Organic aerosol source analysis method combining organic aerosol volatility

Abstract

The invention discloses an organic aerosol source analysis method combined with organic aerosol volatility, which comprises the following steps: S1, carrying out a calibration experiment by using a standard organic matter with known volatility, and establishing a quantitative relation model between a thermal analysis signal peak temperature and a volatility parameter; s2, treating an environmental organic aerosol sample by adopting a thermal desorption and detection process which is the same as that of the calibration experiment to obtain a thermal desorption spectrogram of the environmental organic aerosol sample; s3, obtaining a signal intensity time sequence of a plurality of organic aerosol factors; and S4, calculating to obtain the volatile distribution of each organic aerosol factor. According to the method, on the basis of the volatilization behavior of the organic aerosol in the thermal desorption process, source analysis is carried out on the volatility signal in the thermal desorption process, the volatility of the organic aerosol and the source of the organic aerosol are combined, and the source analysis capability of the organic aerosol is improved.

Description

Technical Field

[0001] This invention relates to the interdisciplinary field of environmental analytical chemistry and atmospheric environmental science, specifically to an organic aerosol source apportionment method that incorporates the volatility of organic aerosols. Background Technology

[0002] Organic aerosols (OA) are a major component of fine particulate matter (PM2.5) in China's atmosphere, exerting a profound impact on regional climate change, human health, and air quality. Although the total emissions of primary pollutants have been effectively controlled and significantly reduced in recent years, OA's crucial role in secondary transformation processes has gradually made it one of the core factors in the formation and evolution of haze pollution in my country. Currently, accurate simulations of OA mass concentration and composition using atmospheric chemical transport models still face significant challenges, primarily due to an incomplete understanding of the complex formation mechanisms and dynamic aging processes of OA in the atmosphere. Therefore, a thorough and comprehensive clarification of the sources, formation, and evolution pathways of OA in the atmosphere has become a critical issue urgently needing to be addressed in atmospheric environmental science research.

[0003] In the apportionment of OA sources, the Positive Matrix Factorization (PMF) method, as a mature acceptor model, has been widely used to analyze the source contributions of primary organic aerosols (POA) and secondary organic aerosols (SOA). This method typically uses the time-series mass spectrometry matrix of OA measured by an aerosol mass spectrometer (AMS) or an aerosol chemical spectrometer (ACSM) as input data. Based on this technique, field observations have successfully identified various typical POA factors, such as hydrocarbon-like OA (HOA) associated with traffic emissions, biomass burning OA (BBOA), and cooking OA (COA), as well as SOA factors with different oxidation levels. The identification of these OA factors is mainly based on their characteristic mass spectrometry profiles and time-series variation patterns. However, because the AMS / ACSM standard configuration uses a 70 eV electron ionization source, and the aerosol samples undergo thermal decomposition within the evaporation chamber at approximately 600°C, the detected ion signals exhibit severe fragmentation. These fragment ions lose the complete structural information of their parent molecules and cannot directly reflect the volatility characteristics of the component. This lack of information severely hinders the ability to further correlate identified OA factors with more specific precursors or emission sources with clearly defined volatile properties, thus fundamentally limiting our understanding of the specific formation pathways and aging mechanisms of OA in the atmosphere.

[0004] Volatility is a core physicochemical property of organic compounds, often expressed in terms of saturation mass concentration (VOC). Quantitative characterization is achieved through the use of volatile organic compounds (OA). The volatility of OA is closely related to its molecular chemical properties, such as oxidation state, number of carbon atoms, and type of functional groups. Therefore, the distribution and evolution of OA volatility can provide crucial information for its formation (e.g., gas-particle partitioning) and aging (e.g., oxidative thickening) processes in the atmosphere.

