VOCs source analysis method based on photochemical loss correction and uncertainty optimization

Abstract

The invention discloses a VOCs source analysis method based on photochemical loss correction and uncertainty optimization, and the method comprises the steps: building a sensitive species data matrix through the monitoring data of urban atmosphere VOCs, selecting proper species, and carrying out the photochemical loss correction to obtain an initial concentration matrix; constructing an uncertainty algorithm group and carrying out initial assignment to obtain an uncertainty data matrix; respectively importing the initial concentration matrix and the uncertainty data matrix into a PMF model for operation, and optimizing the uncertainty matrix according to the accuracy of a source analysis result and the concentration degree of a tracer; and finally, combining the initial concentration matrix with the optimized uncertainty matrix, and operating the PMF model to obtain a high-credibility source analysis result. According to the method, uncertainty parameters are optimized through dynamic iteration, preposed photochemical loss correction is combined, the accuracy, reliability and applicability of a source analysis result are remarkably improved, and technical support is provided for urban VOCs source analysis and air pollution prevention and control.

Description

Technical Field

[0001] This invention relates to the research and application of source apportionment of VOCs pollutants in ambient air, specifically to a VOCs source apportionment method based on photochemical loss correction and uncertainty optimization. Background Technology

[0002] With the promulgation and implementation of air pollution prevention and control policies, my country's overall air quality has improved, and PM2.5 levels have decreased. 2.5 PM 10 While the annual average concentrations of pollutants such as SO2, NO2, and CO have generally shown a downward trend, the number and proportion of days exceeding ozone standards have increased rather than decreased, indicating that O3 pollution is gradually becoming the main pollutant affecting regional air quality in my country. The analysis of the causes of ozone pollution has become a current research hotspot, often closely linked to source apportionment of volatile organic compounds (VOCs).

[0003] Currently, VOCs source apportionment methods mainly fall into five categories: emission source inventory methods, numerical simulation methods, linear and nonlinear parameterized regression methods, and receptor models. Commonly used receptor models include Chemical Mass Balance (CMB) and Positive Matrix Factorization (PMF). Compared to the CMB model, PMF estimates the composition of pollution sources and their contribution to environmental concentrations based on extensive observational data from receptor sites. It does not rely on dynamically updated pollution source compositional data and can account for uncertainties in monitoring data, making it convenient and quick to operate. In recent years, the PMF receptor model, as a relatively mature and convenient source apportionment tool, has been widely used in VOCs source apportionment, such as in cities like Beijing, Shanghai, Wuhan in my country, and Houston in the United States. However, the PMF model suffers from problems such as photochemical losses of gaseous pollutants and uncertainty bias.

[0004] First, existing PMF models fail to reduce the errors caused by photochemical reactions and lack techniques for correcting for photochemical losses in measured VOC concentrations at receptor sites. VOCs undergo varying degrees of photochemical loss during transport from source to receptor; the more reactive the species, the greater the photochemical loss. Therefore, there is a certain deviation between the monitored VOC concentrations at receptor sites and the original emission concentrations from the pollution sources. One of the fundamental assumptions of PMF models is that pollution sources remain unchanged during atmospheric transport, which contradicts the photochemical nature of VOCs. Therefore, directly applying source apportionment to measured VOC concentrations at receptor sites cannot accurately reflect the actual VOC emissions from urban sources.

[0005] Secondly, when using the PMF method for source apportionment, the uncertainty of the monitoring data is an extremely important input file, playing a crucial role in the calculation and analysis results. VOCs monitoring involves numerous components with significantly different concentrations; applying the same error fraction to all VOC species cannot reflect the true uncertainty of the data.

[0006] Finally, many improved PMF model source apportionment methods and systems have proposed algorithms such as goodness-of-fit analysis, bootstrap (BS) method, and substitution method (DISP) to optimize the uncertainty interval, which helps improve the reliability of PMF source apportionment results. However, these algorithms are often manually optimized based on multiple iterative simulations of the PMF model, and the timing of terminating the iterative calculation relies heavily on human experience. Some studies have also investigated the differences in VOC emission source spectra before and after photochemical loss correction, showing that the source contribution factor ranking after photochemical loss correction is closer to reality.

