A method for quantifying the contribution of pan precursors based on real atmospheric observation constraints

Abstract

The application provides a PAN precursor contribution quantification method based on real atmospheric observation constraints, relates to the technical field of atmospheric pollution tracing, and comprises the following steps: obtaining real atmospheric observation data of a target region, including PAN, nitrogen oxides, ozone, volatile organic compounds, carbonyl compounds, photolysis frequency and meteorological parameters; constructing an observation constraint vector, establishing an observation constraint box model containing PAN conversion, VOCs oxidation, free radical circulation mechanism and the like and driving operation, recording the relationship between reaction flux and species to form a dynamic reaction network; determining a generation flux based on PAN mass conservation, reversely tracing peroxyacetyl radicals, intermediate products to observation precursors from the PAN generation reaction, and distributing the contribution according to the flux proportion; merging the contribution of the same precursor and its oxidation products, and quantifying the contribution amount and contribution rate of each precursor to PAN generation. The application overcomes the defects of high uncertainty and artificial assumption, and accurately quantifies the actual contribution of real atmospheric PAN precursors.

Description

Technical Field

[0001] This invention relates to the field of atmospheric pollution source tracing technology, and in particular to a method for quantifying the contribution of PAN precursors based on real atmospheric observation constraints. Background Technology

[0002] Peroxyacetyl nitrate (PAN) is a typical secondary atmospheric pollutant. PAN is mainly generated by the reversible reaction of peroxyacetyl radicals formed during the oxidation of volatile organic compounds (VOCs) with nitrogen dioxide (NO2). PAN is easily thermally decomposed at high temperatures but is relatively stable at lower temperatures or during regional transport. Therefore, it can reflect local photochemical generation processes and participate in the storage and transport of nitrogen oxides and reactive free radicals at regional scales. PAN formation is closely related to factors such as VOCs, oxygen-containing VOCs, carbonyl compounds, nitrogen oxides, free radicals, photolysis frequency, temperature, and boundary layer changes. Quantitatively identifying the contribution of different precursors to PAN formation is of great significance for understanding the formation mechanism of photochemical pollution, identifying key control species, and formulating synergistic control strategies for ozone and secondary pollution.

[0003] Existing technical solutions regarding PAN sources or precursor contributions typically include precursor-sensitive perturbation methods, emission scenario reduction methods, and source-class labeling methods. For example... Figure 1 As shown, precursor sensitivity perturbation methods typically involve artificially altering the concentration or emission of a certain type of VOCs, NOx, or key precursors in an atmospheric chemistry model, and then comparing the changes in PAN concentration or formation rate before and after the perturbation to determine the sensitivity of PAN to relevant precursors. Emission scenario reduction methods are usually based on emission inventories, setting up reduction scenarios for transportation sources, industrial sources, solvent use sources, biological sources, combustion sources, or regional transport, and simulating changes in PAN concentration under different scenarios using chemical transport models or box models. Source-based labeling methods typically label precursors from different source types, regions, or emission sectors in the model and track the contribution of labeled species to PAN formation during the reaction process.

[0004] Existing methods for precursor-sensitive perturbations, emission scenario reductions, and source class labeling still have the following shortcomings, including: First, existing methods typically rely on artificially set perturbation ratios, reduction ratios, emission inventories, or source labeling rules. Given the diverse types of VOCs, significant spatiotemporal variations in emissions, and complex secondary transformation processes in the actual atmospheric environment, both emission inventories and source labeling may contain considerable uncertainties, thus affecting the accuracy of PAN precursor contribution results.

[0005] Second, existing methods are mostly positive response analyses, which start from changes in precursors or emission sources to determine the response of PAN to changes in input. These results are closer to sensitivity contributions or scenario response contributions, and are not necessarily equivalent to the actual chemical contributions in the PAN formation process at a certain moment in the real atmosphere.

[0006] Third, existing methods struggle to fully utilize the instantaneous reaction information contained in minute- or hourly real atmospheric observation data. PAN concentrations in the actual atmosphere are influenced by a combination of formation, thermal decomposition, transport, dilution, and boundary layer changes. Context reduction or labeling alone is insufficient to analyze the reaction pathways and precursor oxidation product sources corresponding to PAN formation at a specific moment.

[0007] Fourth, PAN formation typically involves a multi-stage oxidation reaction process. Upstream VOCs are first oxidized to aldehydes, ketones, dicarbonyl compounds, and other oxygen-containing intermediates, which further generate peroxyacetyl radicals, ultimately reacting with NO2 to form PAN. Existing methods often struggle to trace the PAN formation reaction chain backwards to the observed upstream precursors, and also find it difficult to uniformly sum and attribute the contributions of various oxidation products of each precursor to the corresponding precursor.

