A method for determining dynamic evolution of atmospheric secondary organic aerosols

Abstract

The application discloses a kind of determination methods of atmospheric secondary organic aerosol dynamic evolution, comprising the following steps: S1: according to existing research determines or adopts AMS to determine the POA mass spectrum of typical primary emission source emission of target research area;S2: the concentration and OA mass spectrum of main component of atmospheric aerosol in target area are observed continuously online using AMS, and the mass spectrum concentration matrix ORG of OA is established;S3: using positive matrix factor analysis method PMF, ORG matrix is analyzed, and fixed POA factor mass spectrum is set simultaneously, decomposes and identifies P-1 POA factor and 1 OOA factor;S4: the atmospheric OA observed in S2 is deducted from the POA factor decomposed in S3, and the variable OOA var factor of a kind is calculated, the mass spectrum of OOA var at each observation time is obtained, and the variation law of the concentration, mass spectrum composition and oxidation degree of OOA var Pollution characteristics are analyzed again.Can relatively reduce the uncertainty of traditional PMF method to OA factor identification and the restriction of factor mass spectrum, be favorable to further analyze the dynamic evolution law and generation mechanism of SOA.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of environmental science and protection technology, and particularly relates to a method for determining dynamic evolution of atmospheric secondary organic aerosol. BACKGROUND

[0002] Atmospheric fine particulate matter (aerosol) is mainly composed of inorganic salts, organic matter (referred to as organic aerosol, OA), black carbon and metal elements, etc., in which the proportion of OA is close to 50%. Due to the variety of OA, the complexity of molecular structure and reaction, the current understanding of the formation mechanism of OA pollution is not comprehensive enough. Generally, according to the source, OA can be divided into primary organic aerosol (POA) and secondary organic aerosol (SOA), the former is from direct emission of pollution sources such as coal-fired power plants and motor vehicles, and the latter is from condensation, coagulation, photochemical reaction, liquid phase chemical reaction and other physical and chemical reactions of POA and volatile organic compounds (VOCs) in the atmosphere. The generation of SOA is affected by multiple factors such as emission sources and atmospheric conditions, and has a large variation range, which is not easy to accurately observe and analyze. With the increasing intensity of pollutant emission reduction in recent years, the emission of POA has been reduced, and the proportion of SOA has increased, which has become the focus of future atmospheric pollution control field, and accurate detection of the concentration and composition of SOA is the premise for exploring its formation mechanism. Therefore, determining the dynamic evolution rule of SOA has important significance for in-depth understanding of its formation mechanism, explaining the cause of fine particulate matter pollution and targeted emission reduction.

[0003] For the determination of atmospheric OA, the most commonly used traditional method is to collect atmospheric particulate matter samples using sampling membranes, detect the content of organic components therein by means of an organic carbon / elemental carbon (OC / EC) analyzer, a total organic carbon analyzer and the like, and then estimate the contribution of primary emissions and secondary reactions to OC according to the empirical ratio of OC / EC of primary emission sources. However, such a method is usually based on a 24-hour cycle, and it is difficult to capture the variation of OA in a short time, and the estimation of SOA is not accurate. In recent years, atmospheric pollution researchers have gradually popularized the application of a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) to perform high-resolution online observation of OA. The instrument is based on mass spectrometry technology, and after the components of particulate matter are evaporated, gasified and ionized, the content of particulate matter components with different mass-to-charge ratios is detected using a magnetic field, and the time resolution and mass resolution are very high. The observed OA mass spectrum is analyzed using positive matrix factor analysis (PMF), and OA factors representing different sources or secondary generation, such as coal combustion emission OA (CCOA), biomass burning emission OA (BBOA), petroleum hydrocarbons mainly from motor vehicle emissions (HOA), catering source emission OA (COA) and oxidation OA (OOA) mainly from secondary generation, can be obtained, and the contribution of each emission source, mass spectrum and concentration variation can be further analyzed.

[0004] However, in the application of the AMS-PMF observation analysis method, the identification of the pollution source represented by the OA factor is mainly based on the research experience of the oxidation degree, mass spectrum composition and daily variation of each factor. In different studies, the mass spectrum of the OA factor of the same type of pollution source is different, and therefore there is a certain uncertainty and subjectivity. At the same time, due to the principle and characteristics of the PMF method, it is assumed that the mass spectrum composition of all OA factors will not change during the observation period, and only the mass concentration changes, which is more applicable to the POA factor of the emission source. However, the OOA factor belongs to the collection of SOA substances, and will change with the change of the contribution of different emission sources and atmospheric reaction conditions, and its mass spectrum composition and oxidation degree will not be fixed in theory. Therefore, the conventional application of the AMS-PMF analysis method is also difficult to comprehensively analyze the dynamic evolution law of SOA.

