VOCs source analysis method and device based on flux data, medium and product

Abstract

The invention discloses a VOCs source analysis method and device based on flux data, a medium and a product, and relates to the technical field of VOCs source analys.The method comprises the steps that VOCs species data in gas to be detected are obtained according to mass spectrum peak data; calculating VOCs flux data of the species according to the wind speed data, the VOCs species name data and the corresponding species concentration data; calculating a VOCs flux error matrix according to the VOCs flux data; and inputting the VOCs flux matrix, the VOCs flux error matrix, the VOCs species name data, the mass-to-charge ratio data and the time axis data into a PMF model for source analysis. According to the present invention, the chemical information of the flux measurement is slightly influenced by the photochemical reaction, and the primary source of the VOCs can be directly identified, such that the VOCs emission source characteristics can be directly represented so as to effectively improve the VOCs source analysis accuracy.

Description

Technical Field

[0001] The present application relates to the technical field of VOCs source analysis, and in particular to a VOCs source analysis method, equipment, medium and product based on flux data. Background Art

[0002] Source apportionment technology is a technology that qualitatively or quantitatively identifies the sources of atmospheric pollutants in the environment through methods such as chemistry, physics, and mathematics. Volatile organic compounds: referred to as VOCs (VOCs (volatile organic compounds) volatile organic compounds), refers to organic chemicals with a vapor pressure above 13.3Pa and a boiling point below 260°C under normal conditions (20°C, 101.3kPa). At present, the methods for measuring the source of VOCs are mostly source apportionment methods based on VOCs concentration. The problem with this method is that it will be affected by meteorological factors such as boundary layer height, air mass transport, and atmospheric photochemical reactions. It is impossible to directly measure the emission and deposition of VOCs, which makes the source apportionment method based on VOCs concentration have great uncertainty and analysis difficulties. At the same time, this method is difficult to accurately quantify the relative contributions of primary emissions and secondary generation of VOCs. In practical applications, since the concentrations of a large number of VOCs species have secondary generation sources, this brings great uncertainty to the source results of VOCs. Summary of the invention

[0003] The purpose of this application is to provide a VOCs source analysis method, equipment, medium and product based on flux data, which can effectively improve the accuracy of VOCs source analysis.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In the first aspect, the present application provides a VOCs source analysis method based on flux data, comprising:

[0006] Obtain wind speed data, mass spectrum peak data of the gas to be measured, and time axis data during mass spectrum peak measurement;

[0007] Acquire VOCs species data in the gas to be tested according to the mass spectrum peak data; the VOCs species data includes: VOCs species name data, corresponding species concentration data and corresponding mass-to-charge ratio data;

[0008] Calculating the VOCs flux data of the species according to the wind speed data, the VOCs species name data and the corresponding species concentration data;

[0009] Calculating a VOCs flux error matrix according to the VOCs flux data; the VOCs flux error matrix includes: a measurement error matrix, a system error matrix and a random error matrix;

[0010] Converting the VOCs flux data into a VOCs flux matrix;

[0011] Inputting the VOCs flux matrix, the VOCs flux error matrix, the VOCs species name data, the mass-to-charge ratio data and the time axis data into an orthogonal matrix factor decomposition model to obtain the number of factors, the source component spectrum of the factors and the flux time series of the factors;

[0012] Determine the pollution source represented by the factor according to the source component spectrum of the factor;

[0013] The specific emission source of the factor is determined based on the flux time series of the factor.

[0014] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-mentioned VOCs source analysis methods based on flux data.

[0015] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned methods for VOCs source analysis based on flux data.

[0016] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned VOCs source analysis methods based on flux data.

[0017] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0018] The present application provides a VOCs source analysis method, device, medium and product based on flux data. First, wind speed data, mass spectrum peak data of the gas to be tested and time axis data of the mass spectrum peak measurement are obtained; VOCs species data in the gas to be tested are obtained according to the mass spectrum peak data; the VOCs species data include: VOCs species name data, corresponding species concentration data and corresponding mass-to-charge ratio data; VOCs flux data of species are calculated according to the wind speed data, the VOCs species name data and the corresponding species concentration data; VOCs flux data of species are calculated according to the VOCs flux data. The VOCs flux error matrix includes: a measurement error matrix, a systematic error matrix and a random error matrix; the VOCs flux data is converted into a VOCs flux matrix; the VOCs flux matrix, the VOCs flux error matrix, the VOCs species name data, the mass-to-charge ratio data and the time axis data are input into the PMF model to obtain the number of factors, the source component spectrum of the factors and the flux time series of the factors; the pollution source represented by the factors is determined according to the source component spectrum of the factors; the specific emission source of the factors is determined according to the flux time series of the factors. In flux observation, the characteristic transmission time of flux turbulence is affected by the surface friction speed and the measurement height of the measuring instrument, which is usually only a few seconds to a few minutes. This means that the chemical information of atmospheric flux measurement is minimally affected by photochemical reactions, so that the primary source of VOCs can be directly identified, and the secondary generation part of VOCs is eliminated. Therefore, the measurement method based on flux data in this application can directly characterize the source characteristics of VOCs emissions, thereby effectively improving the accuracy of VOCs source analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 A schematic diagram of a flow chart of a VOCs source analysis method based on flux data provided in one embodiment of the present application;

[0021] Figure 2 A schematic flow chart of a VOCs source analysis method based on flux data provided in another embodiment of the present application;

[0022] Figure 3 A schematic diagram of a flux species screening mechanism provided in another embodiment of the present application;

[0023] Figure 4A schematic diagram of the time series proportion of factors and the average contribution of five factors to the total VOCs flux provided in another embodiment of the present application; Figure 4 (a) is a schematic diagram of the factor time series proportion. Figure 4 (b) is a schematic diagram of the average contribution of the five factors to the total VOCs flux.