[0007] In summary, it is essential to perform photochemical loss correction and optimize the uncertainty of monitoring data for VOCs at receptor sites to improve the accuracy of source apportionment. Summary of the Invention

[0008] The purpose of this invention is to provide a VOCs source apportionment method based on photochemical loss correction and uncertainty optimization. By combining photochemical loss correction with a dynamic optimization mechanism for uncertainty parameters based on model analysis results, the accuracy, reliability and adaptability of the PMF model in VOCs source apportionment are improved.

[0009] To achieve the above objectives, the technical solution adopted by the present invention is as follows: The VOCs source apportionment method based on photochemical loss correction and uncertainty optimization includes the following steps: Step S1: Obtain atmospheric volatile organic compound (VOCs) concentration monitoring data at monitoring points and construct a VOCs monitoring concentration data matrix; Step S2: Based on the parameterization method of photochemical age, the VOCs monitoring concentration data is corrected for photochemical loss, the initial concentration of each VOCs species is calculated, and the initial concentration data matrix is ​​constructed. Step S3: Construct an uncertainty algorithm group containing at least three different calculation logics, and calculate and construct multiple initial uncertainty data matrices corresponding to the initial concentration data matrix based on VOCs concentration monitoring data and initial concentration data; Step S4: Import the initial concentration data matrix and each initial uncertainty data matrix into the positive definite matrix factorization (PMF) model for calculation to obtain multiple sets of preliminary source apportionment results; evaluate the concentration of tracer substances contained in each pollutant in the pollutant characteristic spectrum corresponding to each set of preliminary source apportionment results; Step S5: Based on the concentration assessment results of the tracer substance, select the preferred uncertainty values ​​corresponding to each pollutant from the multiple initial uncertainty data matrices, and iteratively assign values ​​to the uncertainty data matrix to obtain the final optimized uncertainty data matrix; Step S6: Import the initial concentration data matrix and the final optimized uncertainty data matrix into the PMF model for calculation to obtain the optimized VOCs source apportionment results.

[0010] Furthermore, in step S1, the step of constructing the VOCs monitoring concentration data matrix includes: Construct a basic monitoring concentration data matrix; Based on the aforementioned basic monitoring concentration data matrix, a pollutant information data matrix containing the photochemical reaction rate constants of OH free radicals for each VOC species is constructed; Based on the emission characteristics of the target area, key characteristic species were screened and a VOCs sensitive species data matrix was constructed.

[0011] Furthermore, in step S2, the photochemical loss correction process is as follows: selecting a pair of VOC species that are emitted from the same source and whose reaction rate constant with hydroxyl radicals differs from a preset threshold as a species characteristic pair; selecting the concentration ratio of the species characteristic pair within a specific time period as the initial emission ratio; calculating the hydroxyl exposure of each pollutant, and thus obtaining the initial concentration of each pollutant.

[0012] Furthermore, the formula for calculating the hydroxyl exposure of each pollutant is as follows: The formulas for calculating the initial concentration of each pollutant are as follows: In the formula, [·OH] is the volume fraction of ·OH, Δt is the photochemical age, and the product of [·OH] and Δt is the atmospheric OH exposure; B and C are a pair of VOC species that have the same emission source and whose rate constants for reacting with hydroxyl radicals differ from the preset threshold. and are the reaction rate constants of species B and species C with ·OH, respectively; Let be the reaction rate constant between the i-th VOC species and ·OH; , The initial concentrations of C and B are respectively. , Let C and B be the concentrations at time t, respectively. and They are respectively The monitored concentration and initial concentration.

[0013] Furthermore, the uncertainty data matrix is ​​a data matrix containing at least one parameter, including the concentration detection limit, uncertainty proportionality coefficient, standard deviation, error fraction, and uncertainty component introduced by standard curve fitting.

[0014] Furthermore, in step S4, the method for assessing the concentration of tracer substances is as follows: for the same pollutant, compare and analyze the distribution of the concentration contribution ratio of key tracer species in multiple feature maps obtained from data matrices with different initial uncertainties. The higher the concentration, the clearer the correspondence between the factor and a specific pollution source.