[0008] To address the shortcomings of existing methods, it is urgent to provide a quantitative method for the contribution of peroxyacetyl nitrate precursors, which can unify and merge the contributions of multiple generations of oxidation products of the same precursor, analyze the actual reaction pathways and sources of precursor oxidation products corresponding to PAN formation, and reflect the actual chemical contribution of PAN formation at a certain moment in the real atmosphere, thereby improving the accuracy of PAN precursor contribution results. Summary of the Invention

[0009] To address the problems in the background technology, this invention provides a method for quantifying the contribution of PAN precursors based on real atmospheric observation constraints. Using minute-level or hourly observation data of the real atmosphere as constraints, an observation constraint box model is used to simulate and record the atmospheric chemical reaction process at each moment. Starting from the PAN formation reaction, the peroxyacetyl radical, precursor oxidation products, and upstream observed precursors that generate PAN are traced back along the reaction network. The contributions of a certain precursor and all its oxidation products to PAN formation are summed and attributed to that precursor, thereby obtaining the contribution amount and contribution rate of different precursors to PAN formation.

[0010] To achieve the above objectives, this invention provides a method for quantifying PAN precursor contributions based on real atmospheric observation constraints, comprising: Acquire real atmospheric observation data of the target area or target observation station. The observation data includes PAN concentration, nitrogen oxide concentration, O3 concentration, VOCs concentration, carbonyl compound concentration, photolysis frequency and meteorological parameters, with a time resolution of 1 minute to 1 hour. The observation data is preprocessed to construct an observation constraint vector that varies over time; an observation constraint box model is established that includes the mechanisms of PAN generation and decomposition, VOCs multigenerational oxidation, carbonyl compound conversion, free radical cycling, and nitrogen oxide conversion; and the observation constraint vector is input into the observation constraint box model. The observation data is input and used to drive the observation constraint box model to record the reaction flux, reaction rate and upstream and downstream generation relationship data at each moment, forming a dynamic reaction network. Based on the PAN mass conservation relationship, the PAN concentration change rate is calculated using the time series of observed PAN concentrations. Combined with the thermal decomposition loss calculated by the observation constraint box model, the chemical generation rate or generation flux of PAN at the target time is determined. Starting with the PAN generation reaction, based on the recorded data, the source of peroxyacetyl radical and intermediate oxidation products are traced back along the dynamic reaction network to the upstream observed precursors. According to the proportion of reaction flux in each path, the contribution of PAN generation is allocated to each observed precursor based on the chemical generation rate or generation flux of PAN at the target time. The contributions of the same observed precursor and all its intermediate oxidation products to PAN formation are summed and merged to calculate the contribution amount and contribution rate of each observed precursor to PAN formation at the target time.

[0011] As a further improvement of the present invention, the observation data also includes one or more of the following: carbon monoxide concentration, sulfur dioxide concentration, particulate matter concentration, solar radiation or ultraviolet radiation intensity, wind speed, wind direction, and boundary layer height.

[0012] As a further improvement of the present invention, the VOCs include one or more of the following: alkanes, alkenes, alkynes, aromatic hydrocarbons, halogenated hydrocarbons, oxygen-containing volatile organic compounds, and bio-derived volatile organic compounds; The carbonyl compound includes one or more of formaldehyde, acetaldehyde, propionaldehyde, acetone, glyoxal, and methylglyoxal.

[0013] As a further improvement of the present invention, the recording form of the dynamic reaction network is a reaction flux matrix F(t), and the matrix elements F ij (t) represents the reaction flux at target time t from species i to species j or subsequent products.

[0014] As a further improvement of the present invention, the thermal decomposition loss is calculated by the observation constraint box model based on the temperature, pressure and PAN concentration at the target time, combined with the PAN thermal decomposition reaction rate constant.

[0015] As a further improvement of the present invention, when determining the chemical formation rate of PAN at the target time, the method further includes: Based on the boundary layer height change rate, wind speed, and wind direction in meteorological observation data, a transport dilution correction term is determined, and the transport dilution correction term is incorporated into the mass conservation relationship to correct the chemical formation rate of PAN at the target time.

[0016] As a further improvement of the present invention, during the reverse tracing process, based on the reaction flux recorded by the observation constraint box model, all reaction pathways that generate peroxyacetyl radicals at the target time are identified, including one or more of the following: acetaldehyde oxidation pathway, acetone photolysis or oxidation pathway, methylglyoxal oxidation pathway, glyoxal oxidation pathway, aromatic hydrocarbon oxidation product pathway, olefin oxidation product pathway, and biogenic VOCs oxidation product pathway.

[0017] As a further improvement of the present invention, the reverse tracing adopts a generation-by-generation recursive method: The first generation is the direct PAN generation layer, the second generation is the peroxyacetyl radical source layer, the third generation is the generation layer of peroxyacetyl radical source species, and the fourth generation and above are multi-generation oxidation layers from upstream volatile organic compounds or carbonyl compounds to generate their oxidation products. Each generation determines its contribution allocation ratio based on the actual reaction flux recorded by the observation constraint box model.

