A method and system for quantitatively analyzing nitrate generation contribution of a VOCs emission source

Abstract

The application discloses a VOCs emission source nitrate generation contribution quantitative analysis method and system, relates to the technical field of atmospheric environment monitoring and pollution source analysis, and comprises the following steps: S1, obtaining continuous hour concentration data of each component of VOCs in the target area environment air, synchronously obtaining conventional pollutant concentration data and meteorological parameter observation data, and preprocessing, then according to the USEPA PMF model user guide, calculating the uncertainty and signal-to-noise ratio of each VOC component, and generating preprocessed VOCs concentration data; the application realizes the conversion of pollution source contribution information to photochemical simulation input data by establishing the data mapping relationship between the PMF source analysis result and the observation constraint photochemical model input parameter, unifies the traditional mutually independent pollution source analysis and photochemical sensitivity analysis into the same analysis framework, and realizes the effective coupling between the PMF receptor model and the observation constraint photochemical model.

Description

Technical Field

[0001] This invention relates to the field of atmospheric environmental monitoring and pollution source apportionment technology, specifically to a quantitative analysis method and system for the contribution of nitrate formation from VOCs emission sources. Background Technology

[0002] Volatile organic compounds (VOCs) are important precursors to atmospheric photochemical reactions, particularly nitrogen oxides (NOx). X Under the combined action of oxygen and solar radiation, oxygen and nitrates participate in the atmospheric oxidation process, generating ozone (O3) and nitrates (NO3). - Secondary pollutants such as particulate matter (PM2.5) are also present. In recent years, with the deepening of my country's air pollution control efforts, primary particulate matter emissions have been effectively controlled, but fine particulate matter (PM2.5) remains a significant concern. 2.5 Pollution problems remain prominent; secondary inorganic aerosols play a significant role in PM2.5 pollution. 2.5 The proportion of these pollutants is gradually increasing, especially nitrates, which have become a key component affecting regional air quality. Volatile organic compounds (VOCs), as important precursors to atmospheric photochemical reactions, react with nitrogen oxides (NOx)... X Under solar radiation, complex chemical reactions occur, which is one of the important pathways for the formation of nitrates.

[0003] Currently, source apportionment of atmospheric VOCs mainly employs two types of techniques: receptor models and source inventories. Firstly, the Positive Matrix Factorization (PMF) model utilizes pollutant concentration data obtained from environmental receptor point monitoring. Through matrix factorization, it effectively extracts pollution source factors (such as vehicle emissions, solvent use, and industrial processes) and obtains the contribution rate and pollutant composition characteristics of each source. Secondly, in the study of pollutant formation mechanisms, the Observation-Based Model (OBM) can simulate atmospheric photochemical reaction processes based on in-situ observation data and uses indicators such as Relative Incremental Reactivity (RIR) to evaluate different precursors (such as total VOCs, NOx, etc.). XThe sensitivity of emissions reduction (either for a single VOC species) to the formation of secondary pollutants such as ozone and nitrates is investigated. However, the output of the PMF model is essentially a statistical source contribution; it cannot describe how VOCs emitted from these emission sources participate in atmospheric photochemical reactions, nor can it directly quantify the dynamic impact of each emission source on the formation of secondary pollutants such as nitrates. Moreover, existing RIR analyses are limited to assessing chemical species, not actual pollution emission sources. Since the chemical composition of VOCs from each emission source is complex and unique, and there are synergistic reactions among the species within each source, RIR analyses based on a single species cannot reflect the comprehensive impact of a complete emission source, making it difficult for their results to directly guide emission reduction decisions for specific emission sources.

[0004] For example, application number CN202110553479.1 discloses an improved method and system for calculating the source intensity of reactive VOCs based on observational data. Utilizing OMI observational data, it takes INTEX-B anthropogenic emission sources, FINNv1 biomass combustion sources, and MEGAN biosources—all widely used in current air pollution research in my country—as examples, aiming to improve the source emission inventory of VOCs and enhance the accuracy and effectiveness of air pollution analysis. Application number CN201910418907.2 discloses a method for ozone source apportionment based on observational data and chemical mechanisms. Based on ozone precursor observational data, it establishes an atmospheric chemical reaction box model coupled with a PMF receptor model and an adaptive atmospheric chemical reaction mechanism, apportioning O3 sources through ozone precursor sources. Currently, to improve pollution source apportionment and pollution formation mechanism research, attempts have begun to use receptor model analysis results or photochemical models to analyze pollution sources or explain pollution formation, in order to optimize pollution control schemes and provide technical support for regional air quality management.

[0005] Existing technologies have the following shortcomings: First, PMF models cannot directly evaluate the secondary generation potential of pollution sources; second, the sensitivity analysis of OBM models remains at the level of chemical species and cannot be directed to specific pollution sources; third, there is a lack of an effective technical means to organically integrate the source apportionment results of PMF with the sensitivity analysis tools of OBM, making it difficult to establish a quantitative relationship from "pollution source emissions" to "nitrate formation response," and unable to accurately simulate the response characteristics of different pollution source emission reductions to nitrate formation during actual emission reduction processes. It is also difficult to ensure the consistency between simulation results and real emission control scenarios. As a result, it is currently impossible to scientifically assess the relative incremental reactivity of different VOCs emission sources to nitrate formation, and it is also impossible to provide a decision-making basis for prioritizing the targeted management of regional pollution sources. In other words, it is difficult to meet the needs of precise regional air pollution control and the formulation of targeted emission reduction decisions for pollution sources.

[0006] Therefore, there is an urgent need to propose a quantitative analytical method for the contribution of VOCs emission sources to nitrate formation that can achieve deep coupling between PMF source apportionment results and observation-constrained photochemical models. This method would enable the construction of a source-level sensitivity analysis system while maintaining the chemical composition characteristics of pollution sources, and quantitatively evaluate the impact of different VOCs emission sources on nitrate formation. This would provide reliable technical support for precise control of regional VOCs and collaborative governance of nitrate pollution.