[0005] The information disclosed in this Background section is only for the purpose of increasing the understanding of the general background of the application and should not be taken as an acknowledgement or any form of suggestion that this information forms prior art that is already known in this field. SUMMARY

[0006] The present application aims to provide a method for determining the dynamic evolution of secondary organic aerosols in the atmosphere, thereby overcoming the deficiencies of the prior art.

[0007] To achieve the above-mentioned purpose, the present application provides a method for determining the dynamic evolution of secondary organic aerosols in the atmosphere, comprising the following steps:

[0008] S1: Determine or use AMS to determine the mass spectrum of POA emitted by typical primary emission sources in the target research area according to existing research;

[0009] Since the emission characteristics of air pollution sources are different in different countries, regions or cities, and the composition of aerosols is different, it is necessary to determine the POA emission source spectrum of various primary emission sources in the target research area in order to carry out subsequent OA observation and source analysis; typical primary emission sources include coal combustion, biomass combustion, motor vehicle exhaust, catering activities, etc.; if the emission source spectrum of a certain type of POA in the region has been studied and determined, the existing research emission source spectrum is referred to; if the emission source spectrum of a certain type of POA in the region has not been studied and determined, the POA source spectrum is detected by using the aerosol mass spectrometer AMS; P-1 POA pollution source spectra MS EMIp (1 x m), where p represents a certain POA pollution source category, and m is the number of organic ion fragment categories constituting the source spectrum; MS EMIp The sum of the m elements is 1; at the same time, the elemental composition of the POA emitted by these pollution sources is analyzed to determine the O / C, H / C, N / C and S / C ratios and other characteristics;

[0010] In AMS, aerosol particles pass through a particle size cutter, a dryer and a flow limiting hole, are gathered into a particle beam by an optical lens, are cut by a mechanical rotating wheel cutter with a single / multi-slit to realize particle size differentiation and detection, and are heated by a tungsten vaporizer at 600°C in a high vacuum environment. The generated gas is ionized by a 70 eV electron beam; since the mass-to-charge ratios m / z of different components are different, their flight speeds in the mass spectrometer are different, and each component can be differentiated according to the time of arrival at the detector, and the organic fragments therein collectively constitute the mass spectrum of OA; generally, the SQUIRREL (Sequential Igor Data Retrieval) and PIKA (Peak Integration by Key Analysis) data processing software packages can be used to process the raw data measured by AMS, the Aiken method is used to calculate the C, H, O, N and S element contents in OA based on the mass spectrum, and then the Improved-Ambient method (Improved-Ambient method) is used to improve the accuracy of the estimation of O / C and H / C ratios;

[0011] S2: Using AMS to continuously observe the concentration of main components of atmospheric aerosol and OA mass spectrum in the target area, and establishing the mass spectrum concentration matrix ORG of OA; the specific method is as follows:

[0012] S21: Selecting a representative observation site in the target observation area;

[0013] S22: Using AMS to continuously observe the organic and inorganic components of non-refractory submicron aerosol NR-PM in the observation site 1.0 , obtaining high time resolution organic ion fragment mass spectrum and inorganic ion fragment concentration time series, and forming a matrix ORG (t x m) composed of observed atmospheric OA organic ion fragment concentrations; each row of the ORG matrix is composed of the concentrations of m organic ion fragments observed at the corresponding time, and there are t rows, i.e. t observation times; generally, 20 to 30 days of continuous observation are selected in each season, and the observation period can be appropriately extended in winter and attention is paid to the heavy pollution process; the data acquisition period can be adjusted, and generally one observation time per minute can be selected, i.e. one set of mass spectrum data is obtained per minute; generally, organic ion fragments with high signal-to-noise ratio m / z of 12 to 115 are selected to form the matrix;

[0014] S3: Using positive matrix factor analysis PMF to analyze the mass spectrum concentration matrix ORG, and setting a fixed POA factor mass spectrum to decompose and identify P-1 POA factors and 1 OOA factor; the specific method is as follows:

[0015] S31: Establishing a two-dimensional bilinear model

[0016] (1)