[0018] As a further improvement to the present invention, a result verification step is also included: The contribution of each observed precursor is summed to obtain the reconstructed PAN generation flux. The reconstructed PAN generation flux is compared with the chemical generation rate of PAN at the target time. If the deviation is lower than a preset threshold, the calculated contribution of each precursor to PAN generation and the contribution rate result are valid and verified. The preset threshold is 5% to 30%.

[0019] As a further improvement of the present invention, the output forms of the contribution amount and contribution rate include: The contribution of different observed precursors to PAN formation at each target time point, contribution rate, contribution ranking, main reaction pathway, main oxidation products, and generational tracing relationship; For continuous observation periods, time series of PAN precursor contributions are generated at the minute or hour level.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention uses real atmospheric high temporal resolution observation data to constrain the model, constructs a dynamic reaction network, and traces back from PAN to upstream observed precursors, unifying and merging the contributions of multiple generations of oxidation products. It overcomes the shortcomings of existing positive sensitivity perturbation, emission scenario reduction, source labeling methods that rely on human assumptions, and results that are response contributions rather than real chemical contributions. It significantly reduces the uncertainty brought about by emission inventories and source apportionment rules, and accurately quantifies the actual contribution of each precursor in the real atmosphere to PAN formation.

[0021] The observational constraint box model proposed in this invention is an atmospheric chemical simulation tool that uses real atmospheric observation data as constraints. This model can take minute-level or hourly observational data on PAN, O3, NO, NO2, VOCs, carbonyl compounds, photolysis frequency, temperature, humidity, and air pressure as input conditions to simulate atmospheric chemical reaction processes within a specific observation station or target air mass under given chemical mechanisms. The observational constraint box model can output the concentrations of each chemical species, reaction rates, free radical cycles, and different reaction fluxes, providing a foundation for studying the PAN formation mechanism based on real observations.

[0022] This invention does not obtain the contribution of PAN sources through precursor sensitivity perturbation, emission scenario reduction, or source class labeling. Instead, it obtains the contribution of different precursors to PAN formation in the actual reaction process by recording the model reaction process under the constraint of real observation, mass conservation relationship, apparent reaction rate, and reverse reaction network tracing.

[0023] The observation data of this invention covers pollutants, radiation, wind fields, boundary layers, and various VOCs and carbonyl compounds. It can be adapted to complex atmospheric environments such as cities, industrial parks, and bio-source areas, comprehensively characterizes real photochemical conditions, and significantly improves the accuracy of model constraints and source tracing.

[0024] This invention uses a reaction flux matrix to quantitatively characterize dynamic reaction networks, recording upstream and downstream generation relationships of species in real time and at each time step; dynamic correction of thermal decomposition loss and transport dilution terms ensures that the PAN generation rate benchmark closely matches actual observations, and the tracing process is strictly based on actual flux allocation to avoid bias from human assumptions.

[0025] This invention covers all key peroxyacetyl radical generation pathways, including carbonyl, aromatic hydrocarbons, olefins, and bio-derived radicals. It employs a generation-by-generation recursive tracing method to fully analyze multi-level oxidation chains, ensuring that the tracing results are complete and the source attribution is accurate.

[0026] This invention introduces a reconstructed flux cross-validation mechanism and sets a deviation threshold to ensure robust and reliable results; it outputs minute / hour-level contribution time series, sorting and path information, which can analyze the dynamic process of pollution and provide accurate decision support for collaborative control. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the existing PAN precursor contribution analysis method disclosed in this invention; Figure 2 This is a schematic diagram of the PAN precursor contribution quantification method based on real atmospheric observation constraints disclosed in one embodiment of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] The present invention will now be described in further detail with reference to the accompanying drawings: like Figure 2 As shown, the present invention provides a method for quantifying the contribution of PAN precursors based on real atmospheric observation constraints, specifically including: S1. Obtain real atmospheric observation data of the target area or target observation station. The observation data includes PAN concentration, nitrogen oxide concentration, O3 concentration, VOCs concentration, carbonyl compound concentration, photolysis frequency and meteorological parameters, with a time resolution of 1 minute to 1 hour. The preferred temporal resolution of the observation data is 1 minute, 5 minutes, 10 minutes, 30 minutes, or 1 hour. The observed data includes PAN concentration, and can also measure the concentrations of other components such as PPN and MPAN; NO, NO2, and NOx concentrations; O3, CO, SO2, and particulate matter concentrations; VOCs concentrations, including alkanes, alkenes, alkynes, aromatic hydrocarbons, halogenated hydrocarbons, oxygen-containing volatile organic compounds, and biogenic volatile organic compounds; carbonyl compound concentrations, including formaldehyde, acetaldehyde, propionaldehyde, acetone, glyoxal, methylglyoxal, and other oxygen-containing intermediates that can generate peroxyacetyl radicals; photolysis frequency, solar radiation, or ultraviolet radiation; and meteorological parameters such as temperature, relative humidity, air pressure, wind speed, wind direction, and boundary layer height.