[0005] However, directly and quantitatively linking the volatility characteristics of organic compounds (OA) to their chemical composition and emission sources remains a significant technical challenge. Existing studies have attempted to explore the volatility distribution of OA from different sources using a combination of thermal desorption (TD) and organic mass spectrometry (AMS) (TD-AMS). However, limited by the working principle of AMS itself, even when combined with thermal desorption, it remains difficult to effectively identify and quantify specific organic compounds with different volatility at the molecular level. The information obtained remains at the factor or component level, failing to address the fundamental issue of the correlation between molecular information and volatility. Summary of the Invention

[0006] To overcome the shortcomings of the prior art, the present invention aims to provide an organic aerosol source apportionment method that combines the volatility of organic aerosols. Based on the volatilization behavior of organic aerosols during the thermal desorption process, the method performs source apportionment analysis on the volatility signal during the thermal desorption process, combining the volatility of organic aerosols with their source, thereby improving the source apportionment capability of organic aerosols.

[0007] To achieve the objective of this invention, the following solution is adopted: An organic aerosol source apportionment method incorporating the volatility of organic aerosols includes the following steps: S1. Use standard organic compounds with known volatility to conduct calibration experiments and establish the peak temperature of the thermal desorption signal. A quantitative relationship model between volatile parameters; S2. Using the same thermal desorption and detection process as the calibration experiment, the environmental organic aerosol sample is processed to obtain the thermal desorption spectrum of the environmental organic aerosol sample. S3. Using the thermal ablation spectrum data of the environmental organic aerosol sample as input, perform factor analysis source analysis to obtain the signal intensity time series of multiple organic aerosol factors. S4. Determine the peak temperature of the thermal desorption signal based on the signal intensity time series of each organic aerosol factor. The peak temperature of the thermal desorption signal of each organic aerosol factor was determined. Substituting these values ​​into the quantitative relationship model, the volatility distribution of each organic aerosol factor is calculated.

[0008] Furthermore, in step S1, the standard organic compound used in the calibration experiment is a polyethylene glycol series.

[0009] Furthermore, in step S1, establishing the quantitative relationship model specifically involves: obtaining the formula by fitting the calibration experimental data. The parameters a and b are in the text; where, It is the saturated vapor pressure.

[0010] Further, in step S1, the volatility parameter is the saturated mass concentration. It is through the formula Calculated; where, molar mass The gas constant is... It is the thermodynamic temperature.

[0011] Furthermore, in step S2, the thermal desorption and detection process includes: using pure nitrogen as a carrier gas, linearly heating the filter membrane containing particulate matter from room temperature to a specific temperature and maintaining it.

[0012] Furthermore, in step S2, during the thermal desorption and detection process, chemical ionization mass spectrometry is used to measure the gaseous component signals after volatilization online.

[0013] Furthermore, in step S3, the source analysis of the factor analysis adopts the positive definite matrix factorization method.

[0014] Furthermore, before performing the factor analysis source resolution, it is necessary to calculate the uncertainty matrix of the input data matrix. ,in The It is obtained by calculating the standard deviation of the residual between the stable data segment at the end of the thermal desorption spectrum and its linear fitting value.

[0015] Furthermore, the final stable data segment refers to the last preset number of data points in the thermal analysis spectrum.

[0016] Furthermore, in step S4, when calculating the volatility distribution of each organic aerosol factor, the quantitative relationship model parameters corresponding to the experimental conditions matched in the calibration experiment are selected based on the average characteristics of the environmental organic aerosol sample.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention integrates source apportionment and volatility characterization. It organically combines traditional organic aerosol source apportionment technology with quantitative volatility characterization technology through a standardized thermal desorption and detection process. While completing source apportionment and identifying different source factors (such as HOA, BBOA, SOA, etc.), it can directly utilize a pre-established quantitative relationship model to calculate the volatility distribution (e.g., logarithmic volatility distribution) of each source factor. This marks the first time that, within the same technological framework, the specific source attribution of organic aerosols has been directly and quantitatively linked to their key physicochemical properties (volatility).