[0007] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0008] The purpose of this invention is to provide a quantitative analysis method and system for the contribution of VOC emission sources to nitrate formation. By establishing methods for pollution source contribution reconstruction, source-level perturbation simulation, source apportionment relative incremental reactivity (SR-RIR) calculation, and pollution source control priority evaluation, this invention enables quantitative analysis of the impact of different VOC emission sources on nitrate formation. This addresses the problems in the prior art where existing VOC emission source apportionment methods can only identify pollution sources, observation-constrained photochemical model sensitivity analysis can only evaluate chemical species responses, and there is a lack of effective coupling between the two, making it difficult to directly and quantitatively assess the contribution of different VOC emission sources to nitrate formation.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a method for quantitatively analyzing the contribution of nitrate formation from VOCs emission sources, comprising the following steps: S1. Acquire continuous hourly concentration data of each VOC component in the ambient air of the target area, simultaneously acquire conventional pollutant concentration data and meteorological parameter observation data, perform preprocessing, and then calculate the uncertainty and signal-to-noise ratio of each VOC component according to the USEPA PMF model user guide to generate preprocessed VOC concentration data. S2. Input the preprocessed VOCs concentration data into the PMF model for source apportionment calculation, and extract the contribution matrix contained in the time series and several factors of the corresponding VOCs chemical composition matrix; identify the pollution source type for each factor and assign pollution source attributes to the identified factors; S3. Based on the pollution source contribution matrix and VOCs chemical composition matrix determined by the identification factors in the PMF model, reconstruct the VOC concentration corresponding to each pollution source, establish the correspondence between pollution sources and VOC chemical composition concentrations, and obtain the contribution concentration of each pollution source to different VOC components. S4. For each identified pollution source, construct its emission reduction simulation scenario and proportionally reduce the contribution of the target pollution source. S5. Based on the observation-constrained photochemical model, the VOCs component concentration data under the baseline scenario without emission reduction and all emission reduction scenarios are simulated to obtain the nitrate formation rate under the baseline scenario and under each pollution source emission reduction scenario. S6. Based on the baseline scenario and the nitrate formation rate under each pollution source emission reduction scenario, calculate the source apportionment relative incremental reaction activity (SR-RIR) for each pollution source. S7. Based on the calculated SR-RIR values ​​of each pollution source, sort them in descending order to establish a priority control level for pollution sources; and output each pollution source and its corresponding SR-RIR value and priority ranking result.

[0010] Furthermore, the concentration data of the conventional pollutants include NO, NO2, O3, CO, SO2, and PM2.5. 2.5 ; The meteorological parameter observation data include temperature, relative humidity, solar radiation, wind speed, and wind direction.

[0011] Furthermore, based on the USEPA PMF model user guide, the formula for calculating the uncertainty is:

[0012] In the formula, The limit of detection is expressed as the method detection limit for the corresponding component. This represents the uncertainty of the corresponding component. This is represented by the observed concentration values ​​of the corresponding components. It is expressed as the relative error coefficient of the corresponding component.

[0013] Furthermore, the source resolution formula of the PMF model is:

[0014] In the formula: Represented as the first The first sample Observed concentrations of various VOC components; Represented as the first Class II pollution sources on the first The contribution of each sample; Represented as the first The first type of pollution source The proportion of each VOC component; denoted as the residual between the observation matrix and the reconstruction matrix; p represents the number of pollution source categories; k represents the pollution source category index.

[0015] Furthermore, the relationship between the pollution source and the concentration of VOC chemical composition is established as follows:

[0016] In the formula, Represented as the first The first type of pollution source Concentration of various VOC components; Represented as the first Contribution from similar pollution sources; Represented as the first The first type of pollution source The composition ratio of the concentrations of various VOC components;

[0017] In the formula, The first in ambient air Total concentration of VOC components.

[0018] Furthermore, the formula for reducing the proportion of the target pollution source's contribution is as follows:

[0019] In the formula, Represented as the first after emission reduction simulation The first type of pollution source Concentration of various VOC components Indicates the percentage of pollution source emission reduction; Represented as the first The first type of pollution source The composition ratio of the concentration of each VOC component; Represented as the number after proportional reduction Contribution from pollution sources.

[0020] Furthermore, the formula for calculating the nitrate formation rate is as follows:

[0021] In the formula, This represents the rate of formation of gaseous nitric acid through the gas-phase reaction of OH radicals with NO2; The value represents the reaction rate of the gas-phase reaction between OH radicals and NO2; [OH] and [NO2] represent the concentrations of OH radicals and the real-time molar concentration of NO2, respectively. Expressed as the heterogeneous uptake coefficient of N2O5; This is expressed as the average velocity of N2O5 molecules; Expressed as aerosol surface area concentration; [N2O5] is the real-time molar concentration of N2O5; This represents the rate of N2O5 hydrolysis to form particulate nitrates. Expressed as the quantum efficiency of nitrate products in the hydrolysis of N2O5; Expressed as aerosol liquid phase activity / effective concentration parameter; Expressed as the Henry's constant for N2O5 in the aqueous phase; This is expressed as a parameter representing the liquid water content in aerosols. , These are represented as the concentration of chloride ions and the concentration of nitrate ions in the liquid phase, respectively. Expressed as the gas-particle partition coefficient; , These are expressed as the concentrations of gaseous nitric acid and particulate nitrate, respectively. This is expressed as the total nitrate formation rate.

[0022] Furthermore, the formula for calculating the source apportionment relative incremental reaction activity (SR-RIR) for each pollution source is as follows:

[0023] In the formula, Represented as the first The relative incremental reactivity of different types of pollution sources to nitrate formation; This represents the baseline simulated nitrate formation rate. This represents the nitrate formation rate after pollution source reduction; Represented as the first Emission reduction of pollution sources; Represented as the first The relative emission reductions contributed by different types of pollution sources.

[0024] Another aspect of the present invention provides a quantitative analysis system for the contribution of nitrate formation from VOCs emission sources, including a VOCs observation data acquisition and preprocessing module: used to acquire continuous hourly concentration data of each VOC component in the ambient air of the target area, simultaneously acquire conventional pollutant concentration data and meteorological parameter observation data; and perform outlier screening and removal, missing value identification and imputation processing; according to the USEPA PMF model user guide, calculate the uncertainty and signal-to-noise ratio of each VOC component, and perform weight reduction or removal processing on low signal-to-noise ratio species according to preset thresholds, and finally output standardized preprocessed VOCs concentration data; The VOCs emission source apportionment module is used to input preprocessed VOCs concentration data into the PMF model for source apportionment calculation. Under non-negative constraints, it iteratively minimizes the weighted residual sum of squares to extract several factors. Each factor contains its contribution matrix in the time series and its corresponding VOCs chemical composition matrix. The module identifies the pollution source type for each factor, assigns pollution source attributes to the identified factors, and finally outputs a standardized pollution source contribution matrix and pollution source composition matrix. Pollution source composition reconstruction module: It is used to analyze and identify the pollution source contribution matrix and pollution source composition matrix based on the PMF model, and reconstruct the VOC concentration corresponding to each pollution source item by item through the multiplication reconstruction algorithm, establish the quantitative correspondence between pollution sources and VOC chemical composition concentration, and obtain the contribution concentration of each pollution source to different VOC components. Pollution source emission reduction simulation module: It is used to construct emission reduction simulation scenarios for each identified pollution source to proportionally reduce the contribution of the target pollution source. During the simulation, only the contribution value of the target pollution source is reduced. The VOC component concentration data under the baseline scenario without emission reduction and under all emission reduction scenarios are input into the observation-constrained photochemical model for simulation to obtain the nitrate formation rate under the baseline scenario and under each pollution source emission reduction scenario. The pollution source control priority evaluation module is used to calculate the source apportionment relative incremental reaction activity (SR-RIR) for each pollution source based on the nitrate formation rate under the baseline scenario and the emission reduction scenario of each pollution source. The pollution sources are sorted in descending order of SR-RIR value to establish a priority control level and output each pollution source and its corresponding SR-RIR value and priority ranking result.

[0025] Furthermore, the pollution source attributes include, but are not limited to, motor vehicle emission sources, solvent use sources, industrial process sources, combustion sources, biomass combustion sources, oil and gas volatilization sources, secondary generation sources, or long-lived species sources.