[0030] Specifically, online continuous monitoring equipment is prioritized to acquire minute-level raw data, and missing time periods are filled by linear interpolation of adjacent minute data from the same station; all observation parameters are collected synchronously and timestamps are aligned to avoid time-series misalignment. This step, through high temporal resolution and multi-component collaborative observation, accurately characterizes the real atmospheric photochemical environment, providing a reliable input basis for time-by-time constraints of subsequent models, and reducing the uncertainty brought about by human assumptions and emission inventories from the source.

[0031] S2. Preprocess the observation data and construct the observation constraint vector that changes over time; establish an observation constraint box model that includes the mechanisms of PAN generation and decomposition, VOCs multigenerational oxidation, carbonyl compound conversion, free radical cycle and nitrogen oxide conversion, and input the observation constraint vector into the observation constraint box model. This process involves standardizing data from different instruments to the same temporal resolution. For high temporal resolution data (e.g., 1 minute), the data can be averaged to the minute or hour level as needed. For low temporal resolution data (e.g., 1 hour), the model calculation step size can be matched using time interpolation, moving average, or representative values ​​from nearby time periods. Outlier removal, missing value handling, unit conversion, temperature and pressure correction, and timestamp correction are performed on the observed data.

[0032] PAN, NO, NO2, O3, VOCs, carbonyl compounds, photolysis frequency, and meteorological parameters are used to construct observation constraint vectors. These observation constraint vectors change over time and are used to constrain the boundary and reaction conditions of the observation constraint box model.

[0033] The specific transformation mechanisms included in the observation constraint box model include: PAN generation reaction, PAN thermal decomposition reaction, peroxyacetyl radical generation reaction, carbonyl compound oxidation reaction, VOCs multigeneration oxidation reaction, OH reaction, O3 reaction, NO3 reaction, photolysis reaction, and free radical cycle reaction between RO2, HO2 and NO.

[0034] Specifically, outlier removal employs the 3σ criterion or quartile method to eliminate abrupt changes that deviate from the normal photochemical concentration range; units are standardized to volume mixing ratio (ppbv), and gas concentrations are corrected according to standard temperature and pressure (25℃, 1 atm); observation constraint vectors are generated minute-by-minute to ensure that each model calculation step corresponds to the actual observation state. This step standardizes and aligns multi-source observation data over time, establishing a chemical mechanism that closely resembles the real atmosphere, freeing the model from idealized emission scenarios and ensuring the authenticity and traceability of the subsequent dynamic response network.

[0035] S3. Input the observation data and drive the observation constraint box model to run, record the reaction flux, reaction rate and upstream and downstream generation relationship data at each moment, and form a dynamic reaction network. The observational data drives the observation constraint box model. During the model's operation, the total reaction rate, reaction flux, reactant concentration, product concentration, apparent reaction rate constant, branching yield, and species origin relationship are recorded at each time step.

[0036] The reaction process record includes at least the flux of PAN generation, the flux of PAN thermal decomposition, the flux of peroxyacetyl radical generation, the flux of peroxyacetyl radical loss, the flux of carbonyl compound generation, the flux of carbonyl compound loss, the reaction flux of VOCs to form oxidation products, and the upstream and downstream reaction connections between the various species.

[0037] For any target time t, the recorded results can be represented as the reaction flux matrix F(t), where the elements F... ij (t) represents the reaction flux at target time t from species i to species j or its subsequent products. This matrix allows us to construct the dynamic reaction network at target time t.

[0038] Specifically, the model's computational step size is consistent with the observation time resolution (1–60 minutes). Each step initializes the boundary conditions with the observation constraint vector at that moment, and updates the free radical concentration and reaction rate in real time. The reaction flux matrix F(t) synchronously stores the quantitative correlation between species generation and consumption, with the matrix dimensions adaptively adjusted according to the number of participating species. This step, driven by time-by-time observations and quantitatively recording the entire reaction process, constructs a dynamic reaction network that evolves with the real atmosphere, providing a unique and reliable flux basis for subsequent reverse tracing and avoiding the limitation of static reaction networks in reflecting instantaneous photochemical processes.

[0039] S4. Based on the PAN mass conservation relationship, the PAN concentration change rate is calculated using the time series of observed PAN concentrations. Combined with the thermal decomposition loss calculated by the observation constraint box model, the chemical generation rate or generation flux of PAN at the target time is determined. Specifically, a PAN mass conservation relationship is established for each target time t. The change in PAN concentration is jointly determined by chemical formation, thermal decomposition loss, other chemical losses, transport dilution, and boundary layer changes. The PAN concentration change rate is calculated based on the time series of observed PAN concentrations, and the chemical formation rate of PAN at the target time is determined by combining the PAN thermal decomposition flux, other loss fluxes, and optional transport correction terms recorded by the model.

[0040] The thermal decomposition loss is calculated by the observation constraint box model based on the temperature, pressure, and PAN concentration at the target time, combined with the PAN thermal decomposition reaction rate constant.