[0024] Figure 5 A schematic diagram of the average contribution of five factors of VOCs flux source analysis to a specific species provided in another embodiment of the present application;

[0025] Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] There have been many studies on the sources of VOCs in different regions (such as cities and forest areas), but they are all based on the source analysis method of VOCs concentration. However, this method has limitations. The concentration of atmospheric active gases is affected by meteorological factors such as boundary layer height, air mass transport and atmospheric photochemical reactions. Therefore, the atmospheric concentration cannot directly reflect the emission and deposition of substances, making the source analysis based on concentration data extremely uncertain and difficult to analyze. The principle of flux measurement is achieved by capturing turbulence in the atmosphere. The classic turbulence time scale ranges from a few seconds to 10 minutes. This determines that the photochemical impact of VOCs is significantly smaller than that of VOCs concentration measurement, so it can more intuitively characterize the source characteristics of VOCs emissions. Although there have been some studies on VOCs flux measurements at home and abroad, there is a lack of source analysis research based on VOCs flux measurement data, which makes it impossible to intuitively characterize the source characteristics of VOCs emissions. Therefore, in order to overcome the shortcomings of the current VOCs concentration source analysis method and broaden the VOCs source analysis method, the present invention performs source analysis on VOCs flux based on the orthogonal matrix factorization model (PMF) to identify the main VOCs flux emission sources and quantify the relative contribution of emission sources.

[0028] Source apportionment technology is a research method that uses chemical, physical, mathematical and other methods to qualitatively or quantitatively identify the sources of atmospheric pollutants in the environment. Currently, most VOCs source studies based on measurements are based on source apportionment methods based on VOCs concentrations. The biggest problem with this method is that it is affected by meteorological factors such as boundary layer height, air mass transport and atmospheric photochemical reactions. It is unable to directly measure VOCs emissions and deposition, making source apportionment based on concentration data extremely uncertain and difficult to analyze. At the same time, this method is difficult to accurately quantify the relative contributions of primary emissions and secondary generation of VOCs. In practical applications, since the concentrations of a large number of VOCs species have secondary generation sources, this brings great uncertainty to the source results of VOCs.

[0029] Therefore, in view of the limitations of the above-mentioned prior art, this application proposes a VOCs source analysis method based on VOCs flux data. In flux observation, the flux turbulence characteristic transmission time (i.e., the turbulence time scale between the emission source and the sensor detection) is affected by the surface friction speed and the measurement height of the measuring instrument, which is usually only a few seconds to a few minutes. This means that the chemical information of the atmospheric flux measurement is minimally affected by the photochemical reaction (compared to the concentration measurement method), and can directly eliminate the secondary generation part of VOCs, thereby directly characterizing the source characteristics of VOCs emissions. In response to the key scientific issue of the large uncertainty in the understanding of VOCs emissions, a VOCs flux source analysis method based on the PMF model is constructed to identify the emission sources of the main VOCs fluxes in different regions, and quantify the specific contributions of different emission source types to various VOCs species. By comparing the VOCs source analysis calculation results based on flux measurement and concentration measurement, it is concluded that this method can effectively reduce the uncertainty of the VOCs source analysis method, thereby more accurately identifying and quantifying the sources of VOCs.

[0030] This application proposes a source parsing method based on VOCs flux data, which can identify the main VOCs emission sources in different regions, filling the gap in source parsing technology based on VOCs flux data. This application is based on VOCs flux data and uses the orthogonal matrix factorization model (PMF) for source parsing, which is used to identify and quantify the contribution of the main VOCs emission sources in different regions, filling the gap in source parsing technology based on VOCs flux data. At the same time, it can solve the problem that source parsing based on VOCs concentration is affected by meteorological factors and atmospheric photochemical reactions. It is a supplement and improvement to the source parsing method based on VOCs concentration, reducing the uncertainty of the VOCs source parsing method.

[0031] Among them, some professional terms related to technical solutions are explained:

[0032] Orthogonal Matrix Factorization Model: PMF for short, is a multivariate statistical method widely used in the field of environmental science, especially in the source analysis of atmospheric pollutants. The PMF model decomposes the complex pollution source contribution into different source components and their respective contribution rates by decomposing the sample data matrix collected at the receptor point.

[0033] Oxygenated volatile organic compounds, referred to as OVOCs, are a class of organic compounds that play an important role in atmospheric chemical processes. They are mainly composed of aldehydes and ketones, alcohols, ethers, low molecular weight organic acids, organic esters, and compounds such as aldehydes and ketones.

[0034] Flux: Flux is a physical concept, which refers to the physical quantities such as momentum, heat and matter transported through a unit area of ​​an interface per unit time.

[0035] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0036] In an exemplary embodiment, Figure 1 As shown, a VOCs source analysis method based on flux data is provided, which is executed by a computer device, specifically, it can be executed by a computer device such as a terminal or a server alone, or it can be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to a server as an example for explanation, including the following steps S1 to S8. Among them:

[0037] S1. Obtain wind speed data, mass spectrum peak data of the gas to be measured, and time axis data during mass spectrum peak measurement.

[0038] S2. Acquire VOCs species data in the gas to be tested according to the mass spectrum peak data; the VOCs species data includes: VOCs species name data, corresponding species concentration data and corresponding mass-to-charge ratio data.