[0026] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention establishes a data mapping relationship between PMF source apportionment results and observation-constrained photochemical model input parameters, realizing the conversion of pollution source contribution information into photochemical simulation input data. It unifies traditionally independent pollution source apportionment and photochemical sensitivity analysis into a single analytical framework, achieving effective coupling between the PMF receptor model and the observation-constrained photochemical model. It establishes a quantitative correlation between pollution sources, VOC composition, and nitrate formation response, enhancing the application value of pollution source apportionment results in pollution formation mechanism research. By using pollution sources obtained from PMF apportionment as the analytical object, a method for evaluating the relative incremental reactivity of source apportionment is proposed, enabling photochemical sensitivity analysis at the pollution source level. This allows for direct quantitative evaluation of the impact of different VOC emission sources on nitrate formation, providing a new evaluation method for precise pollution source control.

[0027] This invention maintains the VOC composition ratio within each pollution source unchanged during pollution source disturbance, adjusting only the contribution level of each pollution source. This allows for a more realistic simulation of the emission changes during actual emission reduction, avoiding the source composition distortion that may result from directly reducing the concentration of a single VOC component in traditional methods. This improves the scientific rigor and interpretability of the simulation results. Furthermore, it can calculate the corresponding SR-RIR values ​​based on various pollution sources, ranking different VOC emission sources and identifying the pollution sources with the greatest impact on nitrate formation. This enables a direct quantitative evaluation of the contribution of different VOC emission sources to nitrate formation, providing clear technical support for precise regional VOC emission reduction, pollution source classification and control, and differentiated nitrate pollution treatment. This helps improve the targeting and effectiveness of pollution control measures.

[0028] This invention is based on ambient air VOCs monitoring data and does not rely on a complete emission inventory. It is applicable to different application scenarios such as cities, industrial parks, and regional scales. At the same time, the proposed pollution source contribution reconstruction and source-level sensitivity analysis methods have good versatility and can be used in conjunction with existing PMF source apportionment models and observation-constrained photochemical models, providing reliable technical support for regional air quality improvement, nitrate pollution control, emergency emission reduction during heavy pollution weather, and pollution control effect evaluation. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0030] Figure 1 The flowchart illustrates the quantitative analysis method for the contribution of nitrate formation from VOCs emission sources in this invention.

[0031] Figure 2 This is a schematic diagram illustrating the relationship between PMF analysis and VOC concentration reconstruction in this invention.

[0032] Figure 3 This is a flowchart of the source-level disturbance simulation of the present invention.

[0033] Figure 4 A block diagram of the quantitative analysis system for the contribution of nitrate formation to VOCs emission sources in this invention.

[0034] Figure 5 This is a schematic diagram of the source spectrum of VOCs emission sources analyzed by PMF in an application example of the present invention.

[0035] Figure 6 This is an output diagram showing the priority ranking results of pollution source control in an application example of the present invention. Detailed Implementation

[0036] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0037] Example 1 like Figure 1-3 As shown, this invention provides a method for quantitatively analyzing the contribution of nitrate formation from VOCs emission sources, comprising the following steps: S1. VOCs Observation Data Acquisition and Preprocessing: Acquire continuous hourly concentration data of each VOC component in the ambient air of the target area, simultaneously acquire conventional pollutant concentration data and meteorological parameter observation data, preprocess the acquired raw data, and then calculate the uncertainty and signal-to-noise ratio (S / N) of each VOC component according to the USEPA PMF model user guide to generate preprocessed VOCs concentration data, which is used as the input dataset for constructing the PMF model; In a preferred embodiment, the conventional pollutant concentration data include NO, NO2, O3, CO, SO2, and PM2.5. 2.5 ; Meteorological parameter observation data include temperature, relative humidity, solar radiation, wind speed, and wind direction.

[0038] In a preferred embodiment, based on the USEPA PMF model user guide, the formulas for calculating the uncertainty and signal-to-noise ratio are as follows:

[0039] In the formula, The term "method detection limit" refers to the method detection limit for the corresponding component. This represents the uncertainty of the corresponding component. This is represented by the observed concentration values ​​of the corresponding components. This is expressed as the relative error coefficient of the corresponding component; Based on a preset threshold, the signal-to-noise ratio (S / N) is used for classification. Components with an S / N lower than the preset S / N threshold are deemed to have insufficient effective information in the dataset and are subject to weight reduction or removal in subsequent source analysis to avoid noise accumulation interfering with model convergence, thereby ensuring the stability and reliability of the model source analysis results. For example, components with S / N < 0.2 are identified as weak species and directly removed in PMF analysis; for components with 0.2 ≤ S / N < 0.5, their uncertainty is increased by 3 times to reduce their weight.

[0040] Supplementary working principle and effects: First, by simultaneously collecting multi-source heterogeneous data, including continuous hourly concentration data of various VOCs components in the ambient air of the target area, NO, NO2, O3, CO, SO2, PM2.5, etc. 2.5 The concentration data of conventional pollutants, including temperature, relative humidity, solar radiation, wind speed, and wind direction, are combined with meteorological parameter observation data, because atmospheric photochemical reactions are a highly complex nonlinear system, and the oxidation process of VOCs is affected by NO. XMultiple environmental factors, such as concentration, solar radiation intensity, temperature, and humidity, work together to control the data. Therefore, collecting multi-source heterogeneous data ensures that the subsequent PMF model input can fully represent the actual atmospheric environment, avoiding simulation distortion caused by missing input information. Secondly, a data quality control strategy based on statistical principles is adopted. Given that the raw environmental monitoring data inevitably contains issues such as instrument noise, drift, and occasional outliers, it is necessary to preprocess the collected multi-source heterogeneous data, such as outlier screening and missing value handling. For missing values, linear interpolation is used to fill in single missing points; for data with continuous missing values ​​exceeding 3 hours, the data for that period is directly removed without interpolation, thus eliminating explicit errors in the data and improving the overall quality of the input dataset. Then, the uncertainty quantification method recommended by USEPA was adopted. The uncertainty was obtained by distinguishing between the upper and lower limits of the method detection limit and the observed concentration values ​​of the corresponding components. Based on the uncertainty-weighted matrix factorization strategy, the PMF model can treat data points of different qualities differently during the iterative solution process: when the uncertainty is low, it contributes more weight to the objective function and dominates the rotation direction of the factor; when the uncertainty is high, the weight is compressed to prevent outliers or noise points from pulling the factor away from the true physical meaning. This improves the reproducibility and reliability of the source apportionment results, that is, the analysis results of different observation periods and different data subsets have higher stability and consistency. Finally, the species classification and screening mechanism based on signal-to-noise ratio (S / N) combines a preset S / N threshold to divide VOC components into different quality levels, obtaining preprocessed VOC concentration data. This not only reduces the dimensionality and redundancy of the PMF model input matrix, reducing the complexity and convergence time of model calculations, but also avoids collinear coupling between low S / N species and other species during matrix decomposition, thereby reducing the risk of source spectrum misjudgment, more clearly identifying subsequent factors, and providing a reliable data support foundation for the final priority ranking of pollution source control.