[0041] Furthermore, the PAN chemical formation rate can also be directly obtained from the PAN formation flux recorded by the observational constraint box model. The main PAN formation reaction is the reaction of peroxyacetyl radicals with NO2 in the presence of a third body to form PAN. The apparent formation rate of this reaction is jointly determined by the concentrations of peroxyacetyl radicals, NO2, and the third body, as well as the rate constants corrected for temperature and pressure. By using real observations of NO2 and meteorological parameter constraints, the model can obtain the PAN formation flux at the target time.

[0042] When there is a discrepancy between the PAN formation rate inferred from mass conservation and the PAN formation flux recorded by the model, the mass conservation condition under observational constraints can be satisfied by adjusting the transport dilution term, boundary layer correction term, or model free radical constraint term. Specifically, the transport dilution correction term is determined based on the boundary layer height change rate, wind speed, and wind direction in meteorological observation data, and is incorporated into the mass conservation relation to correct the chemical formation rate of PAN at the target time.

[0043] Specifically, the PAN concentration change rate was calculated using the central difference method (ΔPAN / Δt); the thermal decomposition rate constant was calculated using the temperature-dependent Arrhenius formula and dynamically updated with real-time temperature; the transport dilution term was estimated by coupling boundary layer volume change with horizontal wind transport flux, and the free radical constraint term was adjusted preferentially. The concentration is within a reasonable range. This step, by coupling the observed concentration time series with the model process, achieves bidirectional determination and closed-loop calibration of the PAN generation rate, ensuring that the traceability benchmark closely matches the actual observed changes and avoiding systematic biases caused by a single calculation path.

[0044] S5. Starting with the PAN generation reaction, based on the recorded data, trace back along the dynamic reaction network to the source of peroxyacetyl radical, intermediate oxidation products, and upstream observed precursors. Based on the proportion of reaction flux in each pathway, allocate the contribution of PAN generation to each observed precursor based on the chemical generation rate or generation flux of PAN at the target time. In this invention, the reverse tracing method adopts a generational recursive approach. The first generation is the direct generation layer of PAN, the second generation is the peroxyacetyl radical source layer, the third generation is the generation layer of peroxyacetyl radical source species, and the fourth generation and above are multi-generation oxidation layers from upstream VOCs or carbonyl compounds to generate their oxidation products. The contribution allocation ratio of each generation is determined based on the actual reaction flux recorded by the observation constraint box model, rather than based on artificially set source labels or reduction scenarios.

[0045] Starting with the PAN generation reaction at the target time t as the starting point for reverse tracing, the direct reactants for PAN generation are first identified, namely peroxyacetyl radical and NO2. Since NO2 participates in PAN generation as an inorganic reactant, this invention focuses on tracing the source of peroxyacetyl radical. Based on the reaction flux recorded by the observation constraint box model, all reaction pathways for the generation of peroxyacetyl radical at the target time t are identified, including the acetaldehyde oxidation pathway, the acetone photolysis or oxidation pathway, the methylglyoxal oxidation pathway, the glyoxal oxidation pathway, the aromatic hydrocarbon oxidation product pathway, the olefin oxidation product pathway, the biogenic VOCs oxidation product pathway, and other oxygen-containing intermediate pathways.

[0046] Furthermore, for each pathway that generates peroxyacetyl radicals, its proportion of the total peroxyacetyl radical generation flux at the target time t is calculated. Then, the PAN generation flux is allocated to the corresponding peroxyacetyl radical source pathways according to the proportion of each pathway, thus obtaining the direct contribution of each oxidation product or intermediate to PAN generation.

[0047] For each identified peroxyacetyl radical source species, the reaction flux matrix recorded in the observation constraint box model is traced upstream. If the source species is an observed precursor, its contribution is directly attributed to that observed precursor. If the source species is an oxidation product or intermediate generated by the model, the contribution is further allocated backward based on the upstream reaction flux that generates the oxidation product, until the observed precursor set is reached. The observed precursor set includes VOCs and carbonyl compounds observed by actual instruments and constrained by the observation constraint box model, including ethylene, propylene, isoprene, benzene, toluene, ethylbenzene, xylene, trimethylbenzene, acetaldehyde, propionaldehyde, acetone, methylglyoxal, glyoxal, and other observable species that can participate in the PAN generation chain.

[0048] Specifically, the reverse tracing is based on flux conservation. The contribution allocation of each generation strictly follows the upstream generation flux ratio, without fixed allocation weights. The tracing terminates when the observed precursor set is reached, and does not extend to unobserved virtual species. Parallel paths (such as toluene → methylglyoxal, xylene → methylglyoxal) are independently allocated according to their respective fluxes and then aggregated. This step, through flux-driven generational reverse tracing, eliminates artificial labels and scenario assumptions, accurately locates the actual chemical pathways of PAN generation, achieves a closed-loop tracing from observed products to upstream precursors, and analyzes the true contribution of multi-stage oxidation processes.