[0039] In this embodiment, based on the original data measured by PTR-ToF-MS (proton reaction time-of-flight mass spectrometry), the measured mass spectrum peaks are fitted with high resolution using mass spectrometry instrument data analysis software to obtain the VOCs species name data, corresponding mass-to-charge ratio data, and species signal data measured during the mass spectrometry measurement.

[0040] According to the species signal data, combined with the species response factor obtained by the calibration experiment with chemical standards, the species signal data is converted into species concentration data. The calculation formula is as follows:

[0041]

[0042] Where [X] represents species concentration, Signal represents species signal, and Sensitivity represents species response factor.

[0043] Then the concentration detection limit is calculated as follows:

[0044]

[0045] Where DL is the concentration detection limit of the species; C f and B are the average signal and background signal of the species during mass spectrometry measurement; t is the time resolution of VOCs flux data during mass spectrometry measurement (i.e., the measurement interval required to obtain a flux data, generally 30 min), SNR is the signal-to-noise ratio of VOCs measurement, and its value is set to 3 when calculating the detection limit.

[0046] By calculating the concentration detection limit, VOCs species with concentrations higher than the detection limit are screened out, and VOCs species with average concentrations lower than the detection limit will not be included in subsequent analysis.

[0047] S3. Calculate the VOCs flux data of the species according to the wind speed data, the VOCs species name data and the corresponding species concentration data.

[0048] In this embodiment, the VOCs flux data is calculated by combining the wind speed data measured by the three-dimensional ultrasonic anemometer:

[0049]

[0050] In the formula, w' and c' represent the change of vertical wind speed and the change of certain VOCs concentration respectively.

[0051] Then, according to the species concentration data and the wind speed data, the VOCs species data are marked for data quality using the steady-state test and the complete turbulence characteristic test method to obtain the data quality marking result; the VOCs species data corresponding to the quality marking result higher than the preset threshold are deleted. The marked data of each VOCs species data is obtained, and the data points that do not meet the flux measurement conditions (data marking value is greater than 6) are eliminated.

[0052] In this embodiment, in order to ensure the quality of VOCs flux data, a data quality marking method is used to screen the data. The quality marking method for flux data includes steady-state testing and complete turbulent characteristic testing. The specific operation steps are as follows:

[0053] In the first step, the steady-state condition parameters and turbulence development characteristic parameters are calculated by combining the measured VOCs concentration data and wind speed data to obtain the parameter results and obtain the data mark value of the parameter based on the data mark classification range (Table 1). The calculation method of the steady-state condition parameters and turbulence development condition parameters is shown in the following formula.

[0054] Table 1 Data label classification range

[0055]

[0056] In the second step, the highest number of data marks in the steady-state test and the complete turbulence characteristic test is selected as the total mark (for example, the steady-state test is 3 and the turbulence development characteristic test is 5, then the total mark is 5).

[0057] In the third step, the data marked as 1-6 are used for data analysis, while the data marked as 7-10 are removed.

[0058]

[0059] In the formula, x s Represents the horizontal wind component or scalar, RN x The steady-state condition parameters of the representative species in the average interval (30 minutes). The calculation formula of the turbulence development condition parameters is as follows:

[0060]

[0061] In the formula, ITC σ is the characteristic parameter of turbulence development, and They represent the wind steady-state parameters obtained from the theoretical model and the measured wind steady-state parameters under neutral conditions, respectively.

[0062] Then, the species flux detection limit is calculated based on the VOCs concentration data and wind speed data, and the species with an average VOCs flux value lower than their flux detection limit are eliminated, and the remaining VOCs species that meet the flux detection limit enter the next step of analysis. The calculation formula for the flux detection limit is:

[0063] LOD flux =3*RE noise ;

[0064] Among them, random error The formula for calculating flux is the same as above, but in terms of data, the wind speed data and concentration data are randomly shifted forward or backward (forming the noise of flux measurement after data misalignment calculation) by 300-900 data points. flux and RE noise They are the flux detection limit and random errors of VOCs flux measurement, respectively.

[0065] S4. Calculate a VOCs flux error matrix according to the VOCs flux data; the VOCs flux error matrix includes: a measurement error matrix, a system error matrix and a random error matrix.

[0066] Specifically, in this embodiment, the error mainly comes from three aspects. By considering the three aspects of measurement error, system error and random error, the flux error is obtained, wherein the matrix is ​​a form of calculation.

[0067] S5. Convert the VOCs flux data into a VOCs flux matrix.

[0068] The VOCs flux data are synthesized into a VOCs flux matrix. If there are i VOCs species and each species has j data points, a matrix with i columns and j rows is generated. In the matrix, for flux data points that are less than 0 or are null values, the median flux of the species is used to replace them.

[0069] The second step is to calculate the error U of PTR-ToF-MS measurement of VOCs concentration for all VOCs species after screening. PTR,ij , the systematic error of the measured flux, and the random error RE of the flux measurement noise The method for calculating the VOCs concentration error is shown in the following formula:

[0070]

[0071] Where U PTR,ij SNR is the error in the PTR-ToF-MS measurement of species j concentration at time period i. ij is the signal-to-noise ratio of species j in time period i, which is used to evaluate the counting statistical error in the instrument measurement process and is calculated by the following formula. ij is the average concentration of j in time period i (30 min). ij It is the error rate of PTR-ToF-MS during the measurement process, and the value is set to 20%. Among them, 10% of the error comes from fluctuations in instrument operation, such as ion source temperature fluctuations, parent ion signal fluctuations, etc. The other 10% comes from the error caused by calibration using standard gases during long-term observations. DL is the instrument detection limit of species j, and the calculation method is as follows:

[0072]

[0073] In the formula, SNR ij is the signal-to-noise ratio of species j in the i-th time period; C f，j and B are the average and background signals of species j during mass spectrometry measurement; t is the time interval (30 min) for VOCs flux calculation used in PMF. Since the systematic error of flux measurement can be estimated based on the flux averaging interval, it is considered negligible after evaluation.