[0041] S2. VOCs emission source apportionment and factor identification: The preprocessed VOCs concentration data is input into the PMF model for source apportionment calculation, and several factors are extracted. Each factor contains its contribution matrix (G matrix) in the time series and its corresponding VOCs chemical composition matrix (F matrix). The pollution source type is identified for each factor, and the identified factors are assigned pollution source attributes. In a preferred embodiment, the source resolution steps of the PMF model are as follows:

[0042] In the formula: Represented as the first The first sample Observed concentrations of various VOC components; Represented as the first Class II pollution sources on the first The contribution of each sample; Represented as the first The first type of pollution source The proportion of each VOC component; denoted as the residual between the observation matrix and the reconstruction matrix; p represents the number of pollution source categories; k represents the pollution source category index.

[0043] In a preferred embodiment, each factor is identified as having a pollution source type, including the following information: The proportion of characteristic tracer species in the VOCs composition spectrum of a factor, such as MTBE being considered a tracer for gasoline vehicles (motor vehicle emission sources), aromatic hydrocarbons such as BTEX often being considered tracers for solvent use sources, chloromethane being a tracer for biomass combustion sources, Freon being identified as a long-lived species source, and factors mainly composed of VOCs often being considered secondary generation sources, etc. The temporal variation pattern of factor contribution values; Correlation with other pollutants, such as correlation with tracer pollutants like NOx and CO; A comprehensive assessment should be made based on the local emission source survey list; The criterion for the number of PMF analytical factors is to select the optimal number of factors by running a PMF model with 3-10 factors, where the Q value reaches an inflection point and the absolute value of the residual is less than 3. At the same time, it is ensured that each factor spectrum has clear chemical tracer characteristics. That is, by trying different numbers of factors, the optimal number of factors is determined based on the convergence of Q value, residual analysis and factor interpretability.

[0044] In a preferred embodiment, the identified factors are assigned pollution source attributes, including but not limited to motor vehicle emission sources, solvent use sources, industrial process sources, combustion sources, biomass combustion sources, oil and gas volatilization sources, and secondary generation sources, ultimately forming a pollution source contribution matrix G and a pollution source chemical composition matrix F in a time series.

[0045] Supplementary working principle and effects: The VOCs concentration observed in ambient air is a superimposed and mixed signal from dozens of emission sources under different meteorological diffusion conditions. The VOCs composition of different sources has similarities and serious collinearity. By utilizing the multivariate matrix factorization capability and non-negative constraints of PMF, independent and physically interpretable source factors can be extracted from highly mixed observation data without relying on prior source spectra, achieving the technical effect of "blind source separation". This is the core technical foundation for achieving source-level fine analysis in complex pollution scenarios. Furthermore, the multi-dimensional cross-validation mechanism of the VOCs emission source analysis and factor comprehensive identification system significantly reduces the risk of factor misjudgment. After identification and naming, the G matrix (time-by-time contribution sequence of each pollution source) and F matrix (fixed VOCs composition spectrum of each pollution source) realize the data form transformation from "observation data" to "source information", providing a quantitative source analysis information input basis for subsequent steps.

[0046] S3. Reconstruction of VOC composition of pollution sources: Based on the pollution source contribution matrix G and pollution source chemical composition matrix F determined by the identification factors of the PMF model, the VOC concentration corresponding to each pollution source is reconstructed to establish the correspondence between pollution sources and VOC chemical composition concentrations, so as to obtain the contribution concentration of each pollution source to different VOC components. The source contribution information resolved by PMF is converted into VOC component concentration input data in units of pollution sources required by the subsequent photochemical model. In a preferred embodiment, the steps for establishing the correspondence between pollution sources and VOC chemical composition concentrations are as follows:

[0047] In the formula, Represented as the first The first type of pollution source Concentration of various VOC components; Represented as the first Contribution from similar pollution sources; Represented as the first The first type of pollution source The composition ratio of the concentration of each VOC component;

[0048] In the formula, The first in ambient air Total concentration of VOC components.

[0049] Supplementary working principle and effects: Through source-level concentration reconstruction, the macroscopic information of the contribution of each pollution source to total VOCs output by the PMF is deconstructed into microscopic information of the contribution concentration of each pollution source to different VOC species. This makes the source apportionment results standard input data that can be identified and calculated by the OBM model. This transformation technically breaks down the barriers between the two independent technical fields of source apportionment and photochemical simulation, providing a direct data-driven foundation for subsequent source-level sensitivity analysis. In the reconstruction process, by adopting a reconstruction strategy that varies the total source contribution over time while keeping the source composition ratio constant, the physical characteristics of actual emission behavior are realistically restored, avoiding the introduction of artificial source spectrum distortion during data conversion. This provides a basic guarantee for the scientific validity of subsequent source-level perturbation simulation. Through the above reconstruction process, the contribution concentration of each type of pollution source to each VOC component can be obtained, and a quantitative correlation between each pollution source and its photochemical contribution can be established. This realizes the expansion of the cognitive dimension of pollution source apportionment from source emission contribution to the impact of secondary pollutant generation. The reconstructed "source × time × component" three-dimensional data matrix has a universal and standard data format, which can be directly input into OBM models such as F0AM for photochemical simulation, and can also be flexibly connected to other mainstream atmospheric chemical models (such as CMAQ, CAMx, WRF-Chem, etc.), with good model compatibility and data reusability; and can generate perturbation scenarios that conform to actual emission reduction characteristics, laying the input foundation for source-level SR-RIR calculation.

[0050] S4. Construct source-level emission reduction disturbance simulation scenarios: For each identified pollution source, construct its emission reduction simulation scenario and proportionally reduce the contribution of the target pollution source; in the simulation process, only the contribution value of the target pollution source is reduced, while the VOC composition ratio inside the pollution source remains unchanged, and the contributions and compositions of all other pollution sources remain unchanged. In a preferred embodiment, the steps for reducing the proportion of pollution contribution from the target pollution source are as follows:

[0051] In the formula, Let represent the concentration of the j-th VOC component in the k-th pollution source after emission reduction simulation. This indicates the percentage of pollution source emission reduction, and uses the emission reduction percentage. For the 10% emission reduction scenario, this small perturbation helps to approximate the marginal emission reduction effect of pollution sources within a local linear range. It can also be set to 20%, 30%, 50% or other emission reduction ratios as needed. Represented as the first The composition ratio of the concentration of the first VOC component in a pollution source of type 1; Represented as the number after proportional reduction Contribution from pollution sources.

[0052] Supplementary working principle and effects: By using the single-factor variable control principle, only the emission level of one pollution source is changed each time, making the calculated SR-RIR value have a clear causal attribution meaning. That is, the change in nitrate formation rate is uniquely and solely attributed to the emission reduction of the target pollution source. This avoids the fundamental technical difficulty of not being able to clarify the individual contributions of each source in the scenario of simultaneous emission reduction from multiple sources, achieving a technical leap from correlation analysis to causal inference, and providing a scientific basis for prioritizing pollution sources. Emission reduction measures targeting a specific pollution source will lead to a reduction in the total VOCs emissions of that source, but the chemical composition characteristics of its emissions will not fundamentally change. The emission reduction design with a reduction in total contribution and unchanged composition ratio accurately matches this actual emission change law, that is, it truly simulates the implementation characteristics of actual emission reduction policies and avoids source spectrum distortion. This step generates p structured source-level emission reduction datasets, converting the reconstructed basic data matrix into a series of specific simulation scenarios that can be directly called by the OBM model. The output of this step has a standardized data structure of pollution source concentration matrices after emission reduction simulation under each scenario, which can be batch input into the OBM model for automated simulation.