[0018] 2. This invention overcomes the information bottleneck of traditional source apportionment techniques, significantly improving the depth of analysis. Addressing the inherent limitations of traditional AMS / ACSM-based PMF source apportionment methods, which only obtain fragmented mass spectrometry information and lack volatile dimension data, this invention creatively performs factor analysis on the overall data matrix of the "thermal desorption signal," which includes volatile information. This not only inherits and leverages the powerful source apportionment capabilities of the PMF model but also adds a volatile dimension to the analysis results, ensuring that each OA factor possesses both a "source spectrum" and a "volatile spectrum." This greatly enhances the ability to finely identify OA sources, helping to more accurately trace its precursors and analyze its aging process in the atmosphere.

[0019] 3. This invention provides a reliable tool for studying atmospheric processes and assessing environmental effects. Since the volatility of organic aerosols directly dominates their gas-particle distribution, diffusion transport, cloud condensation nucleus activity, and ultimate environmental fate in the atmosphere, the "source-volatility" linkage information provided by this invention offers more reliable data support and methodological tools for a deeper understanding of the formation mechanism of secondary organic aerosols (SOA) and for assessing the relative contributions of different OA sources to haze formation and global climate change. Attached Figure Description

[0020] Figure 1 This is a flowchart of an organic aerosol source desorption method that incorporates the volatility of organic aerosols in an embodiment of the present invention; Figure 2 This is a schematic diagram of the technical route of the organic aerosol source desorption method that incorporates the volatility of organic aerosols in an embodiment of the present invention; Figure 3 This is a schematic diagram of the calibration experiment of organic aerosol standard samples in an embodiment of the present invention; Figure 4 The PEG5-8Tmax and volatility were compared with different sampling masses and particle sizes in the embodiments of the present invention. A diagram illustrating the relationship between ( ). Figure 5 This is a schematic diagram demonstrating the FIGAERO-CIMS particulate matter sampling process during field observation in an embodiment of the present invention; Figure 6 This is a schematic diagram illustrating the calculation of the uncertainty matrix using levoglucan as an example in an embodiment of the present invention; Figure 7 This is a schematic diagram of the source spectra of various organic aerosol factors in the embodiments of the present invention; Figure 8 This is a schematic diagram of the thermal desorption spectra of various organic aerosol factors in the embodiments of the present invention. Detailed Implementation

[0021] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments. It should be noted that, without conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0022] like Figure 1 As shown, this embodiment of the invention provides a method for the source apportionment of organic aerosols that incorporates the volatility of organic aerosols, including the following steps: S1. Use standard organic compounds with known volatility to conduct calibration experiments and establish the peak temperature of the thermal desorption signal. A quantitative relationship model between volatile parameters and volatile parameters.

[0023] S2. Using the same thermal desorption and detection process as the calibration experiment, the environmental organic aerosol sample is processed to obtain the thermal desorption spectrum of the environmental organic aerosol sample.

[0024] S3. Using the thermal ablation spectrum data of the environmental organic aerosol sample as input, perform factor analysis source analysis to obtain the signal intensity time series of multiple organic aerosol factors.

[0025] S4. Determine the peak temperature of the thermal desorption signal based on the signal intensity time series of each organic aerosol factor. The peak temperature of the thermal desorption signal of each organic aerosol factor was determined. Substituting these values ​​into the quantitative relationship model, the volatility distribution of each organic aerosol factor is calculated.

[0026] The organic aerosol source apportionment method in this invention, which incorporates the volatility of organic aerosols, improves upon traditional organic aerosol source apportionment methods, enhances the analytical capability of organic aerosol source apportionment analysis, and incorporates the volatility of organic aerosols into the scope of consideration. This helps to analyze the interaction between the organic aerosol generation process and its volatility, providing methodological support for controlling air pollution and assessing the climate effects of particulate matter.

[0027] The following is a more detailed description of the organic aerosol source analysis method that incorporates the volatility of organic aerosols according to embodiments of the present invention.