[0017] The TS matrix in formula (1) is composed of the concentration time series (t x p) of P OA factors; the MS matrix is composed of the mass spectrum of P OA factors (p x m), and the E matrix is the residual matrix, and the composition of each element in the ORG matrix is:

[0018] (2)

[0019] In formula (2), the org ij is the element of the i-th row and the j-th column in the ORG matrix, that is, the mass concentration of organic ion fragment j at time i; ts ip is the element of the i-th row and the p-th column in the matrix TS, that is, the mass concentration of certain organic aerosol factor p at time i; ms pjis the element in the pth row and jth column of matrix MS, i.e. the proportion of organic ion fragment j in the mass spectrum of organic aerosol factor p; e ij is the element in the ith row and jth column of matrix E, i.e. the fitting residual of organic ion fragment j at time i; P is the total number of OA factors, wherein the mass spectrum of P-1 POA factors is determined by the pollution source spectrum determined in step S1, and the variable range is set as: (3)

[0020] is the element in the pth row and jth column of matrix MS, i.e. the proportion of organic ion fragment j in the mass spectrum of organic aerosol factor p; f j,p is the proportion of any organic ion fragment j in all ion fragments in the mass spectrum of a certain organic aerosol factor p analyzed by PMF; f j,EMIp is the proportion of any organic ion fragment j in all ion fragments in the mass spectrum of a certain organic aerosol factor p determined in step S1, i.e. the jth element in MS EMIp ; is the maximum variation amplitude of the proportion of each organic fragment allowed to be adjusted, which can usually take the value of 10% of ;

[0021] S32: performing weighted least squares analysis on ORG by using positive matrix factor analysis PMF, wherein ORG is a positive matrix, each element in the mass spectrum MS and time series TS matrix is also a non-negative value, and the weighted residual sum of squares is continuously fitted to be minimized: Q

[0022] (4)

[0023] is the element in the pth row and jth column of matrix MS, i.e. the proportion of organic ion fragment j in the mass spectrum of organic aerosol factor p; σ ij is the standard deviation of the element in the matrix ORG org ij ; when the value reaches the minimum, all elements in the matrix meet the expected error, i.e. Q e ij / σ ij ≈1, at this time the expected value of Q is Q exp equal to the degree of freedom of the fitted data set:

[0024] (5)

[0025] in the fitting process Q / Q exp ​​The ratio is used to evaluate the quality of the PMF analysis results; the final TS matrix contains the time series of mass concentrations of P-1 POA factors representing different emission sources and one OOA factor generated by secondary atmospheric generation; in addition, the diurnal variation of POA factor concentration and its correlation with other gaseous pollutant concentrations can be analyzed to verify the rationality of POA factor identification; usually, the above factor analysis process can be implemented using the PMF Evaluation Toolkit (PET) and the multilinear engine (ME-2) implementation tool;

[0026] S4: Subtract the POA factor decomposed in S3 from the atmospheric OA observed in S2 to calculate a class of variable OOA. var Factors, to obtain OOA at each observation time var The mass spectrometry was then analyzed, and the OOA at each observation time was further analyzed. var The changing patterns of pollution characteristics, including concentration, mass spectrometry composition, and degree of oxidation, were studied; the specific methods are as follows:

[0027] S41: In step S3, the mass spectra of the P-1 POA factors representing different emission sources and the 1 OOA factor generated through atmospheric secondary reactions, resolved by the PMF, are assumed to be constant. However, SOA substances in the actual atmosphere exhibit more significant variations than POA substances. Therefore, assuming the POA factor mass spectra remain constant and only the concentration changes, while both the OOA factor mass spectra and concentration change, the contributions of the P-1 POA factors are subtracted from the ORG matrix to obtain the OOA. var matrix:

[0028] (6)

[0029] TS in Formula (6) POAp (t×1) is a column vector, representing a specific organic aerosol factor resolved by PMF in step S3. p Concentration time series; MS POAp (1×m) is a row vector, representing a certain organic aerosol factor resolved by PMF in step S3. p The mass spectrometry of the POA pollution source determined in step S1 was compared with that of the mass spectrometry of the POA pollution source determined in step S1. EMIp The difference is limited to the variable range in step S3; OOA var (t×m) represents OOA var The mass spectrometry concentration matrix of the factor, its first... i line, number j The elements of the column are OOA var Organic ion fragments in the factor j In timei a mass concentration of the OOA

