, etc. The memory can store the operating system and other applications. When the technical solutions provided in the embodiments of this application are implemented through software or firmware, the relevant program code is stored in the memory and called and executed by the processor.

[0057] Input / output interfaces are used to connect input / output modules to enable information input and output. Input / output modules can be configured as components within the device (not shown in the diagram) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0058] The communication interface is used to connect the communication module (not shown in the figure) to enable communication and interaction between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0059] A bus is a pathway that transmits information between various components of a device, such as processors, memory, input / output interfaces, and communication interfaces.

[0060] It should be noted that although the above-described device only shows the processor, memory, input / output interface, communication interface, and bus, in actual implementation, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this application, and not necessarily all the components shown in the figures.

[0061] The above describes the method of this embodiment of the invention by combining historical observation data from September 27, 2019 to October 31, 2019, and real-time observation data from 12:00 to 21:00 on January 27, 2021, for a certain urban location in Shenzhen. Below, in Embodiment Two, the method of this embodiment of the invention will be described again by combining historical observation data from December 27, 2020 to January 31, 2021, and real-time observation data from 8:00 on February 6, 2021 to 20:00 on February 8, 2021, for a certain urban location in Shenzhen that is close to a road.

[0062] <Example 2>

[0063] This second embodiment of PM is based on the CMB model. 2.5 The online source resolution method is similar to that in Example 1, please refer to... Figure 1 Specifically, it includes steps one through six:

[0064] Step 1: Obtain historical observation data of the particulate matter and its chemical components to be monitored at the monitoring points.

[0065] In this second embodiment, four online monitoring instruments—a real-time particulate matter mass monitor, a particulate matter chemical composition monitor, a black carbon analyzer, and an atmospheric multi-metal element monitor—were used to conduct observations at a location in Shenzhen that is close to a road. PM2.5 levels from December 27, 2020 to January 31, 2021 were obtained. 2.5 Its historical hourly observation data on chemical components, including PM 2.5 OM, SO4 2- NO3 - NH4 + Cl - , BC, Si, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Mo, Cd, Sn, Ba, Pb, mass spectrometry information m / z 44.

[0066] Step 2: Constructing PMF input data: Based on historical observation data, select historical component concentration data that are accurately measured, have low data missing rates, and serve as pollution source identifiers to construct the PMF model. Calculate the corresponding uncertainties for each concentration data point using a predetermined uncertainty calculation method. Additionally, after selecting historical component concentration data for PMF model construction, organic fragments with a mass-to-charge ratio of 44 (m / z 44) are selected as the SOA identifier component. This ultimately forms the PMF input data, which includes historical component concentration data and corresponding uncertainties.

[0067] Specifically, based on the historical observation data collected in the first step, components with concentrations below the detection limit exceeding 40% of all observation data were removed; components with low correlation to all other components (r² ≤ 0.4) were also removed; and components with no tracer effect and low concentration levels were removed. Finally, 16 components were selected as the input concentration data for constructing the PMF model, including OM and NO3. - SO4 2- NH4 + , BC, Si, K, Ca, V, Cr, Mn, Fe, Ni, Zn, As, m / z44.

[0068] Next, according to formula U ij =k j ×C ij Uncertainty data required to construct the PMF model, where U ij C ij The uncertainty, C ij The concentration data input to the PMF model, k j The relative uncertainty of the j-th component is set based on sampling and chemical composition analysis, with the relative uncertainty of each component ranging from 10% to 30%.

[0069] Step 3: Obtain the composition profile of local pollution sources.

[0070] Specifically, the PMF input data constructed in step two is input into the PMF model for calculation and the factor component spectrum matrix is ​​obtained. Based on the characteristics of the identifier components of each factor, the pollution source type represented by each factor is identified.

[0071] In this embodiment, 8-12 factors were selected to determine the optimal analysis results. Possible pollution sources included secondary sulfates, secondary nitrates, SOA, vehicle emissions, dust, biomass combustion, coal combustion, industrial emissions, ship emissions, construction dust, sea salt, and brake wear. The analysis focused on the physical meaning of the source component spectra, the correlation between the time series of source contributions and their source identifiers, and the model's impact on PM2.5. 2.5Based on the goodness of fit with chemical substance concentrations, nine factors were identified as the optimal source apportionment results. The pollution source types represented by each factor were identified based on the characteristics of its identifying components, including ship emissions, dust, vehicle emissions, secondary sulfates, biomass combustion, coal combustion, industrial emissions, SOA, and secondary nitrates.

[0072] Next, the bootstrap function of the PMF model is used to assess the uncertainty of the source composition spectrum, and finally the composition spectrum data of each pollution source and its corresponding uncertainty are obtained.

[0073] Finally, the compositional spectral data of each pollution source and its corresponding uncertainty were obtained. The compositional spectral data of each pollution source constructed in this embodiment are shown in Table 4 below.

[0074] Table 4. Pollution source composition profile (abundance ± uncertainty, g / g)

[0075]

[0076] Step 4: Obtain real-time observation data of the chemical composition of particulate matter at the monitoring points.

[0077] In this second embodiment, four online monitoring instruments—a real-time particulate matter mass monitor, a particulate matter chemical composition monitor, a black carbon analyzer, and an atmospheric multi-metal element monitor—were used to conduct observations at the same location in Shenzhen, obtaining PM2.5 concentration data from 8:00 AM to 8:00 PM on February 6, 2021. 2.5 Its historical hourly observation data on chemical components, including PM 2.5 OM, SO4 2- NO3 - NH4 + Cl - Si, Si, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Mo, Cd, Sn, Ba, Pb, mass spectrometry information m / z 44.