[0053] S5. Scenario simulation based on observation-constrained photochemical model: VOC component concentration data under the baseline scenario without emission reduction and under all emission reduction scenarios are input into the observation-constrained photochemical model (OBM) for simulation to obtain the nitrate formation rate under the baseline scenario and under each pollution source emission reduction scenario; in the simulation process, the same meteorological conditions, chemical mechanisms, and initial pollutant (NOx, CO, etc.) concentrations are kept constant. In a preferred embodiment, the steps for obtaining the nitrate formation rate are as follows:

[0054] In the formula, Expressed as the gas-phase formation rate (mol·cm) of gaseous nitric acid reacting with NO2 via OH radicals. -3 ·s -1 ); Expressed as the reaction rate (cm) of the gas-phase reaction between OH radicals and NO2. 3 ·mol -1 ·s -1 [OH] and [NO2] represent the concentrations of OH radicals and the real-time molar concentrations of NO2, respectively (mol·cm).-3 ); Expressed as the heterogeneous uptake coefficient of N2O5, and calculated... At that time, the concentrations of all liquid phases were uniformly expressed in mol / L, and the Henry's constant was used. Using mol·L -1 ·atm -1 ; Expressed as the average velocity of N2O5 molecules (cm / s); Expressed as aerosol surface area concentration (μm) 2 / cm 3 [N2O5] is the real-time molar concentration (ppbv) of N2O5. Expressed as the rate of N2O5 hydrolysis to form particulate nitrate (mol·cm) -3 ·s -1 ); Expressed as the quantum efficiency of nitrate products in the hydrolysis of N2O5, where, under normal atmospheric conditions, The value should be between 0.8 and 1.0; the default value is recommended. =0.95; under special pollution conditions (such as high chloride ion aerosols). The value can be adjusted down to 0.7-0.8; Expressed as aerosol liquid phase activity / effective concentration parameter; Expressed as the Henry's constant (mol·L⁻¹) for N₂O₅ in the aqueous phase. -1 ·atm -1 ); This is expressed as a parameter representing the liquid water content in aerosols. , These are expressed as liquid phase chloride ion concentration and liquid phase nitrate ion concentration (mol / L), respectively. Expressed as the gas-particle partition coefficient; , These are expressed as gaseous nitric acid concentration and particulate nitrate concentration (ppbv), respectively. Expressed as the total nitrate formation rate (mol·cm⁻¹) -3 ·s -1 ).

[0055] In a preferred embodiment, F0AM (Framework for O-D Atmospheric Modeling, available as an open-source model on GitHub) is preferentially used as the observation-constrained photochemical model, coupled with MCM v3.3.1 (Master Chemical Mechanism, available as an open-source model on the Master Chemical Mechanism platform) for simulation. Model parameters are as follows: simulation duration is set to 72 hours, with the first 48 hours for model stabilization (spin-up) and the following 24 hours for analysis. An adaptive step-size Rosenbrock integral algorithm is employed, with the accuracy control parameter RTOL = 1 × 10⁻⁶. -8 ATOL = 1 × 10 -10 Calculate the nitrate formation rate under the baseline scenario and the pollution source disturbance scenario, respectively. The nitrate formation rate is determined by the gaseous nitrate formation pathway and... The heterogeneous hydrolysis pathway is jointly determined.

[0056] Supplementary working principle and effects: By employing detailed chemical mechanisms in MCM v3.3.1, the complete chemical transformation pathway of each VOC species from initial emission to final nitrate formation can be accurately tracked, significantly improving the simulation accuracy of nitrate formation rate and making the calculation basis of SR-RIR more reliable. Furthermore, by using the "controlled variable method," all scenarios' meteorological, mechanistic, and NOx external conditions are strictly unified, eliminating confounding variables that might obscure causal relationships. This ensures that the subsequently calculated SR-RIR values ​​have strict causal inference power, achieving pure attribution of nitrate formation rate changes across different scenarios, and realizing synergistic characterization and complete coverage of the dual-channel nitrate formation process. This invention not only calculates the formation rate of gaseous HNO3 but also... The gas-particle partition coefficient takes into account the actual conversion efficiency of HNO3 to particulate nitrate, so that the model output directly corresponds to the PM measured in environmental monitoring. 2.5 The treatment of nitrate concentration significantly improved the comparability and verifiability between simulation results and environmental observations, enabling SR-RIR analysis results to be directly used to guide PM2.5 improvement. 2.5 Actual emission reduction decisions based on mass concentration.

[0057] S6. Calculate the source apportionment relative incremental reaction activity (SR-RIR): Based on the baseline scenario and the nitrate formation rate under each pollution source emission reduction scenario, calculate the source apportionment relative incremental reaction activity (SR-RIR) for each pollution source. In a preferred embodiment, the calculation steps for the source apportionment relative incremental reactivity (SR-RIR) corresponding to each pollution source are as follows:

[0058] In the formula, Represented as the first The relative incremental reactivity of different types of pollution sources to nitrate formation; This represents the baseline simulated nitrate formation rate, i.e., the nitrate formation rate simulated based on a real-world scenario. This represents the nitrate formation rate after pollution source reduction; Represented as the first Emission reduction of pollution sources; Represented as the first The relative emission reductions contributed by different types of pollution sources; in, The higher the value, the greater the nitrate reduction benefit per unit of emission reduction from the pollution source, and the more significant the impact of the pollution source on nitrate formation, and the higher its control priority. To ensure the comparability of the SR-RIR calculation results of relative incremental reactive activity from source apportionment, the following unified calculation conditions are adopted: First, all pollution sources adopt the same emission reduction ratio; Secondly, maintain the VOC composition ratio within the pollution source unchanged; Third, meteorological conditions remain unchanged; Fourth, other pollutants such as NOx and SO2 remain unchanged; Fifth, the chemical mechanisms remain consistent; Sixth, the simulation period remains consistent.

[0059] Supplementing the working principle and its effects: By utilizing SR-RIR to establish a pollution source-level sensitivity index with unified dimensions, the relative importance of "solvent use sources" and "vehicle emission sources" to nitrate formation can be directly compared on the same numerical scale. This achieves a direct quantitative comparison of the contributions of different VOC emission sources to nitrate formation, representing a technological leap from qualitative comparison to quantitative ranking. SR-RIR measures the nitrate reduction benefit per unit of relative emission reduction, rather than the total contribution. This allows for the evaluation of the marginal efficiency of unit emission reduction benefits, providing a scientific decision-making logic for cost-benefit optimization. Furthermore, SR-RIR decomposes the total response into the independent marginal contributions of each source, realizing a transparent decomposition of the pollution source emission reduction effect from a "black box total response" to a "source-level decomposition response."