[0030] S42: obtaining the OOA var summing up elements of each row of the matrix to obtain the OOA var a time series of the mass concentration of the OOA OOAvar (t x 1), and elements of each row are denoted as ts i,OOAvar analyzing the OOA var the concentration variation law of the OOA var may come from secondary chemical reactions of pollutants emitted by the POA pollution source in the atmosphere; and the OOA var dividing elements of each row of the matrix by the mass concentration of the OOA var factor at the time corresponding to the row ts i,OOAvar After that, each row is the mass spectrum of the OOA var factor at the time, so as to analyze the variation law of the pollution characteristics of the mass spectrum elements and the oxidation degree (O / C) of the OOA var factor.

[0031] Compared with the prior art, one aspect of the present application has the following beneficial effects:

[0032] The present application is based on the determination of various POA pollution source spectra and the AMS-PMF observation analysis method, and obtains a determination method for dynamic evolution of atmospheric secondary organic aerosol; can relatively reduce the uncertainty of POA factor recognition of the traditional PMF method, and breaks through the limitation of the traditional PMF method on the mass spectrum of the factor, recognizes a kind of OOA factor whose mass concentration and mass spectrum composition are allowed to change, represents the collection of SOA substances, is beneficial to further analyze the dynamic evolution law of SOA, can provide more basis for the generation process analysis of secondary organic aerosol and the identification of precursor, thereby promotes the research on SOA generation mechanism and atmospheric pollution causes, and the development of regional atmospheric pollution control work. DETAILED DESCRIPTION

[0033] The specific embodiments of the present application are described in detail below, but it should be understood that the protection scope of the present application is not limited by the specific embodiments.

[0034] The following presents a simplified summary of one or more aspects in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements of all aspects nor delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that is presented later.

[0035] Example:

[0036] A method for determining the dynamic evolution of atmospheric secondary organic aerosols includes the following steps:

[0037] S1: POA mass spectrometry of typical primary emission sources in the target study area, determined based on existing research or measured using AMS.

[0038] Because the emission characteristics and aerosol compositions of air pollution sources vary across different countries, regions, and cities, it is necessary to measure the emission source spectra of POA (Potential Oxygen Acid) from various primary emission sources in the target study area before subsequent OA observation and source apportionment can be conducted. Typical primary emission sources include coal combustion, biomass combustion, vehicle exhaust, and catering activities. If the emission source spectra of a certain type of POA in the region have already been studied and measured, then the existing emission source spectra should be used as a reference. If the emission source spectra of a certain type of POA in the region have not been studied and measured, then an aerosol mass spectrometer (AMS) should be used to detect the POA source spectra. A total of P-1 POA pollution source spectra (MS) are generated. EMIp (1×m), where p MS represents a specific POA pollution source category, where m is the number of organic ion fragment categories constituting that source spectrum; EMIp The sum of m elements is 1; at the same time, the elemental composition of the POA emitted by these pollution sources is analyzed to determine characteristics such as the O / C, H / C, N / C and S / C ratios;

[0039] In AMS, aerosol particles pass through a particle size cutter, dryer, and flow restrictor before being focused into a particle beam by an optical lens. A mechanical rotating wheel with single / multiple slits then separates and detects the particles. The particles are then heated at 600°C in a high-vacuum environment by a tungsten vaporizer, and the resulting gas is ionized by a 70 eV electron beam. Because different component ions have different mass-to-charge ratios (m / z), their flight velocities in the mass spectrometer vary, allowing for differentiation based on their arrival times at the detector. Organic fragments collectively constitute the mass spectrum of the OA. Typically, the raw data from AMS can be processed using the SQUIRREL (Sequential Igor Data Retrieval) and PIKA (Peak Integration by Key Analysis) data processing software packages. The Aiken method is used to calculate the C, H, O, N, and S elemental contents in the OA based on mass spectrometry. Furthermore, the improved-Ambient method (IA) is employed to enhance the accuracy of O / C and H / C ratio estimations.