[0028] like Figure 2 As shown in the figure, the present invention provides a method for the source analysis of organic aerosols that incorporates the volatility of organic aerosols. The specific details are as follows: In this embodiment, particulate matter membrane sampling is required for environmental samples and subsequent calibration experimental samples.

[0029] Specifically, a suitable particulate matter sampler should be used to collect particulate matter, and PTFE material is recommended for the particulate matter sampling filter membrane.

[0030] In this embodiment, the collected samples are subjected to uniform thermal analysis and signal acquisition to obtain thermal analysis spectra.

[0031] Specifically, the collected samples are heated with pure nitrogen gas, the nitrogen temperature of which is set to rise linearly from room temperature to a specific temperature (T) and maintained for a period of time. The particulate matter sample gradually heats up under the nitrogen gas, and its temperature change is positively correlated with the temperature change of the nitrogen gas. During the heating process, the particulate matter on the sampling membrane volatilizes, transforming from a particulate state to a gaseous state. Chemical ionization mass spectrometry (CIMS) is used to measure the gaseous components after volatilization to obtain the signals of each component (thermal desorption signals), thus forming a thermal desorption spectrum (the curve of thermal desorption signal versus heating temperature). Because different components in the particulate matter have different volatility, their thermal decomposition processes also differ, and their thermal desorption signal changes also differ. Substances with higher volatility volatilize at lower heating temperatures, while those with higher volatility volatilize at higher heating temperatures. Here, the temperature corresponding to the peak value of the thermal desorption signal is called Tmax. Tmax exhibits a linear relationship with the saturated vapor pressure (Psat) of the substance. In the formula, a and b are the fitted curves. This is the saturated vapor concentration of the component, which is usually used to characterize its volatility. This is the molar mass of the component. The gas constant is 8.314 J mol. -1 K -1 ), The thermodynamic temperature is 298.15 K.

[0032] In this embodiment, in order to establish a quantitative relationship model, it is necessary to conduct calibration experiments on organic aerosol standard samples.

[0033] Specifically, because the sampling quality and particle size in membrane sampling affect the thermal desorption spectra of components, even substances of the same component will have different thermal desorption spectra under different sampling concentrations and particle sizes. Therefore, it is necessary to calibrate the thermal desorption spectra and their corresponding volatility in the laboratory to determine the fitting curves a and b. This invention uses standard organic compounds PEG5, PEG6, PEG7, and PEG8 (PEG5-8) for calibration experiments. PEG5-8 are organic components with known volatility. By analyzing their thermal desorption spectra under different particle sizes and sampling qualities, the relationship between the thermal desorption spectra and the volatility of organic components can be constructed.

[0034] like Figure 3As shown, PEG5-8 is first dissolved in acetonitrile to form a solution. Then, a particle generator produces PEG5-8 particles. These particles first pass through a dilution tank, where clean, dry air is continuously circulated to ensure complete evaporation of the acetonitrile, leaving only the PEG5-8 component. After evaporation, the particles are screened by a differential electromobility analyzer (DMA). Particles of a specific size (Dp) are then passed through a particle number counter (CPC) for counting and a particle membrane sampler for sampling.

[0035] Sampling is stopped after a specific sampling time (t). The sampling concentration (M) is calculated based on the sampling flow rate (Q), sampling time (t), particulate number concentration (N), and particle size (Dp), using the following formula: By setting the sampling time and particle size, experiments with different concentrations and sampling concentrations are conducted to form PEG5-8 film samples with different particle sizes and sampling concentrations. By repeating step 2, PEG5-8 thermal desorption curves with different particle sizes and sampling masses can be obtained. Based on the known volatility of PEG5-8, the fitting parameters a and b for different sampling masses and particle sizes are determined by formulas (1) and (2).

[0036] In this embodiment, after obtaining the thermal desorption spectrum of the environmental sample, the thermal desorption signal source can be analyzed.