[0015] Furthermore, in step S5, the process of cyclically assigning values ​​to the uncertainty data matrix includes: After initially selecting the preferred uncertainty values, the newly integrated uncertainty data matrix is ​​used as input to run the PMF model again and evaluate the results. If the concentration of tracer substances for some pollutants does not reach the preset improvement target, the uncertainty value will be optimized again for these pollutants. Repeat this process until the concentration of tracer substances for each pollutant meets the requirements or the number of iterations reaches the upper limit, to obtain the final optimized uncertainty data matrix.

[0016] Furthermore, in step S3, the uncertainty calculation formula in the uncertainty algorithm group includes: (Conc>MDL) (Conc≤MDL) In the formula, For the uncertainty of the receptor sample, For error fractions, For species concentration, The method detection limit, Here, SD is the uncertainty proportionality coefficient, and SD is the standard deviation. This refers to the uncertainty component introduced by the standard curve fitting of the measured sample. For other uncertainty components, Represents the number of uncertainty components. This represents the number of inclusion factors.

[0017] Furthermore, in step S6, the accuracy of the source resolution result is determined by the objective function. To determine: ≤N In the formula, For the first The first sample The difference between the measured concentration of a VOC component and the concentration of that component as determined by the model; For the first The first sample Measurement uncertainty of VOCs components Indicates the number of samples. This indicates the number of VOC components, where N is the total number of VOC monitoring samples.

[0018] Furthermore, the tracer material includes characteristic VOC species or combinations thereof used to identify stationary combustion sources, motor vehicle emission sources, industrial solvent use sources, biological sources, residential sources, or petrochemical sources.

[0019] The beneficial effects of the above scheme are as follows: 1. This invention significantly improves the accuracy and reliability of source apportionment results by integrating photochemical loss correction and dynamic uncertainty optimization. Photochemical loss correction back-calculates the concentration monitored at the receptor point to the initial state of pollutant emissions, effectively eliminating source contribution distortion caused by atmospheric chemical reactions. Simultaneously, the dynamic uncertainty optimization mechanism provides the model with a precisely calibrated data weighting system, enabling the calculation results to more reasonably reflect the true emission intensity of different pollution sources, thereby obtaining analytical conclusions that are closer to reality.

[0020] 2. This invention enhances the objectivity and automation of the source resolution process, reducing the reliance on human experience in traditional methods. It drives parameter optimization through objective standards such as algorithm group comparison and factor graph quality feedback, transforming the key parameter assignment process into a data-driven iterative flow. This adaptive mechanism can automatically approximate the optimal parameter combination based on different datasets, improving the method's universality and intelligence.

[0021] 3. This invention improves the robustness and reliability of the overall source apportionment scheme. The uncertainty matrix obtained through multiple rounds of iterative optimization enables the factor spectrum to reach a more stable tracer concentration state. Models running based on this exhibit reduced sensitivity to initial conditions in their analytical results, and the output factor contributions and component spectra show better reproducibility, thereby comprehensively enhancing the scientific validity of the model system and the credibility of the results.

[0022] 4. This invention enhances the technical support value for decision-making in air pollution prevention and control. The more accurate and objective source apportionment results obtained can help environmental management departments accurately identify key pollution sources, providing a reliable basis for formulating targeted emission reduction strategies. This helps optimize monitoring network design and improve the effectiveness of control measures, thus providing a solid technical foundation for the scientific prevention and control of ozone pollution and the continuous improvement of air quality. Attached Figure Description

[0023] Figure 1 This is an overall flowchart of an embodiment of the present invention; Figure 2 This is a comparison diagram of source feature maps under different uncertainty conditions in the embodiments of the present invention. Detailed Implementation

[0024] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0025] It should be noted that, unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0026] This invention provides a VOCs source apportionment method based on photochemical loss correction and uncertainty optimization. First, the method corrects the monitored concentration using a photochemical age parameterization method to restore the initial VOCs emission concentration. Second, it innovatively constructs an uncertainty algorithm group and uses the concentration of tracer substances in the pollutant characteristic spectrum of the preliminary analysis results from the PMF model as a feedback signal to dynamically and iteratively optimize the uncertainty parameters, ultimately obtaining highly reliable and accurate source apportionment results.