[0040] S2: Using AMS to continuously observe the concentration of main components of atmospheric aerosol and OA mass spectrum in the target area, to establish the mass spectrum concentration matrix ORG of OA; the specific method is as follows:

[0041] S21: Selecting a representative observation site in the target observation area;

[0042] S22: Using AMS to continuously observe the organic and inorganic components of atmospheric non-refractory submicron aerosol NR-PM in the observation site 1.0 , to obtain high time resolution of organic ion fragment mass spectrum and concentration time series of inorganic ion fragments, and to form a matrix ORG (t x m) composed of observed organic ion fragment concentrations of atmospheric OA; each row of the ORG matrix is composed of the concentrations of m organic ion fragments observed at the corresponding time of the row, and there are t rows, i.e. t observation times; generally, 20 to 30 days of continuous observation are selected in each season, and the observation period can be appropriately extended in winter and attention is paid to the heavy pollution process; the data acquisition period can be adjusted, and generally one observation time per minute can be selected, i.e. one set of mass spectrum data is obtained per minute; generally, the organic ion fragments with high signal-to-noise ratio of m / z in 12 to 115 can be selected to form the matrix;

[0043] S3: Using positive matrix factor analysis PMF to analyze the mass spectrum concentration matrix ORG, and setting a fixed POA factor mass spectrum to decompose and identify P-1 POA factors and 1 OOA factor; the specific method is as follows:

[0044] S31: Establishing a two-dimensional bilinear model

[0045] (1)

[0046] The TS matrix in formula (1) is composed of the concentration time series (t x p) of P OA factors respectively; the MS matrix is composed of the mass spectrum of P OA factors (p x m), and the E matrix is the residual matrix, and the composition of each element in the ORG matrix is:

[0047] (2)

[0048] In formula (2), the org ij is the element of the i-th row and the j-th column in the ORG matrix, that is, the mass concentration of organic ion fragment j at time i; ts ip is the element of the i-th row and the p-th column in the matrix TS, that is, the mass concentration of certain organic aerosol factor p at time i; ms pjis the element in the pth row and jth column of matrix MS, i.e., the proportion of organic ion fragment j in the mass spectrum of organic aerosol factor p; e ij is the element in the ith row and jth column of matrix E, i.e., the fitting residual of organic ion fragment j at time i; P is the total number of OA factors, wherein the mass spectrum of P-1 POA factors is determined according to the pollution source spectrum determined in step S1, and the changeable range is set as: (3)

[0049] in formula (3) is f j,p is the proportion of any organic ion fragment j in the mass spectrum of the organic aerosol factor p analyzed by PMF; f j,EMIp is the proportion of any organic ion fragment j in the mass spectrum of the organic aerosol factor p determined in step S1, i.e., the jth element in MS EMIp ; is the maximum change range of the proportion of each organic fragment allowed to be adjusted, which can usually take the value of 10%;

[0050] S32: performing weighted least squares analysis on ORG by using the positive matrix factor analysis method PMF, wherein ORG is a positive matrix, each element in the mass spectrum MS and the time sequence TS matrix is also a non-negative value, and the weighted residual sum of squares is continuously fitted to be minimized: Q

[0051] (4)

[0052] in formula (4) is σ ij is the standard deviation of the element in the ORG matrix org ij ; when the value reaches the minimum, all elements in the matrix meet the expected error, i.e., Q e ij / σ ij ≈1, at this time, the expected value of Q Q exp is equal to the degree of freedom of the fitted data set:

[0053] (5)

[0054] in the fitting process, the value of Q / Q exp ​​​​The ratio is used to evaluate the quality of the PMF analysis results; the final TS matrix contains the time series of mass concentrations of P-1 POA factors representing different emission sources and one OOA factor generated by secondary atmospheric generation; in addition, the diurnal variation of POA factor concentration and its correlation with other gaseous pollutant concentrations can be analyzed to verify the rationality of POA factor identification; usually, the above factor analysis process can be implemented using the PMF Evaluation Toolkit (PET) and the multilinear engine (ME-2) implementation tool;

[0055] S4: Subtract the POA factor decomposed in S3 from the atmospheric OA observed in S2 to calculate a class of variable OOA. var Factors, to obtain OOA at each observation time var Mass spectrometry, then analysis of OOA var The changing patterns of pollution characteristics, including concentration, mass spectrometry composition, and degree of oxidation, were studied; the specific methods are as follows:

[0056] S41: In step S3, the mass spectra of the P-1 POA factors representing different emission sources and the 1 OOA factor generated through atmospheric secondary reactions, resolved by the PMF, are assumed to be constant. However, SOA substances in the actual atmosphere exhibit more significant variations than POA substances. Therefore, assuming the POA factor mass spectra remain constant and only the concentration changes, while both the OOA factor mass spectra and concentration change, the contributions of the P-1 POA factors are subtracted from the ORG matrix to obtain the OOA. var matrix:

[0057] (6)

[0058] TS in Formula (6) POAp (t×1) is a column vector, representing a specific organic aerosol factor resolved by PMF in step S3. p Concentration time series; MS POAp (1×m) is a row vector, representing a certain organic aerosol factor resolved by PMF in step S3. p The mass spectrometry of the POA pollution source determined in step S1 was compared with that of the mass spectrometry of the POA pollution source determined in step S1. EMIp The difference is limited to the variable range in step S3; OOA var (t×m) represents OOA var The mass spectrometry concentration matrix of the factor, its first... i line, number j The elements of the column are OOA var Organic ion fragments in the factor j In time imass concentration of the factor;

[0059] S42: obtaining OOA var S41: summing up the elements of each row of the matrix to obtain OOA var S43: obtaining the time series of the mass concentration of the factor, i.e., column vector TS OOAvar (t x 1), and the elements of each row are denoted as ts i,OOAvar S44: analyzing OOA var S45: analyzing the concentration variation of the factor, and if it has a high correlation with the mass concentration variation of a certain type of POA, it indicates that OOA var may come from the secondary chemical reaction of the pollutant emitted by the POA pollution source in the atmosphere; S46: obtaining the mass concentration of the factor in the OOA var S47: dividing the elements of each row of the matrix by the OOA var factor mass concentration ts i,OOAvar S48: obtaining the mass spectrum of the factor, and thus analyzing OOA var S49: analyzing the mass spectrum elements of the factor, the variation of the pollution characteristics such as the oxidation degree (O / C). var S50: obtaining the mass spectrum of the factor, and thus analyzing OOA var S49: analyzing the mass spectrum elements of the factor, the variation of the pollution characteristics such as the oxidation degree (O / C).

[0060] The foregoing description of specific exemplary embodiments of the application is intended to be illustrative only and is not intended to limit the application to the precise forms disclosed. Many modifications and variations are possible in light of the above teachings without departing from the spirit or essential characteristics of the application. The exemplary embodiments were chosen and described in order to explain the principles of the application and its practical application and to allow others skilled in the art to understand the application for various exemplary embodiments with various modifications being suited to the particular use contemplated. The scope of the application is to be defined by the claims and their equivalents.

Claims

1. A method for determining the dynamic evolution of atmospheric secondary organic aerosols, characterized in that, Includes the following steps: S1: Primary organic aerosol mass spectra of typical primary emission sources in the target study area, determined based on existing research or measured using high-resolution time-of-flight aerosol mass spectrometry. S2: Continuously observe the concentration of major atmospheric aerosol components and the mass spectra of organic aerosols in the target area using a high-resolution time-of-flight aerosol mass spectrometer, and establish a mass spectrometry concentration matrix of organic aerosols. S3: The mass spectrometry concentration matrix of organic aerosols was analyzed using positive definite matrix factor analysis. At the same time, a fixed mass spectrometry of primary organic aerosol factors was set up to decompose and identify P-1 primary organic aerosol factors and 1 oxidized organic aerosol factor. S4: Subtract the primary organic aerosol factor from the decomposition in S3 from the atmospheric organic aerosols observed in S2, calculate a variable secondary oxidized organic aerosol factor, obtain the mass spectrum of the secondary oxidized organic aerosols at each observation time, and then analyze the pollution characteristics of the concentration, mass spectrum composition and oxidation degree of the secondary oxidized organic aerosols at each observation time. When the high-resolution time-of-flight aerosol mass spectrometer in S1 measures the primary organic aerosol mass spectrometry of typical primary emission sources in the target study area, a total of P-1 primary organic aerosol pollutant source spectra are formed. EMIp The elemental composition of primary organic aerosols emitted from these pollution sources was analyzed, and the characteristics of the O / C, H / C, N / C and S / C ratios were determined. The specific method of S4 is as follows: S41: Assuming the mass spectra of primary organic aerosol factors remain unchanged, only their concentrations change, while the mass spectra and concentrations of secondary atmospheric oxidized organic aerosol factors both change, subtracting the contributions of P-1 primary organic aerosol factors from the ORG matrix yields the secondary atmospheric oxidized organic aerosol factor, denoted as OOA. var : TS in the formula POAp Let be a column vector, representing a specific organic aerosol factor obtained from the positive definite matrix factor analysis method in step S3. p Concentration time series; MS POAp Let be a row vector, representing a specific organic aerosol factor obtained from the positive definite matrix factor analysis method in step S3. p The mass spectrometry of the sample, compared with the MS spectrum of the primary organic aerosol pollution source determined in step S1. EMIp The difference is limited to the variable range in step S3; S42: OOA var The sum of the elements in each row of the matrix is ​​used to obtain the OOA. var The time series of the mass concentration of the factor, i.e., the column vector TS OOAvar The elements in each row are denoted as ts i,OOAvar Analyzing OOA var The concentration change pattern of the factor, OOA var Each element in each row of the matrix is ​​divided by the Out-of-Ability (OOA) at the time corresponding to that row. var Factor mass concentration ts i,OOAvar After that, each line represents the OOA at that moment. var Mass spectrometry of factors to analyze OOA var The variation patterns of pollution characteristics of mass spectrometry elemental composition and oxidation degree of the factors.