[0060] S7. Priority Ranking and Output of Pollution Source Control: Based on the calculated SR-RIR values ​​of each pollution source, they are ranked in descending order to establish a priority control level for pollution sources. When the SR-RIR value is greater than zero, it indicates that the pollution source has a positive promoting effect on nitrate formation; the higher the value, the higher the control priority. The system outputs each pollution source and its corresponding SR-RIR value and priority ranking results, providing a quantitative basis for formulating regional VOCs source-oriented treatment and emission reduction strategies.

[0061] In a preferred embodiment, the quantitative analysis method for the contribution of nitrate formation from VOCs emission sources can be widely applied in fields such as atmospheric environmental monitoring, air pollution cause analysis, and pollution prevention and control decision-making. Specifically, it includes, but is not limited to, multiple application scenarios such as urban and regional air pollution cause analysis, VOCs precise emission reduction strategy formulation, emergency control of heavy pollution weather, regional air quality management and policy evaluation, and pollution source apportionment and photochemical model collaborative analysis platform.

[0062] Supplementary working principle and effects: By calculating the SR-RIR values ​​and priority rankings of each pollution source, complex scientific calculation results are transformed into intuitive and actionable pollution source management decision-making information. This allows for the hierarchical and classified management of pollution sources. For example, pollution sources with the highest SR-RIR are marked as key control sources, subjecting them to stricter emission standards and more intensive emission reduction measures; while pollution sources with lower SR-RIR are marked as general control sources, subjecting them to routine management and moderate emission reduction. Furthermore, the descending order of SR-RIR essentially generates the marginal emission reduction benefit curve of pollution sources. Under the constraint of a given total emission reduction target, implementing emission reductions sequentially from high to low SR-RIR yields the optimal benefit path for nitrate reduction, thereby achieving optimized resource allocation for pollution source emission reduction decisions.

[0063] Example 2 like Figure 4 As shown, the present invention provides a quantitative analysis system for the contribution of nitrate formation from VOCs emission sources, comprising:

[0064] VOCs observation data acquisition and preprocessing module: Used to acquire continuous hourly concentration data of various VOCs components in the ambient air of the target area, and simultaneously acquire data including NO, NO2, O3, CO, SO2, and PM2.5. 2.5The data includes conventional pollutant concentration data, as well as meteorological parameter observation data including temperature, relative humidity, solar radiation, wind speed, and wind direction; outlier screening and removal, missing value identification and imputation are performed on the collected raw data; according to the USEPA PMF model user guide, the uncertainty and signal-to-noise ratio of each VOC component are calculated, and low signal-to-noise ratio species are downweighted or removed according to preset thresholds, and finally standardized preprocessed VOCs concentration data are output. The VOCs emission source apportionment module is used to input preprocessed VOCs concentration data into the PMF model for source apportionment calculation. Under non-negative constraints, it iteratively minimizes the weighted residual sum of squares to extract several factors. Each factor contains its contribution matrix (G matrix) over time and its corresponding VOCs chemical composition spectrum (F matrix). By integrating multi-dimensional information such as the proportion of characteristic tracer species in the factors, the time variation of factor contribution values, correlation with tracer pollutants, and the local emission source survey list, the module identifies the pollution source type for each factor and assigns pollution source attributes such as motor vehicle emission source, solvent use source, industrial process source, combustion source, biomass combustion source, oil and gas volatilization source, or secondary generation source to the identified factors. Finally, it outputs the standardized pollution source contribution matrix G and pollution source composition matrix F. The pollution source composition reconstruction module is used to reconstruct the VOC concentration corresponding to each pollution source item by item through a multiplication reconstruction algorithm based on the pollution source contribution matrix G and pollution source composition matrix F determined by the PMF model. It establishes a quantitative correspondence between pollution sources and VOC chemical composition concentrations, obtains the contribution concentration of each pollution source to different VOC components, and converts the statistical source contribution information resolved by the PMF into the VOC component concentration input data matrix with pollution sources as the basic unit required by the subsequent photochemical model. The pollution source emission reduction simulation module is used to construct emission reduction simulation scenarios for each identified pollution source to proportionally reduce the contribution of the target pollution source. During the simulation, only the contribution value of the target pollution source is reduced, while strictly maintaining the VOC composition ratio within the pollution source and keeping the contributions and composition of all other pollution sources unchanged. The VOC component concentration data under the baseline scenario without emission reduction and under all emission reduction scenarios are input into the observation-constrained photochemical model (OBM) for simulation. Under the same meteorological conditions, chemical mechanisms, and initial pollutant concentrations, the nitrate formation rate under the baseline scenario and under each pollution source emission reduction scenario is obtained. The pollution source control priority evaluation module is used to calculate the source apportionment relative incremental reactivity (SR-RIR) of each pollution source based on the nitrate formation rate under the baseline scenario and the emission reduction scenario of each pollution source. The pollution sources are ranked in descending order of SR-RIR value to establish a priority control level. The module outputs each pollution source and its corresponding SR-RIR value and priority ranking results, providing a quantitative basis for formulating regional VOCs source-oriented treatment and emission reduction strategies.

[0065] Application Example 3 like Figure 5-6 As shown, the quantitative analysis method and system disclosed in Examples 1 and 2 are further verified. Specifically, taking the environmental monitoring station in Chengdu, Sichuan Province as an example, this study analyzes the priority of pollution source control for secondary nitrate generation in a typical city: Step 1: Collect raw observation data: Collect continuous observation data of each component of VOCs in ambient air at the target station during the one-month period from December 20, 2021 to January 20, 2022, and simultaneously acquire data on NO, NO2, O3, CO, SO2, and PM2.5. 2.5 Conventional pollutants, including temperature, relative humidity, solar radiation, wind speed, and wind direction; Preprocessing: Quality control preprocessing of raw observation data, including outlier screening, missing value handling, and calculation of uncertainty and signal-to-noise ratio of each VOC component according to the USEPA PMF model user guide. The input data is classified according to the signal-to-noise ratio of each species, and species with low signal-to-noise ratio are downweighted or removed to improve the stability of PMF source apportionment results, resulting in preprocessed VOCs concentration data. For example, outlier screening: data points that exceed the linear range of the instrument or are based on physical principles (such as negative concentration) are deleted. Missing value handling: For single missing values, linear interpolation is used to fill them in; for continuous missing values ​​over a long period of time, the data for that period is directly removed. Uncertainty: According to the USEPA guidelines, the uncertainty is calculated for each VOC species at each time step. For values ​​that are undetectable or below the limit of detection (MDL), the concentration is set to 1 / 2 of the MDL and the uncertainty is set to (5 / 6)*MDL. Signal-to-noise ratio (S / N): The S / N of each species is calculated. Species with S / N < 0.2 are classified as "weak" species and marked as "Bad" and removed in subsequent PMF analyses. Species with S / N < 0.5 are also classified as "weak" species, and their uncertainty is increased by a factor of 3 to reduce their weight, ensuring the stability of the PMF model's analytical results.