[0027] like Figure 1 As shown, the VOCs source apportionment method based on photochemical loss correction and uncertainty optimization includes the following steps: Step S1: Obtain atmospheric volatile organic compound (VOCs) concentration monitoring data at monitoring points and construct a VOCs monitoring concentration data matrix; Step S2: Based on the parameterization method of photochemical age, the VOCs monitoring concentration data is corrected for photochemical loss, the initial concentration of each VOCs species is calculated, and the initial concentration data matrix is ​​constructed. Step S3: Construct an uncertainty algorithm group containing at least three different calculation logics, and calculate and construct multiple initial uncertainty data matrices corresponding to the initial concentration data matrix based on VOCs concentration monitoring data and initial concentration data; Step S4: Import the initial concentration data matrix and each initial uncertainty data matrix into the positive definite matrix factorization (PMF) model for calculation to obtain multiple sets of preliminary source apportionment results; evaluate the concentration of tracer substances contained in each pollutant in the pollutant characteristic spectrum corresponding to each set of preliminary source apportionment results; Step S5: Based on the concentration assessment results of the tracer substance, select the preferred uncertainty values ​​corresponding to each pollutant from the multiple initial uncertainty data matrices, and iteratively assign values ​​to the uncertainty data matrix to obtain the final optimized uncertainty data matrix; Step S6: Import the initial concentration data matrix and the final optimized uncertainty data matrix into the PMF model for calculation to obtain the optimized VOCs source apportionment results.

[0028] Taking atmospheric VOCs monitoring in a petrochemical industrial city in western Henan as an example, this paper provides a detailed explanation of the implementation process of each step.

[0029] Step S1: Construct the data matrix.

[0030] The city's existing automatic monitoring stations for volatile organic compounds (VOCs) acquired hourly concentration data for 116 VOC components over a continuous period. The monitoring process was strictly carried out in accordance with the "Technical Requirements and Detection Methods for Continuous Gas Chromatography Monitoring Systems for Volatile Organic Compounds in Ambient Air" (HJ 1010-2018), and the raw data underwent quality control to remove outliers.

[0031] Constructing a basic monitoring concentration data matrix: The basic monitoring concentration data matrix P0 was obtained using hourly concentration data of 116 VOCs.

[0032] Based on the different types of pollutants, a pollutant information data matrix P1 was constructed using the photochemical reaction rate constants of OH radicals for different pollutants. According to the city's industrial characteristics, which are mainly petrochemical and mechanical processing, key species with indicative significance for local pollution sources were screened from all components, and a VOCs sensitive species data matrix P2 was constructed.

[0033] Step S2: Perform photochemical loss correction and construct the initial concentration matrix.

[0034] o-xylene and toluene, which share the same emission source and exhibit significant differences in their OH radical reaction rate constants, were selected as species characteristic pairs. The average concentration ratio of o-xylene to toluene within a relatively stable time period (e.g., from 20:00 the previous day to 05:00 the following day) was selected as the initial emission ratio. / .

[0035] Subsequently, the hydroxyl exposure levels of each pollutant were calculated using a photochemical age parameterization formula. The calculation formula is as follows: In the formula, [·OH] is the volume fraction of ·OH, Δt is the photochemical age, and the product of [·OH] and Δt is the amount of OH exposed in the atmosphere; and are the reaction rate constants of o-xylene and toluene with OH, respectively. / This represents the initial concentration ratio of the two. / Let t be the concentration ratio of the two as monitored at time t.

[0036] Finally, based on the calculated hydroxyl exposure and the reaction rate constant of each species... The initial concentration of each VOC species is calculated using the following formula: In the formula, To monitor concentration, This is the initial concentration.

[0037] Using the calculated initial concentrations, an initial concentration data matrix P3 is constructed, which serves as the concentration input for the subsequent PMF model.

[0038] Step S3: Construct the uncertainty algorithm group and the initial uncertainty data matrix.