[0074] Calculate the VOCs flux error. The VOCs flux error is obtained by adding the error of VOCs concentration and the random error of flux measurement. However, the error matrix of VOCs concentration is not completely transferred to VOCs flux measurement. According to the evaluation, about half of the VOCs concentration error is transferred to the VOCs flux measurement error. Therefore, the error calculation of VOCs flux is shown as follows:

[0075]

[0076] Where U total,ij is the error in the flux of species j measured by PTR-ToF-MS in time period i, RE noise,ij is the random error in measuring the flux of species j in time period i, U PTR,ij is the error in the PTR-ToF-MS measurement of species j concentration at time period i.

[0077] Synthesize the VOCs flux error matrix. Based on the calculated VOCs flux error data, for flux data points with concentrations less than 0 or null values, the corresponding error data is set to three times the median of the corresponding species flux. The flux error data of all the above-calculated VOCs species are synthesized into a VOCs flux error matrix. If there are i VOCs species and each species has j data points, a matrix with i columns and j rows is generated. The flux matrix and the flux error matrix should have the same number of rows and columns, and the data should correspond one to one.

[0078] S6. Input the VOCs flux matrix, the VOCs flux error matrix, the VOCs species name data, the mass-to-charge ratio data and the time axis data into the PMF model to obtain the number of factors, the source component spectrum of the factors and the flux time series of the factors.

[0079] S7. Determine the pollution source represented by the factor according to the source component spectrum of the factor.

[0080] S8. Determine the specific emission source of the factor based on the flux time series of the factor.

[0081] Input the VOCs flux matrix, VOCs flux error matrix, VOCs species name data, mass-to-charge ratio data, and time axis data into the PMF Evaluation Tool 3.05 program package based on Igor Pro software for source analysis. The specific steps are as follows:

[0082] In the first step, the VOCs flux matrix, VOCs flux error matrix, VOCs species name data, mass-to-charge ratio data and time axis data of mass spectrometry measurement are imported into the PMF software. First, the flux matrix needs to be removed from the spike outliers that exceed 2 consecutive points. Secondly, the error matrix needs to be removed from the spike outliers that exceed 2 consecutive points. Finally, the empty values ​​and rows containing 0 in the matrix are removed to ensure that the input data meets the model requirements.

[0083] In the second step, the species flux signal-to-noise ratio (S / N) was calculated to assess the data quality, as shown in the following formula:

[0084]

[0085] In the formula, Flux signal-to-noise ratio of representative species, C f represents the species response factor, [X] represents the species concentration, t represents the measurement period, and B represents the background signal. The S / N results are divided into three categories: less than 0.2, between 0.2-2, and greater than 2. The flux data of the two categories of less than 0.2 and between 0.2-2 are reduced in weight to obtain flux data with further quality assurance.

[0086] The third step is to set the parameters of the PMF model. Generally, the number of factors is set between 1 and 10 to find the optimal solution. The Fpeak (parameter for adjusting the rotational freedom of the factor) value in the PMF model is set to a strength range of -3 to 3 with an interval of 0.2. The storage path is selected and the source is parsed. The PMF model results obtained include the following:

[0087] 1. The number of factors (which can be understood as the number of analyzed pollution sources) ranging from 1 to 10 corresponds to the Q (residual sum of squares) / Qexp (expected residual sum of squares).

[0088] 2. Under specified factor conditions (such as 1 to 10), the flux time series of all factors (referring to a series of VOCs flux data points arranged in chronological order, reflecting the changes in VOCs flux at different time points).

[0089] 3. Under the condition of a specified number of factors (such as 1 to 10), the source component spectrum corresponding to all factors, where the component spectrum shows the composition distribution of different VOCs components in each factor.

[0090] Based on the above PMF model results, the number and name of VOCs flux sources are determined, mainly including the number of factors determined based on Q / Qexp, factor component spectrum comparison, tracer species and factor daily changes comparison, and factor component spectrum and measured source spectrum comparison. The specific steps are as follows:

[0091] The first step is to preliminarily determine the number of factors by Q / Qexp. The Q (residual sum of squares) value is a key parameter used in this embodiment to verify and determine the optimal number of factors and verify the error matrix. It is an important basis for judging whether the PMF error matrix setting is reasonable and the factor selection. The Q value is closely related to the residual and error. The larger the error of the data, the lower its weight in the calculation. The calculation formula is as follows:

[0092]

[0093] In the formula, e ij is the residual of the i-th sample of the j-th species; u ij is the error of the i-th sample of the j-th species. Q / Qexp refers to the ratio of Q of all data points to their expected error Qexp, which is used to test whether the setting of the error matrix is ​​reasonable. At the same time, the appropriate number of factors is selected according to the decrease in Q / Qexp in different factors. If Q / Qexp~1, it means that all data points in the matrix are within their expected error range. If Q / Qexp>>1, it means that the error of the input data is seriously overestimated. On the contrary, if Q / Qexp<<1, it means that the error of the input data is seriously underestimated. Generally speaking, when the number of factors increases, Q / Qexp continues to decrease. If the ratio is close to 1 when the factor is high, it means that the error matrix of the flux is reasonably set. When the factor increases to a certain value, the decrease in Q / Qexp is significantly reduced, indicating that when the number of factors is greater than or equal to this value, the VOCs flux data will be over-analyzed, which is used as the basis for judging whether the number of factors is reasonable. When the number of factors increases by 1 (Q / Qexp is close to 1 at this time), but the decrease in Q / Qexp is less than 20%, the number of factors is taken as the optimal number of factors.