[0037] Specifically, source analysis involves taking a signal matrix... Decomposed into time series matrices of each factor Source spectrum matrix of each factor and residuals : Here This is a thermal ablation spectrum of a particulate membrane sample. Here, only organic molecules measured by CIMS are used as data input to analyze the source of organic aerosols.

[0038] In source analysis, an uncertainty matrix needs to be input. ),in For species sequences, This is a data sequence. For a specific membrane sample's thermal desorption spectrum, Assume: In the thermal desorption analysis of a specific membrane sample, this invention assumes that the last 20 data points are in a stable state, meaning that the particulate matter is no longer thermally decomposed. It can then be calculated as the signal residual l ( Standard deviation: For the last 20 data points in the thermal desorption analysis of a specific membrane sample, This is for linear fitting of the data points.

[0039] In obtaining Then, source analysis can be performed on the thermal spectrum signal matrix to solve for different factors and their corresponding source spectra.

[0040] In this embodiment, the volatility distribution of each organic aerosol factor can be calculated based on the factor signals obtained from source apportionment.

[0041] Specifically, after source analysis of the thermal aerosol spectrum of the membrane sample, the signal intensity time series of different factors can be obtained, i.e., the signal curves of different organic aerosol factors changing with time. Since there is a correlation between the change in time and the change in temperature during heating, the signal intensity of different factors at different temperatures can be obtained by transforming this time series, thereby obtaining the signal intensity of each factor. The volatility distribution of each factor can be obtained by formulas (1) and (2).

[0042] Experimental example: 1. Background Introduction From October 2nd to November 16th, 2019, field observation experiments were conducted at the Heshan Environmental Monitoring Superstation in Jiangmen, Guangdong Province. Particulate matter sampling and analysis were performed using a dual-channel gas-particle sampler-chemical ionization mass spectrometry (FIGAERO-CIMS). FIGAERO-CIMS can perform online sampling and analysis of both gaseous and particulate matter. During gaseous sampling, particulate matter is collected onto a sampling membrane. After gaseous sampling, the instrument switches to particulate matter sampling mode, heating the sampling membrane with nitrogen to cause it to volatilize, and then measuring the volatilized components. In this observation, each cycle lasted one hour. In particulate matter analysis mode, nitrogen was first heated from room temperature to above 175°C within 12 minutes and maintained for 24 minutes.

[0043] 2. Calibration experiment of organic aerosol standard samples In the laboratory, a calibration experiment was conducted to determine the relationship between Tmax and volatility of organic components. PEG5-8 was selected as the drug, and the Tmax of PEG5-8 under different sampling masses and particle sizes was measured. The relationship between Tmax and volatility was then calculated, and the fitting parameters a and b were obtained. The results are shown in Table 1. Additionally, as... Figure 4 As shown, the relationship between PEG5-8Tmax and volatility ( ) is illustrated with different sampling qualities and particle sizes. The relationship between ).

[0044] Table 1. Samples a and b with different sampling masses and particle sizes 3. Sampling and analysis of environmental organic aerosol samples like Figure 5 As shown, the relationship between thermal desorption signals and heating temperatures in three particulate matter sampling cycles of FIGAERO-CIMS during field observation is illustrated. The thermal desorption signal is the sum of the signal intensities of all organic molecules. When used as input to the PMF source analysis model, it is a data matrix showing the variation of signal intensities of different organic molecules with data points, and all measurement cycles are integrated into a single data matrix.

[0045] 4. Source analysis of thermal desorption signals The uncertainty matrix in the PMF source analysis process is calculated according to formulas (4) and (5). ),like Figure 6 As shown, the thermal desorption spectrum of levoglucan during a certain particulate matter measurement cycle and the corresponding... The calculation process, and so on, yields the results for all organic species across all measurement cycles during the observation period. Thus obtain .