[0066] Step 2: PMF Model Source Analysis and Factor Identification: Input the preprocessed VOCs concentration data into the PMF model to calculate the pollution source contribution matrix and pollution source composition matrix. Combine the characteristic species composition, temporal variation characteristics, and pollution source tracers of each factor to identify and name the PMF factors and determine different VOCs emission sources. Among them, VOCs emission sources include motor vehicle emission sources, industrial process sources, solvent use sources, biomass / combustion sources, oil and gas volatilization sources, secondary generation sources, long-lived species sources, etc. Further, the VOC component concentrations corresponding to each pollution source are reconstructed. For example, motor vehicle emission sources correspond to high proportions of ethylene, acetylene, and MTBE, and the factors with daily variations showing bimodal characteristics of morning and evening traffic peaks. Industrial process sources correspond to high proportions of chlorobenzene, 1,2,4-trichlorobenzene and other halogenated hydrocarbons, as well as propane and acetylene factors; The solvent source corresponds to a high proportion of BTEX (benzene, toluene, ethylbenzene, xylene) and esters, with daily variations matching the working time period; Biomass / combustion source corresponds to a high proportion of chloromethane and is a factor with a good correlation to CO; Oil and gas volatile sources correspond to high proportions of isopentane and 2-methylpentane, and are factors with extremely low proportions of ethylene and acetylene compared to motor vehicle emission sources; The secondary sources mainly consist of OVOCs such as propionaldehyde and acetaldehyde, and the diurnal variation shows a peak at midday. Long-lived species sources correspond to a high proportion of long-lived halogenated hydrocarbons such as Freon-11 and Freon-113.

[0067] Step 3: Reconstruction of VOC composition of pollution sources: Based on the contribution matrices of multiple factors and the pollution source composition matrix output by the PMF model, the contribution concentration of each pollution source to each VOC component is reconstructed using the proportional reduction formula of the target pollution source contribution, thereby obtaining the complete VOC concentration input dataset for each pollution source. For example, the hourly contribution concentration sequence of "motor vehicle emission source" to toluene and the hourly contribution concentration sequence of "solvent use source" to m / p-xylene are reconstructed.

[0068] Step 4: Construct source-level emission reduction disturbance simulation scenarios: Using a uniform emission reduction ratio of 10%, construct emission reduction scenarios for the N pollution sources identified above. For example, taking motor vehicle emission sources: first, extract the contribution time series of motor vehicle emission sources; calculate the new contribution series after a 10% emission reduction; keep the pollution source composition matrix of motor vehicle emission sources unchanged; generate VOCs concentration data under this emission reduction scenario; for non-target sources (such as solvent use sources), their contribution and composition remain unchanged; repeat this process to generate an independent emission reduction scenario for each of the remaining N-1 pollution sources, finally obtaining 1 baseline scenario and N pollution source emission reduction scenarios, for a total of N+1 sets of complete VOCs concentration input data.

[0069] Step 5: Scenario Simulation Based on Observation-Constrained Photochemical Model: Using the observation-constrained photochemical FOAM model, meteorological parameter observation data corresponding to the observation period, such as temperature, relative humidity, solar radiation, wind speed, and wind direction, are set; the chemical mechanism selected is MCM v3.3.1: VOCs concentration data from one baseline scenario and N emission reduction scenarios are used as model inputs, along with synchronously observed NO, NO2, O3, CO, SO2, and PM2.5 concentrations. 2.5 The initial concentrations of conventional pollutant concentration data were used to simulate the nitrate formation process under the same meteorological conditions and chemical mechanisms, respectively, for the baseline and emission reduction scenarios. The simulation time step of the model was consistent with the observation data, and a sufficient simulation duration was set to allow the model to reach a stable state. After the simulation was completed, the nitrate formation rate P(NO3) under each scenario was extracted from the model output results.

[0070] Step Six: Calculate the Relative Incremental Reactivity (SR-RIR): Substitute the nitrate formation rates obtained in Step S5 for each scenario into the formula to calculate the SR-RIR for each pollution source, obtaining the SR-RIR values ​​for all pollution sources. For example, ... Figure 6 The emissions shown include solvent usage sources (0.032), motor vehicle emission sources (0.025), residential emission sources (0.018), oil and gas evaporation sources (0.005), long-lived species sources (-0.017), biomass combustion sources (-0.018), and industrial emission sources (-0.025).

[0071] Step 7: Pollution Source Control Priority Ranking and Output: Based on the Source Apportionment Relative Incremental Reactivity (SR-RIR) corresponding to the pollution sources obtained in Step S6, sort all pollution sources according to their SR-RIR values ​​from largest to smallest to select control priorities. For example, the ranking results are as follows: Figure 6 As shown, the emission sources are ranked as follows: solvent use (0.032) > motor vehicle emissions (0.025) > residential emissions (0.018) > oil and gas volatiles (0.005) > long-lived species (-0.017) > biomass combustion (-0.018) > industrial emissions (-0.025). To control secondary nitrate formation, the city should prioritize controlling solvent use sources, followed by motor vehicle emissions, residential emissions, and oil and gas volatiles. Finally, reducing emissions from long-lived species, biomass combustion, and industrial sources is almost ineffective in reducing nitrates. This ranking provides environmental management departments with clear and quantifiable "source-level" control targets. That is, when formulating VOCs emission reduction plans, priority should be given to strengthening control measures for solvent use sources (such as promoting water-based coatings and improving coating processes) and motor vehicle emissions sources (such as improving oil quality and strengthening exhaust gas supervision) to achieve the greatest synergistic nitrate emission reduction benefits.

[0072] The present invention provides a quantitative analysis system for the contribution of nitrate formation from VOCs emission sources, which is implemented by the above-mentioned quantitative analysis method for the contribution of nitrate formation from VOCs emission sources. For details of the specific method and process of the quantitative analysis method for the contribution of nitrate formation from VOCs emission sources, please refer to the above-mentioned embodiment of the quantitative analysis system for the contribution of nitrate formation from VOCs emission sources, which will not be repeated here.

[0073] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0074] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0075] It should be understood that in the various embodiments of this application, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0076] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0077] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for quantitatively analyzing the contribution of nitrate formation from VOCs emission sources, characterized in that, The steps include the following: S1. Acquire continuous hourly concentration data of each VOC component in the ambient air of the target area, simultaneously acquire conventional pollutant concentration data and meteorological parameter observation data, perform preprocessing, and then calculate the uncertainty and signal-to-noise ratio of each VOC component according to the USEPA PMF model user guide to generate preprocessed VOC concentration data. S2. Input the preprocessed VOCs concentration data into the PMF model for source apportionment calculation, and extract the contribution matrix contained in the time series and several factors of the corresponding VOCs chemical composition matrix; identify the pollution source type for each factor and assign pollution source attributes to the identified factors; S3. Based on the pollution source contribution matrix and VOCs chemical composition matrix determined by the identification factors in the PMF model, reconstruct the VOC concentration corresponding to each pollution source, establish the correspondence between pollution sources and VOC chemical composition concentrations, and obtain the contribution concentration of each pollution source to different VOC components. S4. For each identified pollution source, construct its emission reduction simulation scenario and proportionally reduce the contribution of the target pollution source. S5. Based on the observation-constrained photochemical model, the VOCs component concentration data under the baseline scenario without emission reduction and all emission reduction scenarios are simulated to obtain the nitrate formation rate under the baseline scenario and under each pollution source emission reduction scenario. S6. Based on the baseline scenario and the nitrate formation rate under each pollution source emission reduction scenario, calculate the source apportionment relative incremental reaction activity (SR-RIR) for each pollution source. S7. Based on the calculated SR-RIR values ​​of each pollution source, sort them in descending order to establish a priority control level for pollution sources; and output each pollution source and its corresponding SR-RIR value and priority ranking result.