[0094] In the second step, based on the number of optimal factors (pollution sources) for flux source analysis determined in the first step, the component spectra of all factors are compared and analyzed to determine the component characteristics of different factors and preliminarily determine the pollution sources that different factors may represent.

[0095] The third step is to determine the specific emission source represented by the factor by comparing the tracer species (usually compounds that can characterize a specific emission source, such as nitrogen oxides in cities that can indicate motor vehicle emissions) with the daily variation of the factor after obtaining the component characteristics of different factors. The daily variation of the tracer or factor is based on the tracer data or factor data and the corresponding time data, and the number of hours corresponding to each time is calculated respectively, and the same number of hours is averaged to obtain the average flux value of different hours.

[0096] The fourth step, if the second and third steps are unable to confirm the emission source represented by a certain factor, then compare and analyze the factor component spectrum with the VOCs source component spectrum obtained in the pollution source emission experiment, and calculate the similarity between the two (as shown in the following formula). If the angle between the two is less than 30°, it can be determined that the factor component spectrum is consistent with the VOCs source component spectrum obtained in the pollution source emission experiment, and the pollution source represented by the factor can be finally identified.

[0097]

[0098] Where MSA and MSB are the component proportions of the two component spectra used for comparison, and θ is the similarity angle between the two.

[0099] Finally, data analysis was performed based on the VOCs flux source analysis results obtained under the optimal number of factors. These results include the VOCs flux time series of all factors under the optimal number of factors (a series of VOCs flux data points arranged in chronological order, reflecting the changes in VOCs flux at different time points) and the source component spectra corresponding to all factors. The analysis of PMF results includes analysis steps such as the proportion of factor time changes, factor contribution proportion, factor daily change proportion, and factor-to-point species proportion.

[0100] The specific steps are as follows: First, based on the flux time series of all factors, the VOCs flux time series of each factor is divided by the total VOCs flux time series of all factors to obtain the VOCs flux proportion time series of each factor. This step can be used to understand the changes in the proportion of VOCs sources in different periods and infer the possible reasons for these changes.

[0101] In the second step, based on the flux time series of all factors, all data points of the VOCs flux time series of each factor are added together to obtain the total flux of each factor. Then, the data points of the VOCs flux time series of all factors are added together to obtain the total flux of all factors. The total flux of each factor is divided by the total flux of all factors to obtain the contribution ratio of different factors, so as to understand the relative importance of different VOCs flux sources.

[0102] In the third step, based on the time change ratio of the factors obtained in the first step and the corresponding time data, the number of hours corresponding to each time period is calculated, and the same number of hours is averaged to obtain the average flux value of different hours. By analyzing the daily change characteristics of the flux, the difference between day and night can be compared, and its correlation with environmental factors such as temperature and humidity can be analyzed.

[0103] The fourth step is to combine the factor contribution ratio obtained in the second step with the source component spectrum corresponding to all factors to obtain the relative contribution of each factor in the component spectrum to a single species. By analyzing the contribution ratio of different factors to a single species, more information can be provided for the emission source tracing research of typical VOCs species.

[0104] This embodiment is based on VOCs flux data, and uses an orthogonal matrix factorization model (PMF) for source analysis, including but not limited to VOCs flux data screening methods, VOCs flux uncertainty calculation methods, VOCs flux matrix and VOCs flux error matrix calculation methods, determining the best PMF model output results and in-depth analysis of the results, and subsequently adding the required methods and parameter calculations according to the needs of related research or experiments. This embodiment proposes a VOCs source analysis method based on VOCs flux data, and uses the PMF model to identify the main VOCs emission sources in a certain area. Since this method can directly analyze the source from the emission of the substance, it solves the impact of secondary generation and meteorological factors on the analysis in the source analysis method based on VOCs concentration, so that the primary source of VOCs can be directly identified, and the specific contribution of different emission sources to various VOCs species can be quantified. Compared with the traditional method of VOCs source analysis based on concentration measurement, the uncertainty of the VOCs source analysis method is effectively reduced, and the source characteristics of VOCs can be better analyzed and quantified, filling the technical gap of existing source analysis based on flux data. In the future, it may be promoted and applied in the analysis of VOCs sources in different regions, bringing new insights into the prevention and control of air pollutants.

[0105] Based on the same inventive concept, the embodiment of the present application also provides a device for implementing the VOCs source analysis method based on flux data mentioned above. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above method.

[0106] In an exemplary embodiment, a VOCs source analysis device based on flux data is provided, including: a VOCs flux quality control inspection module, a VOCs flux species determination module, a PMF flux matrix and error matrix generation module, a PMF source analysis module, a PMF factor number determination and factor identification module and a PMF result analysis module.

[0107] The VOCs flux quality control inspection module is designed to ensure the quality of VOCs flux data. It uses a data quality marking method to screen the data. This module is applied to the flux data quality control inspection step of the VOCs flux species determination module.

[0108] The VOCs flux species determination module obtains flux species data that can be ultimately applied to PMF analysis through the data screening method designed in this application. The screening mechanism includes: mass spectrum peak high-resolution fitting, concentration quantification, screening based on concentration detection limit, flux calculation, flux data quality control detection, screening based on flux detection limit, and positive flux evaluation.

[0109] The PMF flux matrix and error matrix generation module includes the generation of VOCs flux matrix and VOCs flux error matrix.

[0110] The PMF source analysis module inputs the flux matrix, flux error matrix, species name, mass-to-charge ratio, and time axis into the PMF EvaluationTool 3.05 program package based on IgorPro software for source analysis.