[0046] After inputting the data matrix and uncertainty matrix into the PMF model, source analysis is performed. Here, eight factors are selected as the solution. Figure 7 As shown, the source spectra of various organic aerosol factors are presented, such as... Figure 8 The image shown is a thermal desorption spectrum of each organic aerosol factor.

[0047] After obtaining the thermal desorption spectra of different organic aerosol factors, their Tmax can be obtained. Based on the average organic aerosol mass and particle size during the observation period, the fitting results of experiment number 3 (particle size of 200 nm and sampling mass of 407 ng) in Table 1 are selected, i.e., a=-0.206 and b=3.732. The volatility of each factor is calculated, and the results are shown in Table 2. Table 2. Relationship between Tmax and volatility of various organic aerosol factors The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A method for source apportionment of organic aerosols that incorporates the volatility of organic aerosols, characterized in that, Includes the following steps: S1. Use standard organic compounds with known volatility to conduct calibration experiments and establish the peak temperature of the thermal desorption signal. A quantitative relationship model between volatile parameters; S2. Using the same thermal desorption and detection process as the calibration experiment, the environmental organic aerosol sample is processed to obtain the thermal desorption spectrum of the environmental organic aerosol sample. S3. Using the thermal ablation spectrum data of the environmental organic aerosol sample as input, perform factor analysis source analysis to obtain the signal intensity time series of multiple organic aerosol factors. S4. Determine the peak temperature of the thermal desorption signal based on the signal intensity time series of each organic aerosol factor. The peak temperature of the thermal desorption signal of each organic aerosol factor was determined. Substituting these values ​​into the quantitative relationship model, the volatility distribution of each organic aerosol factor is calculated.

2. The organic aerosol source desorption method incorporating the volatility of organic aerosols according to claim 1, characterized in that, In step S1, the standard organic compounds used in the calibration experiment are polyethylene glycol series.

3. The organic aerosol source desorption method incorporating the volatility of organic aerosols according to claim 1, characterized in that, In step S1, establishing the quantitative relationship model specifically involves: obtaining the formula by fitting the data from the calibration experiment. The parameters a and b are in the text; where, It is the saturated vapor pressure.

4. The organic aerosol source desorption method incorporating the volatility of organic aerosols according to claim 3, characterized in that, In step S1, the volatility parameter is the saturated mass concentration. It is through the formula Calculated; where, molar mass The gas constant is... It is the thermodynamic temperature.

5. The organic aerosol source desorption method incorporating the volatility of organic aerosols according to claim 1, characterized in that, In step S2, the thermal desorption and detection process includes: using pure nitrogen as a carrier gas, linearly heating the filter membrane containing particulate matter from room temperature to a specific temperature and holding it thereafter.

6. The organic aerosol source desorption method incorporating the volatility of organic aerosols according to claim 1 or 5, characterized in that, In step S2, during the thermal desorption and detection process, chemical ionization mass spectrometry is used to measure the gaseous component signals after volatilization online.

7. The organic aerosol source desorption method incorporating the volatility of organic aerosols according to claim 1, characterized in that, In step S3, the source analysis of the factor analysis adopts the positive definite matrix factorization method.

8. The organic aerosol source desorption method incorporating the volatility of organic aerosols according to claim 1 or 7, characterized in that, Before performing the factor analysis source resolution, it is necessary to calculate the uncertainty matrix of the input data matrix. ,in The It is obtained by calculating the standard deviation of the residual between the stable data segment at the end of the thermal desorption spectrum and its linear fitting value.

9. The organic aerosol source desorption method incorporating the volatility of organic aerosols according to claim 8, characterized in that, The final stable data segment refers to the last preset number of data points in the thermal analysis spectrum.

10. The organic aerosol source desorption method incorporating the volatility of organic aerosols according to claim 1, characterized in that, In step S4, when calculating the volatility distribution of each organic aerosol factor, the quantitative relationship model parameters corresponding to the experimental conditions matched in the calibration experiment are selected based on the average characteristics of the environmental organic aerosol sample.

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