2. The method for quantitatively analyzing the contribution of nitrate formation from VOCs emission sources according to claim 1, characterized in that, The conventional pollutant concentration data include NO, NO2, O3, CO, SO2, and PM2.

5. 2.5 ; The meteorological parameter observation data include temperature, relative humidity, solar radiation, wind speed, and wind direction.

3. The method for quantitatively analyzing the contribution of nitrate formation from VOCs emission sources according to claim 1, characterized in that, Based on the USEPA PMF model user guide, the formula for calculating the uncertainty is: In the formula, The limit of detection is expressed as the method detection limit for the corresponding component. This represents the uncertainty of the corresponding component. This is represented by the observed concentration values ​​of the corresponding components. It is expressed as the relative error coefficient of the corresponding component.

4. The method for quantitatively analyzing the contribution of nitrate formation from VOCs emission sources according to claim 1, characterized in that, The source resolution formula of the PMF model is: In the formula: Represented as the first The first sample Observed concentrations of various VOC components; Represented as the first Class II pollution sources on the first The contribution of each sample; Represented as the first The first of the pollution sources The proportion of each VOC component; denoted as the residual between the observation matrix and the reconstruction matrix; p represents the number of pollution source categories; k represents the pollution source category index.

5. The method for quantitatively analyzing the contribution of nitrate formation from VOCs emission sources according to claim 1, characterized in that, The relationship between the pollution source and the concentration of VOC chemical composition was established as follows: In the formula, Represented as the first The first of the pollution sources Concentration of various VOC components; Represented as the first Contribution from similar pollution sources; Represented as the first The first of the pollution sources The composition ratio of the concentrations of various VOC components; In the formula, The first in ambient air Total concentration of VOC components.

6. The method for quantitatively analyzing the contribution of nitrate formation from VOCs emission sources according to claim 1, characterized in that, The formula for reducing the proportion of the target pollution source's contribution is: In the formula, Let represent the concentration of the j-th VOC component in the k-th pollution source after emission reduction simulation. Indicates the percentage of pollution source emission reduction; Represented as the first The first of the pollution sources The composition ratio of the concentrations of various VOC components; Represented as the number after proportional reduction Contribution from pollution sources.

7. The method for quantitatively analyzing the contribution of nitrate formation from VOCs emission sources according to claim 1, characterized in that, The formula for calculating the nitrate formation rate is as follows: In the formula, This represents the rate of formation of gaseous nitric acid through the gas-phase reaction of OH radicals with NO2; The value represents the reaction rate of the gas-phase reaction between OH radicals and NO2; [OH] and [NO2] represent the concentrations of OH radicals and the real-time molar concentration of NO2, respectively. Expressed as the heterogeneous uptake coefficient of N2O5; This is expressed as the average velocity of N2O5 molecules; Expressed as aerosol surface area concentration; [N2O5] is the real-time molar concentration of N2O5; This represents the rate of N2O5 hydrolysis to form particulate nitrates. Expressed as the quantum efficiency of nitrate products in the hydrolysis of N2O5; Expressed as aerosol liquid phase activity / effective concentration parameter; Expressed as the Henry's constant for N2O5 in the aqueous phase; This is expressed as a parameter representing the liquid water content in aerosols. , These are represented as the concentration of chloride ions and the concentration of nitrate ions in the liquid phase, respectively. Expressed as the gas-particle partition coefficient; , These are expressed as the concentrations of gaseous nitric acid and particulate nitrate, respectively. This is expressed as the total nitrate formation rate.

8. The method for quantitatively analyzing the contribution of nitrate formation from VOCs emission sources according to claim 1, characterized in that, The formula for calculating the source apportionment relative incremental reaction activity (SR-RIR) for each pollution source is as follows: In the formula, Represented as the first The relative incremental reactivity of different types of pollution sources to nitrate formation; This represents the baseline simulated nitrate formation rate. This represents the nitrate formation rate after pollution source reduction; Represented as the first Emission reduction of pollution sources; Represented as the first The relative emission reductions contributed by different types of pollution sources.

9. A quantitative analysis system for the contribution of nitrate formation from VOCs emission sources, comprising a quantitative analysis method for the contribution of nitrate formation from VOCs emission sources according to any one of claims 1-8, characterized in that, It includes a VOCs observation data acquisition and preprocessing module: used to acquire continuous hourly concentration data of each VOC component in the ambient air of the target area, and simultaneously acquire conventional pollutant concentration data and meteorological parameter observation data; and to perform outlier screening and removal, missing value identification and imputation processing; according to the USEPA PMF model user guide, it calculates the uncertainty and signal-to-noise ratio of each VOC component, and performs weight reduction or removal processing on species with low signal-to-noise ratio according to preset thresholds, and finally outputs standardized preprocessed VOCs concentration data; VOCs emission source apportionment module: It is used to input the preprocessed VOCs concentration data into the PMF model for source apportionment calculation. Under non-negative constraints, it iteratively minimizes the weighted residual sum of squares to extract several factors. Each factor contains its contribution matrix in the time series and its corresponding VOCs chemical composition matrix. For each factor, pollution source type identification is performed, and the identified factors are assigned pollution source attributes. Finally, a standardized pollution source contribution matrix and pollution source composition matrix are output. Pollution source composition reconstruction module: It is used to analyze and identify the pollution source contribution matrix and pollution source composition matrix based on the PMF model, and reconstruct the VOC concentration corresponding to each pollution source item by item through the multiplication reconstruction algorithm, establish the quantitative correspondence between pollution sources and VOC chemical composition concentration, and obtain the contribution concentration of each pollution source to different VOC components. Pollution source emission reduction simulation module: It is used to construct emission reduction simulation scenarios for each identified pollution source to proportionally reduce the contribution of the target pollution source. During the simulation, only the contribution value of the target pollution source is reduced. The VOC component concentration data under the baseline scenario without emission reduction and under all emission reduction scenarios are input into the observation-constrained photochemical model for simulation to obtain the nitrate formation rate under the baseline scenario and under each pollution source emission reduction scenario. The pollution source control priority evaluation module is used to calculate the source apportionment relative incremental reactivity (SR-RIR) of each pollution source based on the nitrate formation rate under the baseline scenario and the emission reduction scenario of each pollution source. The pollution sources are sorted in descending order of SR-RIR value to establish a priority control level and output each pollution source and its corresponding SR-RIR value and priority ranking result.

10. The quantitative analysis system for the contribution of nitrate formation from VOCs emission sources according to claim 9, characterized in that, The pollution source attributes include motor vehicle emission sources, solvent use sources, industrial process sources, combustion sources, biomass combustion sources, oil and gas volatilization sources, secondary generation sources, or long-lived species sources.

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