[0111] The PMF factor number determination and factor identification module refers to determining the number and names of VOCs flux sources based on the above-mentioned PMF model results, mainly including determining the number of factors based on Q / Qexp, comparing factor component spectra, comparing tracer species with daily changes in factors, and comparing factor component spectra with measured source spectra.

[0112] The PMF result analysis module refers to the data analysis of the VOCs flux source analysis results under the optimal number of factors obtained by the above-mentioned PMF source analysis module and the PMF factor number determination and factor identification module. These results include the VOCs flux time series of all factors under the optimal number of factors (referring to a series of VOCs flux data points arranged in chronological order, reflecting the changes in VOCs flux at different time points) and the source component spectra corresponding to all factors. The analysis of PMF results includes analysis steps such as the proportion of factor time changes, factor contribution proportion, factor daily change proportion, and factor proportion of point species.

[0113] In another exemplary embodiment, Figure 2 As shown, a VOCs source analysis method based on flux data is provided, which can identify the main VOCs emission sources in different regions, filling the gap in source analysis technology based on VOCs flux data. The source analysis method includes: flux data quality control inspection, flux species determination, PMF flux error matrix determination, PMF source analysis, PMF factor number determination and factor identification, and PMF result analysis.

[0114] The VOCs flux quality control test module is to ensure the quality of VOCs flux data. It uses the data quality marking method to screen the data. This module is applied to the flux data quality control test step of the VOCs flux species determination module. The quality marking method for flux data includes steady-state test and complete turbulence feature test. The specific operation steps are as follows:

[0115] In the first step, the measured VOCs concentration data and environmental data (wind speed data measured by a three-dimensional ultrasonic anemometer) are combined to calculate the steady-state condition parameters and the turbulence development characteristic parameters to obtain the parameter results and obtain the data mark value of the parameter based on the data mark classification range (Table 1). The calculation method of the steady-state condition parameters and the turbulence development condition parameters is shown in Formula 1-2.

[0116] In the second step, the highest number of data marks in the steady-state test and the complete turbulence characteristic test is selected as the total mark (for example, the steady-state test is 3 and the turbulence development characteristic test is 5, then the total mark is 5).

[0117] In the third step, the data marked as 1-6 are used for data analysis, while the data marked as 7-10 are removed.

[0118] In the third step, the data marked as 1-6 were used for data analysis, while the data marked as 7-10 were eliminated.

[0119]

[0120] In the formula, x s Represents the horizontal wind component or scalar, RN x The steady-state condition parameters of the representative species in the average interval (30 minutes). The calculation formula of the turbulence development condition parameters is as follows:

[0121]

[0122] In the formula, ITC σ is the characteristic parameter of turbulence development, and They represent the wind steady-state parameters obtained from the theoretical model and the measured wind steady-state parameters under neutral conditions, respectively.

[0123] The VOCs flux species determination module obtains flux species data that can be finally applied to PMF analysis through the data screening method designed by the present invention. The screening mechanism includes: mass spectrum peak high-resolution fitting, concentration quantification, screening based on concentration detection limit, flux calculation, flux data quality control detection, screening based on flux detection limit and positive flux evaluation. The specific operation steps are as follows:

[0124] In the first step, based on the raw data measured by PTR-ToF-MS (proton reaction time-of-flight mass spectrometry), the measured mass spectrum peaks were fitted with high resolution using mass spectrometry instrument data analysis software to obtain the species names of 1,778 VOCs measured during mass spectrometry measurements, the corresponding mass-to-charge ratios, and species signal data.

[0125] In the second step, based on the VOCs species signal data obtained in the previous step and combined with the species response factor obtained through calibration experiments with chemical standards, the species signal is converted into concentration. The calculation formula is shown below.

[0126]

[0127] In the formula, [X] represents the species concentration, Signal represents the species signal, and Sensitivity represents the species response factor. The species concentration detection limit is then calculated using the formula shown below.

[0128]

[0129] Where DL is the concentration detection limit of VOCs species; Cf and B are the average signal and background signal of the species during mass spectrometry measurement; t is the time resolution of VOCs flux data during mass spectrometry measurement (i.e., the measurement interval required to obtain a flux data, generally 30 minutes); SNR is the signal-to-noise ratio of VOCs measurement, and its value is set to 3 when calculating the detection limit.

[0130] In the third step, VOCs species with concentrations above the detection limit were screened out by calculating the concentration detection limit. VOCs species with average concentrations below the detection limit would not be included in subsequent analysis. A total of 766 species with concentrations above the detection limit were screened out.

[0131] The fourth step is to calculate the VOCs flux based on the VOCs concentration data after screening in the third step and the wind speed data measured by the three-dimensional ultrasonic anemometer (Formula 5).

[0132] The fifth step is to use the above-mentioned VOCs flux quality control inspection module to obtain the data marking data of each VOCs species, and remove the flux data points that do not meet the flux measurement conditions (data marking value is greater than 6).

[0133] In the sixth step, the species flux detection limit was calculated based on the VOCs concentration data and the three-dimensional wind speed data (Formula 6), and the species with an average VOCs flux value lower than their flux detection limit were eliminated. The remaining 199 VOCs species that met the flux detection limit entered the next step of analysis.

[0134] Step 7: Based on the VOCs flux data obtained in the above screening step, the positive value ratio of the effective flux data (non-null value data points that have passed the data quality control test) in each VOCs species is calculated, and 193 species with a positive value ratio greater than or equal to 70% are selected as the species that can be finally used to input the PMF model. The specific process is as follows: Figure 3 shown.