[0049] S6. Sum and merge the contributions of the same observed precursor and all its intermediate oxidation products to PAN formation, and calculate the contribution amount and contribution rate of each observed precursor to PAN formation at the target time.

[0050] For any observed precursor A, the set of all oxidation products generated by it in the observation constraint box model is identified. If precursor A can directly generate peroxyacetyl radicals or directly participate in the PAN generation chain, then the direct contribution is included in precursor A. If precursor A generates oxidation products A1, A2, A3, etc., through one or more generations of oxidation, and these oxidation products further generate peroxyacetyl radicals and eventually generate PAN, then the contributions of A1, A2, A3, etc., to PAN generation are all attributed to precursor A.

[0051] By merging the data, the total contribution of precursor A to PAN formation at the target time is calculated. This total contribution equals the sum of the contributions of precursor A itself and all downstream products formed by the oxidation of precursor A to PAN formation. This merging process avoids the problem of only counting direct precursors while ignoring the contributions of upstream VOCs, and also avoids the problem of dispersing the contributions of different oxidation products of the same precursor in the statistics.

[0052] Furthermore, for each target time t, the contribution of all observed precursors to PAN formation is calculated. The contribution of the i-th precursor to PAN formation is the sum of the contribution fluxes of the precursor and all its oxidation products to the PAN formation reaction through all effective pathways. The sum of the contributions of each precursor matches the PAN formation flux at the target time. The contribution rate of the i-th precursor to PAN formation is then calculated, which is the ratio of the contribution of the i-th precursor to the sum of the contributions of all precursors.

[0053] The final calculation results are output in the following format: contribution amount, contribution rate, contribution ranking, main reaction pathway, main oxidation products, generational tracing relationship, and uncertainty range of different precursors to PAN formation at each target time. For continuous observation periods, minute-level or hour-level time series of PAN precursor contributions can be generated, and the average contribution during daytime, nighttime, pollution peak period, clean period, different air masses, or different seasons can be further calculated.

[0054] Specifically, contribution merging employs a path-based source identification method, where each contributing flux is labeled with its source precursor identifier, automatically aggregating all path contributions for the same precursor. Contribution rate calculations are retained to 1–2 decimal places, and contributions are sorted from highest to lowest contribution rate. Time-series results are stored according to the observation time sequence, supporting multi-period statistical filtering. This step, through full-path contribution merging, unifies the dispersed contributions of multiple generations of oxidation products of the same precursor, restores the true contribution ratio of upstream VOCs, and outputs high-temporal-resolution quantitative results, providing precise species prioritization for pollution control.

[0055] S7. Verify the calculated contribution and contribution rate of each observed precursor to PAN generation at the target time.

[0056] The reconstructed PAN generation flux is obtained by summing the contributions of each observed precursor. The reconstructed PAN generation flux is then compared with the PAN generation flux recorded by the observation constraint box model and the chemical generation rate of PAN at the target time. If the deviation of the comparison results is lower than the preset threshold, the calculated contribution of each precursor to PAN generation and the contribution rate are valid and verified. The preset threshold is 5% to 30%, preferably 10% to 20%.

[0057] If the deviation of any comparison result exceeds the preset threshold, the missing observed species, free radical constraints, transport corrections, boundary layer dilution terms, or reaction branch parameters will be checked and recalculated.

[0058] During the results verification process, uncertainties can be further output. Uncertainties can be obtained through the propagation of instrument observation errors, reaction rate constant errors, branching yield errors, free radical estimation errors, transport correction errors, and data time resolution errors.

[0059] Specifically, the deviation is calculated using the relative deviation formula: |Reconstructed flux − Baseline flux| / Baseline flux × 100%. The verification order prioritizes comparing the reconstructed flux with the model flux, followed by comparison with the mass conservation rate. Uncertainty is addressed using the Monte Carlo error propagation method, introducing ±5%–±20% random perturbations to key parameters and outputting a 95% confidence interval. This step, through three-flux cross-validation and error quantification, ensures the reliability and robustness of the contribution results, quantifies the uncertainty range, and enhances the credibility and applicability of the results in pollution control decision-making.

[0060] Example 1: Quantifying the contribution of precursors to PAN formation in urban summer afternoons based on the method of the present invention. The specific process includes: Continuous summer observations were conducted at an atmospheric composite pollution observation station in a certain city, with an observation time resolution of 1 hour. The observed species included PAN, NO, NO2, O3, VOCs, carbonyl compounds, photolysis frequency, temperature, humidity, air pressure, wind speed, and boundary layer height. 14:00 on a certain day was selected as the target time, and the observation constraint box model was input for observation constraint simulation. The model recorded the PAN generation flux, PAN thermal decomposition flux, peroxyacetyl radical generation flux, and VOCs oxidation product generation flux at 14:00.

[0061] At that moment, the concentrations of PAN, NO2, O3, acetaldehyde, acetone, methylglyoxal, and methylglyoxal were 1.80 ppbv, 12.0 ppbv, 145 ppbv, 4.5 ppbv, 6.8 ppbv, and 0.65 ppbv, and the temperature was 32°C.