[0078] Step 5: Filter the real-time observation data and calculate the real-time component concentration data and its corresponding uncertainty based on the predetermined uncertainty calculation method.

[0079] Referring to the component screening and uncertainty calculation process in step two, the real-time observed component concentrations and their uncertainty datasets required to construct the CMB model are shown in Table 5:

[0080] Table 5 Input data for the CMB model constructed based on online observation data (concentration ± uncertainty, μg / m³) 3 )

[0081]

[0082] The real-time component concentration data and their corresponding uncertainties, as well as the pollution source component spectrum data and their uncertainties obtained in step three, are used as input data for the CMB model.

[0083] Step 6: Input the component spectrum data and its corresponding uncertainty, and the real-time component concentration data and its corresponding uncertainty into the CMB model, and perform calculations to obtain the PM2.5 concentrations for each pollution source, including SOA sources. 2.5 Source analysis results, i.e., PM 2.5 Real-time monitoring of PM2.5 from various pollution sources 2.5 Contributions. February 6, 2021, 8:00 AM - 8:00 PM 2.5 The source analysis results are shown in Table 6:

[0084] Table 6 PM based on CMB model 2.5 PM obtained by online source resolution method 2.5 Source analysis results (μg / m 3 )

[0085]

[0086] Functions and effects of the embodiments

[0087] This invention provides a PM based on the CMB model. 2.5 The online source apportionment method and equipment, by combining the PMF model to construct a localized source composition spectrum and using organic fragments with a mass-to-charge ratio of 44 (m / z 44) as the identifier component of the SOA to obtain the SOA composition spectrum, uses the localized source composition spectrum obtained by the PMF model and real-time online observation data as input conditions for the CMB model, and establishes a PMF model-based system. 2.5 Online source resolution technology. Therefore, the online source resolution technology established in this invention has the following characteristics:

[0088] 1. A representative localized source composition spectrum was constructed, which can automatically identify pollution sources and the physical meaning of pollution sources is clear;

[0089] 2. Fully utilize mass spectrometry information to independently identify and quantify SOA source contributions;

[0090] 3. No need to input a large amount of receptor data, the source apportionment result at a certain moment does not depend on previous historical observation data, reducing the uncertainty of the source apportionment result.

[0091] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention 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 within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these 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 the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A PM based on the CMB model 2.5 The online source resolution method is characterized by, Includes the following steps: Step 1: Obtain historical observation data of the particulate matter and its chemical components to be monitored at the monitoring sites; Step 2: Based on the historical observation data, select historical component concentration data for constructing PMF input data, and calculate the uncertainty corresponding to each concentration data based on a predetermined uncertainty calculation method; Step 3: Input the PMF input data into the PMF model and perform calculations to obtain the component spectrum data of each pollution source at the monitoring site and its corresponding uncertainty. The PMF input data includes the historical component concentration data and the uncertainty. Step 4: Obtain real-time observation data on the chemical composition of the particulate matter at the monitoring points; Step 5: Filter the real-time observation data and calculate the real-time component concentration data and its corresponding uncertainty based on the predetermined uncertainty calculation method; Step six: Input the component spectrum data and its corresponding uncertainty, and the real-time component concentration data and its corresponding uncertainty into the CMB model, and perform calculations to obtain the impact of each pollution source on PM2.

5. 2.5 The contribution results, The historical component concentrations also include the identified components of each of the pollution sources. When selecting historical component concentration data for constructing the PMF model based on the historical observation data, organic fragments with a mass-to-charge ratio of 44 are also selected as SOA source identifier components. The identification component includes the SOA source identification component. When the PMF model is performed, it first obtains the factor component spectrum matrix, then identifies the pollution source type represented by each factor based on the identifier component of each factor to obtain the component spectrum data, and further evaluates the uncertainty of the component spectrum data based on the bootstrap function of the PMF model to obtain the corresponding uncertainty. The method for calculating the predetermined uncertainty is based on the formula. Calculate the uncertainty. In the formula, For concentration data, k j For the relative uncertainty of the j-th chemical component, for The uncertainty.

2. PM based on CMB model as described in claim 1 2.5 The online source resolution method is characterized by: in, The number of factors is set based on the actual situation of the pollution source, ranging from 8 to 12.

3. PM based on the CMB model as described in claim 2 2.5 The online source resolution method is characterized by: in, The number of factors is nine, including biomass combustion, secondary sulfate, coal combustion, secondary nitrate, industrial emissions, SOA, ship emissions, motor vehicle emissions, and dust.

4. PM based on the CMB model according to claim 1 2.5 Online source resolution methods Its features are: The chemical components consist of 13 elements, including: OM, BC, Cl. - NO3 - SO4 2- NH4 + , K, Si, Ca, Fe, Zn, V, m / z 44.

5. PM based on the CMB model according to claim 1 2.5 Online source resolution methods Its features are: The chemical components consist of 16 types, including: OM, NO3. - SO4 2- NH4 + , BC, Si, K, Ca, V, Cr, Mn, Fe, Ni, Zn, As, m / z 44.

6. PM based on the CMB model according to claim 1 2.5 The online source resolution method is characterized by: The real-time observation data is PM 2.5 Data on water-soluble ions, organic matter, black carbon, and atmospheric metallic elements.

7. A computer device comprising a memory and a processor, wherein the memory stores executable code, and the processor, when executing the executable code, implements the method of any one of claims 1-5.

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