[0135]

[0136] In the formula, w' and c' represent the change of vertical wind speed and the change of certain VOCs concentration respectively.

[0137] LOD flux =3*RE noise( 6)

[0138] Where LOD flux and RE noise They are the flux detection limit and random error of VOCs flux measurement respectively.

[0139] The PMF flux matrix and error matrix generation module includes the generation of VOCs flux matrix and VOCs flux error matrix. The specific operation steps are as follows:

[0140] In the first step, all VOCs species flux data that can be used for PMF analysis screened by the above VOCs flux species determination module are synthesized into a VOCs flux matrix. If there are i VOCs species and each species has j data points, a matrix with i columns and j rows is generated. In the matrix, for flux data points that are less than 0 or null values, the median flux of the species is used to replace them.

[0141] In the second step, the error UPTR,ij of PTR-ToF-MS measurement of VOCs concentration, the systematic error of flux measurement, and the random error REnoise of flux measurement are calculated for all VOCs species involved in the first step.

[0142] The method for calculating VOCs concentration error is shown in the following formula.

[0143]

[0144] Where UPTR,ij is the error in the concentration of species j measured by PTR-ToF-MS in time period i. SNRij is the signal-to-noise ratio of species j in time period i, which is used to evaluate the counting statistical error in the instrument measurement process and is calculated by the following formula. xij is the average concentration of j in time period i (30min). pij is the error rate of PTR-ToF-MS during the measurement process, and the value is set to 20%. Among them, 10% of the error comes from fluctuations in instrument operation, such as ion source temperature fluctuations, parent ion signal fluctuations, etc. The other 10% comes from the error caused by calibration with standard gases during long-term observations. DL is the instrument detection limit of species j, and the calculation method is as follows.

[0145]

[0146] Where SNRij is the signal-to-noise ratio of species j in the i-th time period; Cf,j and B are the average and background signals of species j during mass spectrometry measurement; t is the time interval (30 min) for VOC flux calculation used in PMF. Since the systematic error of flux measurement can be estimated based on the flux averaging interval, it is considered negligible after evaluation.

[0147] The third step is to calculate the VOCs flux error. The VOCs flux error is obtained by adding the error of VOCs concentration and the random error of flux measurement. However, the error matrix of VOCs concentration is not fully transferred to VOCs flux measurement. According to the evaluation, about half of the VOCs concentration error is transferred to the VOCs flux measurement error. Therefore, the error calculation of VOCs flux is shown in the following formula.

[0148]

[0149] Where U total,ij is the error in the flux of species j measured by PTR-ToF-MS in time period i, RE noise,ij is the random error in measuring the flux of species j in time period i, U PTR,ij is the error in the PTR-ToF-MS measurement of species j concentration at time period i.

[0150] Step 4: Synthesize the VOCs flux error matrix. Based on the VOCs flux error data calculated in the third step, for flux data points with concentrations less than 0 or null values, the corresponding error data is set to three times the median of the corresponding species flux. The flux error data of all the above-calculated VOCs species are synthesized into a VOCs flux error matrix. If there are i VOCs species and each species has j data points, a matrix with i columns and j rows is generated. The flux matrix and the flux error matrix should have the same number of rows and columns.

[0151] The PMF source analysis module inputs the flux matrix, flux error matrix, species name, mass-to-charge ratio, and time axis into the PMF Evaluation Tool 3.05 program package based on Igor Pro software for source analysis. The specific steps are as follows:

[0152] In the first step, the flux matrix and flux error matrix generated by the PMF flux matrix and error matrix generation module, as well as the VOCs species name, mass-to-charge ratio and mass spectrum measurement time axis obtained in the mass spectrum peak high-resolution fitting step of the VOCs flux species determination module are imported into the PMF software. First, the flux matrix needs to be removed from the spike outliers that exceed 2 consecutive points. Secondly, the error matrix from which the spike outliers are removed needs to be removed from the spike outliers that exceed 2 consecutive points. Finally, the empty values ​​and rows containing 0 in the matrix are removed to ensure that the input data meets the model requirements.

[0153] In the second step, the species flux signal-to-noise ratio (S / N) was calculated to assess the data quality, as shown in the following formula:

[0154]

[0155] In the formula, Flux signal-to-noise ratio of representative species, C f represents the species response factor, [X] represents the species concentration, t represents the measurement period, and B represents the background signal. The S / N results are divided into three categories: less than 0.2, between 0.2-2, and greater than 2. The flux data of the two categories of less than 0.2 and between 0.2-2 are reduced in weight to obtain flux data with further quality assurance.

[0156] The third step is to set the parameters of the PMF model. Generally, the number of factors is set between 1 and 10 to find the optimal solution. The Fpeak (parameter for adjusting the rotational freedom of the factor) value in the PMF model is set to a strength range of -3 to 3 with an interval of 0.2. The storage path is selected and the source is parsed. The PMF model results obtained include the following:

[0157] 1. The number of factors (which can be understood as the number of analyzed pollution sources) ranging from 1 to 10 corresponds to the Q (residual sum of squares) / Qexp (expected residual sum of squares).

[0158] 2. Under specified factor conditions (such as 1 to 10), the flux time series of all factors (referring to a series of VOCs flux data points arranged in chronological order, reflecting the changes in VOCs flux at different time points).

[0159] 3. Under the condition of a specified number of factors (such as 1 to 10), the source component spectrum corresponding to all factors, where the component spectrum shows the composition distribution of different VOCs components in each factor.