[0039] An uncertainty algorithm group Ni is constructed, and initial values ​​are assigned to each uncertainty algorithm to obtain an uncertainty data matrix; where uncertainty is... Based on, but not limited to, the following calculation formula: Formula (1): Formula (2): Formula (3): (When Conc > MDL) Formula (4): (When Conc≤MDL) Formula (5): In the formula, For the uncertainty of the receptor sample, For error fractions, For species concentration, The method detection limit, Here, SD is the uncertainty proportionality coefficient, and SD is the standard deviation. This refers to the uncertainty component introduced by the standard curve fitting of the measured sample. For other uncertainty components, Represents the number of uncertainty components. This represents the number of inclusion factors.

[0040] In this embodiment, uncertainty matrices M1 and M2 are constructed based on formulas (3) and (4), where the error fraction EF of all pollutants in M1 is 10%, and the calculation formula for M2 is the same as that for M1, but the error fraction EF is the relative standard deviation of each VOC component calculated in daily quality control inspections. Uncertainty matrix M3 is constructed based on calculation formula (5), which takes into account the uncertainty introduced by standard curve fitting. Other main components .

[0041] Step S4: Basic operation of the PMF model and preliminary acquisition of results.

[0042] The initial concentration matrix P3 was paired with uncertainty matrices M1, M2, and M3, and then sequentially imported into the positive definite matrix factorization (PMF) model for basic calculations. Model operating parameters were set as follows: the range of the number of factors and the number of iterations; other parameters were set to the model's default optimal settings. After running the model, three sets of preliminary source apportionment results were obtained, yielding basic pollution source contribution rates and initially identifying pollution sources such as stationary combustion sources, vehicle emission sources, industrial solvent use sources, biological sources, residential sources, and petrochemical sources.

[0043] By analyzing the characteristic profiles of pollutants, the concentration of tracer substances and the accuracy of source apportionment results are determined. The assessment criterion for concentration is: whether the concentration contribution of key tracer species in the factor profile is highly concentrated on that factor; the higher the concentration, the clearer the correspondence between the factor and a specific pollution source.

[0044] The concentration of pollutants refers to ethylene, ethane, n-decane, dodecane, undecane, toluene, and 2,3-dimethylbutane, which mainly originate from stationary combustion sources such as power generation and heating. n-Octane, undecane, dodecane, ethylene, and toluene are important tracers in the exhaust emissions of gasoline and diesel vehicles, representing sources of emissions from motor vehicles. n-Decanane, undecane, toluene, ethylbenzene, xylene, isopropylbenzene, and other C9 benzene series compounds, as well as some short-chain alkanes and alkenes, represent sources of industrial solvent use. Isoprene, trans-2-butene, and some short-chain alkanes and alkynes are primarily biogenic (mainly emitted by plants). Ethylene, ethane, benzene, toluene, and 2,3-dimethylpentane are sources from residential use. Trans-2-butene, n-octane, n-hexane, and xylene are sources from petrochemical processes.

[0045] Step S5: Selection and iterative optimization of uncertainty values.

[0046] Based on the evaluation results of step S4, the iterative optimization process is initiated. First, the uncertainty matrix that performs best overall among the three sets of results is selected as the benchmark (e.g., M3). Then, it is checked whether there are cases in this benchmark result where the concentration of tracers for a certain pollutant is significantly inferior to that of the other matrix results.

[0047] For example, if the "industrial solvent source" factor spectrum in the M3 result is relatively scattered, while the spectrum of this factor in the M1 result has a higher concentration, then the uncertainty values ​​corresponding to the "industrial solvent source" characteristic species in the M1 matrix are replaced in the corresponding positions of the baseline matrix M3 to form a new, hybrid-optimized uncertainty matrix M(N)1.

[0048] Subsequently, the first iteration is performed: the concentration matrix P3 and M(N)1 are input into the PMF model again to evaluate the concentration of each factor in the new results. If it is found that some factors still do not meet the expected improvement target, the above-mentioned optimal replacement process is repeated (for example, introducing the corresponding better value from the M2 matrix) to generate M(N)2. This process is repeated until the concentration of the characteristic spectrum of all major pollutants reaches a set threshold, or the iteration reaches a preset number of times, to obtain the final optimized uncertainty data matrix M(N). In this embodiment, after multiple iterations, when the concentration of the worse factors in M3 increases by more than 20%, the final optimized uncertainty data matrix M(N) is obtained.