2. The method for determining the dynamic evolution of atmospheric secondary organic aerosols according to claim 1, characterized in that, The specific method of S2 follows these steps: S21: Select representative observation stations in the target observation area; S22: Continuous observation of atmospheric non-refractory submicron-sized aerosols NR-PM at the observation site using a high-resolution time-of-flight aerosol mass spectrometer. 1.0 The organic and inorganic components were analyzed to obtain high temporal resolution mass spectra of organic ion fragments and time series of inorganic ion fragment concentrations. A matrix ORG was formed, consisting of the concentrations of organic ion fragments of observed atmospheric organic matter. Each row of the matrix consists of the concentrations of m organic ion fragments observed at the corresponding time in that row. There are t rows, i.e., t observation times.

3. The method for determining the dynamic evolution of atmospheric secondary organic aerosols according to claim 2, characterized in that, For each season, select 20-30 days for continuous observation, with each minute representing one observation moment.

4. The method for determining the dynamic evolution of atmospheric secondary organic aerosols according to claim 2, characterized in that, The specific method of S3 follows these steps: S31: Establish a two-dimensional bilinear model （1） In formula (1), the TS matrix consists of the concentration time series of P organic aerosol factors; the MS matrix consists of the mass spectra of P organic aerosol factors; the E matrix is ​​the residual matrix; and each element in the ORG matrix is ​​composed of: （2） In formula (2) org ij The element in the i-th row and j-th column of the ORG matrix represents the mass concentration of organic ion fragment j at time i. ts ip Let p be the element in the i-th row and p-th column of matrix TS, which is the mass concentration of organic aerosol factor p at time i. ms pj is the element in the p-th row and j-th column of the matrix MS, which is the proportion of organic ion fragment j in all ion fragments in a certain organic aerosol factor p mass spectrometry. e ij Let be the element in the i-th row and j-th column of matrix E, which is the fitting residual of organic ion fragment j at time i; P is the total number of primary organic aerosol factors, where the mass spectra of P-1 primary organic aerosol factors are taken from the pollution source spectra determined in step S1, and the variable range is set as follows: （3） In formula (3) f j,p The proportion of any organic ion fragment j in all ion fragments in a mass spectrum of an organic aerosol factor p determined by positive definite matrix factor analysis. f j,EMIp The percentage of any organic ion fragment j in the mass spectrum of a certain organic aerosol factor p determined in step S1, i.e., the percentage of MS fragments of any organic ion fragment j in all ion fragments. EMIp The j-th element in; This represents the maximum permissible variation in the proportion of each organic fragment. S32: Weighted least squares analysis of ORG was performed using positive definite matrix factorization. ORG is a positive definite matrix, and each element in the mass spectrometry (MS) and time series (TS) matrices is also non-negative. The weighted sum of squared residuals was optimized through continuous fitting. Q Minimum: （4） In formula (4) σ ij For the ORG matrix org ij Standard deviation of elements; when Q When the value reaches its minimum, all elements in the matrix conform to their expected error, i.e., | e ij | / σ ij ≈1, at this time Q Expected value Q exp The degrees of freedom equal to the fitted dataset: （5） During the fitting process, use Q / Q exp The ratio is used to evaluate the quality of the analytical results of the positive definite matrix factor analysis method; the final TS matrix contains the time series of mass concentrations of P-1 primary organic aerosol factors representing different emission sources and 1 oxidized organic aerosol factor generated secondary in the atmosphere.

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