[0062] The observation constraint box model calculated the PAN generation flux to be 1.10 ppbv / h. Starting from this PAN generation flux, the source of peroxyacetyl radicals was traced backward and then back to the observed precursors.

[0063] The calculation results show that acetaldehyde and its related oxidation pathways contribute 34% to PAN formation, acetone and its oxidation pathways contribute 18%, methylglyoxal contributes 15%, toluene and xylene oxidation products contribute 13%, isoprene oxidation products contribute 8%, and other precursors and incompletely resolved pathways contribute 12%.

[0064] Based on this embodiment, the present invention can obtain the contribution of different precursors to PAN generation at a certain time under the constraint of real observation.

[0065] Example 2: Quantification of the contribution of PAN precursors to the influence of aromatic hydrocarbons in industrial parks based on the method of the present invention, specifically including: Ten-minute resolution observations were conducted at a site in an industrial park. A rapid increase in PAN was observed between 10:30 AM and 11:30 AM. 11:00 AM was selected as the target time for analysis. The observation data included PAN, NO2, O3, toluene, xylene, acetaldehyde, acetone, methylglyoxal, photolysis frequency, and meteorological parameters.

[0066] Input the above data into the observation constraint box model to record the PAN generation and related VOCs oxidation process at that moment.

[0067] At 11:00, the concentrations of PAN were 2.35 ppbv, NO2 was 18.5 ppbv, toluene was 8.2 ppbv, m / p xylene was 5.1 ppbv, acetaldehyde was 3.2 ppbv, and methylglyoxal was 1.1 ppbv.

[0068] The observational constraint box model calculated the PAN generation flux to be 1.65 ppbv / h. Backtracking revealed that aromatic hydrocarbons are first oxidized to form various carbonyl and dicarbonyl intermediates, which further generate peroxyacetyl radicals and participate in PAN generation.

[0069] After merging the contributions of aromatic hydrocarbons and all their related oxidation products, the contribution rate of aromatic hydrocarbon-related precursors to PAN formation is 39%; the contribution rate of the acetaldehyde pathway is 22%; the contribution rate of the methylglyoxal pathway is 17%; the contribution rate of the acetone pathway is 9%; and the contribution rate of other VOCs pathways is 13%.

[0070] Based on this embodiment, the present invention can attribute the contribution of multi-generation oxidation products of aromatic hydrocarbons to PAN formation to aromatic hydrocarbon precursors, and is applicable to the analysis of PAN formation contribution in complex VOCs environments in industrial areas.

[0071] Example 3: Quantification of the contribution of PAN precursors under the influence of biogenic VOCs based on the method of the present invention, specifically including: Summer observations were conducted at a suburban forest edge station with a time resolution of 30 minutes. The analysis was performed at 13:30 during the afternoon when there was strong sunlight. The observation data included PAN, NO2, O3, isoprene, MVK, MACR, acetaldehyde, acetone, temperature, humidity, and photolysis frequency.

[0072] The observation data were input into the observation constraint box model, and the isoprene oxidation process, MVK and MCR generation and consumption process, peroxyacetyl radical generation process, and PAN generation process were recorded.

[0073] At 13:30, the concentrations were: PAN 1.25 ppbv, NO2 6.5 ppbv, O3 118 ppbv, isoprene 2.7 ppbv, MVK and MACR combined 1.4 ppbv, acetaldehyde 2.8 ppbv, and temperature 34℃.

[0074] The observational constraint box model calculated the PAN generation flux to be 0.82 ppbv / h. Backtracking revealed that MVK, MACR and their subsequent oxidation products formed by isoprene oxidation can generate peroxyacetyl radicals, which ultimately contribute to PAN generation.

[0075] The combined results show that the acetaldehyde pathway contributes 31% to PAN formation, isoprene and its oxidation products contribute 28%, the acetone pathway contributes 12%, the aromatic hydrocarbon oxidation pathway contributes 10%, and other pathways contribute 19%.

[0076] Based on this embodiment, the present invention can identify the contribution of bio-derived VOCs to PAN formation through multiple generations of oxidation.

[0077] Advantages of this invention: This invention directly uses minute-level or hour-level real atmospheric observation data to constrain the observation constraint box model, making the reaction process simulation consistent with the actual atmospheric environment. This avoids the influence of idealized emission scenarios or emission inventory uncertainties on the PAN contribution analysis results, and significantly improves the authenticity and reliability of the results.

[0078] This invention starts from the PAN formation reaction and traces back along the reaction network to the source of peroxyacetyl radical, intermediate oxidation products and upstream observed precursors. It can connect PAN, radical, oxidation products and precursors into a complete generation-by-generation reaction chain, revealing the actual chemical source of PAN formation, rather than response analysis based on perturbations.