[0049] Step S6: Output the best source resolution result.

[0050] The initial concentration matrix P3 after photochemical loss correction and the final optimized uncertainty matrix M(N) are imported into the PMF model. After running, the best source apportionment result is output, which includes the time series contribution rate of each pollution source, the proportion of the total contribution rate, and the characteristic component spectrum.

[0051] The accuracy of the source resolution result is determined by the objective function Q(E), and the formula is: ≤N In the formula, For the first The first sample The difference between the measured concentration of a VOC component and the concentration of that component as determined by the model; For the first The first sample Measurement uncertainty of VOCs components Indicates the number of samples. This indicates the number of VOC components, where N is the total number of VOC monitoring samples.

[0052] The analysis results show that the contribution of pollutant emissions to VOCs in this region is as follows: petrochemical and refining sources (37.4%) > mobile sources (14.9%) > gas and liquefied petroleum gas usage sources (14.3%) > combustion sources (12.7%) > solvent evaporation sources (8.5%) > chemical synthesis sources (6.7%) > oil storage, transportation and sales sources (5.5%). Petrochemical and refining sources and motor vehicle exhaust (mobile sources) are important sources of ozone formation in this city. Compared with the conclusion of the main contributing sources before optimization, the conclusion of gas and liquefied petroleum gas usage sources is more in line with the actual situation and has a better guiding role in the formulation of urban VOCs prevention and control and environmental ozone control strategies.

[0053] Finally, it should be noted that any parts of this invention not described in detail are prior art. Those skilled in the art will understand that the above descriptions are merely preferred embodiments of the invention and are not intended to limit the invention. Although the invention has been described in detail with reference to the foregoing examples, those skilled in the art can still modify the technical solutions described in the foregoing examples or make equivalent substitutions for some of the technical features. All modifications and equivalent substitutions made within the spirit and principles of the invention should be included within the scope of protection of the invention.

Claims

1. A VOCs source apportionment method based on photochemical loss correction and uncertainty optimization, characterized in that, Includes the following steps: Step S1: Obtain atmospheric volatile organic compound (VOCs) concentration monitoring data at monitoring points and construct a VOCs monitoring concentration data matrix; Step S2: Based on the parameterization method of photochemical age, the VOCs monitoring concentration data is corrected for photochemical loss, the initial concentration of each VOCs species is calculated, and the initial concentration data matrix is ​​constructed. Step S3: Construct an uncertainty algorithm group containing at least three different calculation logics, and calculate and construct multiple initial uncertainty data matrices corresponding to the initial concentration data matrix based on VOCs concentration monitoring data and initial concentration data; Step S4: Import the initial concentration data matrix and each initial uncertainty data matrix into the positive definite matrix factorization (PMF) model for calculation to obtain multiple sets of preliminary source apportionment results; evaluate the concentration of tracer substances contained in each pollutant in the pollutant characteristic spectrum corresponding to each set of preliminary source apportionment results; Step S5: Based on the concentration assessment results of the tracer substance, select the preferred uncertainty values ​​corresponding to each pollutant from the multiple initial uncertainty data matrices, and iteratively assign values ​​to the uncertainty data matrix to obtain the final optimized uncertainty data matrix; Step S6: Import the initial concentration data matrix and the final optimized uncertainty data matrix into the PMF model for calculation to obtain the optimized VOCs source apportionment results.

2. The VOCs source apportionment method based on photochemical loss correction and uncertainty optimization according to claim 1, characterized in that, In step S1, the step of constructing the VOCs monitoring concentration data matrix includes: Construct a basic monitoring concentration data matrix; Based on the aforementioned basic monitoring concentration data matrix, a pollutant information data matrix containing the photochemical reaction rate constants of OH free radicals for each VOC species is constructed; Based on the emission characteristics of the target area, key characteristic species were screened and a VOCs sensitive species data matrix was constructed.