[0160] Based on the result analysis of the PMF model, the number of factors was determined according to Q / Qexp, the comparison of factor component spectra, the comparison of tracer species and factor daily changes, and the comparison of factor component spectra with measured source spectra. The final number of factors was determined to be 5, namely Source1, Source2, Source3, Source4 and Source5. The Q value calculation formula is as follows:

[0161]

[0162] Taking the sample data set as an example, the PMF result analysis is not limited to the above content. Figure 4 As shown in the figure, Source 1 occupies a major position and its contribution is stable, especially during the period from May 29 to June 7, indicating that VOCs emissions during this period were greatly affected by Source 1. Source 2 and Source 4 fluctuated greatly during the entire mass spectrometry measurement period, and it is speculated that there is a great deal of uncertainty in the related emissions of Source 2 and Source 4. As can be seen from the factor proportion diagram, the factor contribution is mainly Source 1 (33%), and the emissions of Source 2 (22%) are also not to be ignored. Figure 5 Shown are the average contributions of factors to typical species, where Figure 5 (a) is isoprene, Figure 5 (b) is toluene, Figure 5 (c) is C8 aromatic hydrocarbon, Figure 5 (d) is acetone, Figure 5 (e) is ethanol. There are differences in the sources of typical aromatic hydrocarbons. Toluene mainly comes from Source 5 (30%), while C8 aromatics mainly come from Source 4 (38%). The five factors contribute to acetone in a relatively balanced manner, while ethanol mainly comes from the related emissions of Source 1 (38%) and Source 4 (37%). Analysis based on the flux-based PMF results can bring new insights into the local VOCs sources, which is of great significance for the subsequent VOCs prevention and control work.

[0163] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a VOCs source analysis method based on flux data is implemented.

[0164] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0165] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0166] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0167] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0168] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0169] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0170] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0171] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0172] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A VOCs source analysis method based on flux data, characterized in that: The VOCs source analysis method based on flux data includes: Obtain wind speed data, mass spectrum peak data of the gas to be tested, and time axis data during mass spectrum peak measurement; Determine VOCs species data in the gas to be tested according to the mass spectrum peak data; the VOCs species data includes: VOCs species name data, corresponding species concentration data and corresponding mass-to-charge ratio data; Calculate the VOCs flux data of each species according to the wind speed data, the VOCs species name data and the corresponding species concentration data; For each species: Calculating a VOCs flux error matrix according to the VOCs flux data; the VOCs flux error matrix includes: a measurement error matrix, a system error matrix and a random error matrix; Converting the VOCs flux data into a VOCs flux matrix; Inputting the VOCs flux matrix, the VOCs flux error matrix, the VOCs species name data, the mass-to-charge ratio data and the time axis data into an orthogonal matrix factor decomposition model to obtain the number of factors, the source component spectrum of the factors and the flux time series of the factors; Determine the pollution source represented by the factor according to the source component spectrum of the factor; The specific emission source of the factor is determined based on the flux time series of the factor.

2. The VOCs source analysis method based on flux data according to claim 1, characterized in that: Determine VOCs species data in the gas to be tested according to the mass spectrum peak data; The VOCs species data includes: VOCs species name data, corresponding species concentration data and corresponding mass-to-charge ratio data, specifically including: The mass spectrometer data is subjected to high-resolution fitting using mass spectrometer data analysis software to obtain VOCs species name data, corresponding mass-to-charge ratio data, and corresponding species signal data; Calculate species concentration data based on the species signal data; the calculation formula for the species concentration data is: Where [X] represents species concentration, Signal represents species signal, and Sensitivity represents species response factor.

3. The VOCs source analysis method based on flux data according to claim 1, characterized in that: The VOCs flux data of each species is calculated according to the wind speed data, the VOCs species name data and the corresponding species concentration data, specifically including: Delete the species data whose concentration data is lower than the concentration detection limit; the calculation formula of the concentration detection limit is: Where DL is the concentration detection limit of the species; C f and B are the average signal and background signal of the species during mass spectrometry measurement; t is the time resolution of VOCs flux data during mass spectrometry measurement, and SNR is the signal-to-noise ratio of VOCs measurement; The VOCs flux data of each species is calculated according to the wind speed data, the VOCs species name data and the corresponding species concentration data.

4. The VOCs source analysis method based on flux data according to claim 1, characterized in that: Calculating a VOCs flux error matrix according to the VOCs flux data specifically includes: Deleting VOCs flux data that is below the flux detection limit; wherein the flux detection limit is calculated using the species concentration data and the wind speed data; A VOCs flux error matrix is ​​calculated based on the VOCs flux data.

5. The VOCs source analysis method based on flux data according to claim 1, characterized in that: The specific emission source of the factor is determined according to the flux time series of the factor, including: Calculating the daily variation flux average of the factor according to the flux time series of the factor; The daily flux average is matched with the daily flux average of the tracer species in the city to obtain the emission source.

6. The VOCs source analysis method based on flux data according to claim 1, characterized in that: The VOCs flux data of each species is calculated according to the wind speed data, the VOCs species name data and the corresponding species concentration data, specifically including: According to the species concentration data and the wind speed data, the VOCs species data are marked for data quality using a steady-state test and a complete turbulence characteristic test method to obtain a data quality marking result; Deleting VOCs species data corresponding to the quality labeling result being higher than a preset threshold; The VOCs flux data of each species is calculated according to the wind speed data, the VOCs species name data and the corresponding species concentration data.

7. The VOCs source analysis method based on flux data according to claim 1, characterized in that: The calculation formula of the VOCs flux data is: Among them, w' and c' represent the change of wind speed and VOCs concentration, respectively.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the VOCs source analysis method based on flux data as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the VOCs source analysis method based on flux data described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the VOCs source analysis method based on flux data described in any one of claims 1 to 7 is implemented.

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