3. The VOCs source apportionment method based on photochemical loss correction and uncertainty optimization according to claim 1, characterized in that, In step S2, the photochemical loss correction process is as follows: select a pair of VOC species that are emitted from the same source and whose reaction rate constant with hydroxyl radicals differs from a preset threshold as a species characteristic pair; select the concentration ratio of the species characteristic pair within a specific time period as the initial emission ratio; calculate the hydroxyl exposure of each pollutant, and then obtain the initial concentration of each pollutant.

4. The VOCs source apportionment method based on photochemical loss correction and uncertainty optimization according to claim 3, characterized in that, The formulas for calculating the hydroxyl exposure of each pollutant are as follows: The formulas for calculating the initial concentration of each pollutant are as follows: In the formula, [·OH] is the volume fraction of ·OH, Δt is the photochemical age, and the product of [·OH] and Δt is the atmospheric OH exposure; B and C are a pair of VOC species that have the same emission source and whose rate constants for reacting with hydroxyl radicals differ from the preset threshold. and are the reaction rate constants of species B and species C with ·OH, respectively; Let be the reaction rate constant between the i-th VOC species and ·OH; , The initial concentrations of C and B are respectively. , Let C and B be the concentrations at time t, respectively. and They are respectively The monitored concentration and initial concentration.

5. The VOCs source apportionment method based on photochemical loss correction and uncertainty optimization according to claim 1, characterized in that, In step S3, the uncertainty data matrix is ​​a data matrix containing at least one parameter, including the concentration detection limit, uncertainty proportionality coefficient, standard deviation, error fraction, and uncertainty component introduced by standard curve fitting.

6. The VOCs source apportionment method based on photochemical loss correction and uncertainty optimization according to claim 1, characterized in that, In step S4, the method for assessing the concentration of tracer substances is as follows: for the same pollutant, compare and analyze the distribution of the concentration contribution ratio of key tracer species in multiple feature maps obtained from data matrices with different initial uncertainties. The higher the concentration, the clearer the correspondence between the factor and a specific pollution source.

7. The VOCs source apportionment method based on photochemical loss correction and uncertainty optimization according to claim 1, characterized in that, Step S5, the process of cyclically assigning values ​​to the uncertainty data matrix, includes: After initially selecting the preferred uncertainty values, the newly integrated uncertainty data matrix is ​​used as input to run the PMF model again and evaluate the results. If the concentration of tracer substances for some pollutants does not reach the preset improvement target, the uncertainty value will be optimized again for these pollutants. Repeat this process until the concentration of tracer substances for each pollutant meets the requirements or the number of iterations reaches the upper limit, to obtain the final optimized uncertainty data matrix.

8. The VOCs source apportionment method based on photochemical loss correction and uncertainty optimization according to claim 1, characterized in that, In step S3, the uncertainty calculation formulas in the uncertainty algorithm group include: (Conc>MDL) (Conc≤MDL) In the formula, For the uncertainty of the receptor sample, For error fractions, For species concentration, The method detection limit, Here, SD is the uncertainty proportionality coefficient, and SD is the standard deviation. This refers to the uncertainty component introduced by the standard curve fitting of the measured sample. For other uncertainty components, Represents the number of uncertainty components. This represents the number of inclusion factors.

9. The VOCs source apportionment method based on photochemical loss correction and uncertainty optimization according to claim 1, characterized in that, In step S6, the accuracy of the source resolution result is determined by the objective function. To determine: ≤N In the formula, For the first The first sample The difference between the measured concentration of a VOC component and the concentration of that component as determined by the model; For the first The first sample Measurement uncertainty of VOCs components Indicates the number of samples. This indicates the number of VOC components, where N is the total number of VOC monitoring samples.

10. The VOCs source apportionment method based on photochemical loss correction and uncertainty optimization according to claim 1, characterized in that, The tracer material includes characteristic VOC species or combinations thereof used to identify stationary combustion sources, motor vehicle emission sources, industrial solvent use sources, biological sources, residential sources or petrochemical sources.

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