[0079] This invention does not rely on precursor sensitivity perturbations, emission scenario reductions, or source class labeling methods. The results are not scenario responses or label contributions, but rather precursor generation contributions based on actual reaction fluxes and reaction networks, thus possessing objectivity, reproducibility, and verifiability.

[0080] This invention sums and merges the contributions of the same observed precursor and all its oxidation products (including multi-generation oxidation products) to PAN formation, solving the technical problem of dispersed contributions from intermediate products and difficulty in attributing contributions from upstream precursors during PAN formation, and achieving complete quantification of precursor contributions.

[0081] This invention can generate a high temporal resolution time series of PAN precursor contributions, which is suitable for analyzing pollution peak times, rapid formation processes, afternoon photochemical processes, and dynamic changes under the influence of different air masses, thus making up for the low temporal resolution of traditional methods.

[0082] This invention can simultaneously output the contribution amount, contribution rate, contribution ranking, main reaction pathway, main oxidation products, and generational tracing relationship of precursors, providing direct technical basis for atmospheric photochemical pollution diagnosis, key VOCs species identification, and synergistic control of PAN and ozone.

[0083] This invention is not only applicable to PAN, but can also be extended to other peroxyacyl nitrate components such as PPN and MPAN; it is not only applicable to single-site observations, but can also be used for mobile observations, regional background station observations, urban cluster integrated observations, and field enhanced observations, and has broad application prospects.

[0084] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for quantifying PAN precursor contributions based on constraints from real atmospheric observations, characterized in that, include: Acquire real atmospheric observation data of the target area or target observation station. The observation data includes PAN concentration, nitrogen oxide concentration, O3 concentration, VOCs concentration, carbonyl compound concentration, photolysis frequency and meteorological parameters, with a time resolution of 1 minute to 1 hour. The observation data is preprocessed to construct an observation constraint vector that varies over time; an observation constraint box model is established that includes the mechanisms of PAN generation and decomposition, VOCs multigenerational oxidation, carbonyl compound conversion, free radical cycling, and nitrogen oxide conversion; and the observation constraint vector is input into the observation constraint box model. The observation data is input and used to drive the observation constraint box model to record the reaction flux, reaction rate and upstream and downstream generation relationship data at each moment, forming a dynamic reaction network. Based on the PAN mass conservation relationship, the PAN concentration change rate is calculated using the time series of observed PAN concentrations. Combined with the thermal decomposition loss calculated by the observation constraint box model, the chemical generation rate or generation flux of PAN at the target time is determined. Starting with the PAN generation reaction, based on the recorded data, the source of peroxyacetyl radical and intermediate oxidation products are traced back along the dynamic reaction network to the upstream observed precursors. According to the proportion of reaction flux in each path, the contribution of PAN generation is allocated to each observed precursor based on the chemical generation rate or generation flux of PAN at the target time. The contributions of the same observed precursor and all its intermediate oxidation products to PAN formation are summed and merged to calculate the contribution amount and contribution rate of each observed precursor to PAN formation at the target time.

2. The PAN precursor contribution quantification method based on real atmospheric observation constraints according to claim 1, characterized in that: The observation data also includes one or more of the following: carbon monoxide concentration, sulfur dioxide concentration, particulate matter concentration, solar or ultraviolet radiation intensity, wind speed, wind direction, and boundary layer height.

3. The PAN precursor contribution quantification method based on real atmospheric observation constraints according to claim 1, characterized in that, The VOCs include one or more of the following: alkanes, alkenes, alkynes, aromatic hydrocarbons, halogenated hydrocarbons, oxygen-containing volatile organic compounds, and bio-derived volatile organic compounds; The carbonyl compound includes one or more of formaldehyde, acetaldehyde, propionaldehyde, acetone, glyoxal, and methylglyoxal.

4. The PAN precursor contribution quantification method based on real atmospheric observation constraints according to claim 1, characterized in that: The dynamic reaction network is recorded in the form of a reaction flux matrix F(t), where the matrix elements F... ij (t) represents the reaction flux at target time t from species i to species j or subsequent products.

5. The PAN precursor contribution quantification method based on real atmospheric observation constraints according to claim 1, characterized in that: The thermal decomposition loss is calculated by the observation constraint box model based on the temperature, pressure, and PAN concentration at the target time, combined with the PAN thermal decomposition reaction rate constant.

6. The PAN precursor contribution quantification method based on real atmospheric observation constraints according to claim 1, characterized in that, Determining the chemical formation rate of PAN at the target time also includes: Based on the boundary layer height change rate, wind speed, and wind direction in meteorological observation data, a transport dilution correction term is determined, and the transport dilution correction term is incorporated into the mass conservation relationship to correct the chemical formation rate of PAN at the target time.

7. The PAN precursor contribution quantification method based on real atmospheric observation constraints according to claim 1, characterized in that: During the reverse tracing process, based on the reaction flux recorded by the observation constraint box model, all reaction pathways that generate peroxyacetyl radicals at the target time are identified, including one or more of the following: acetaldehyde oxidation pathway, acetone photolysis or oxidation pathway, methylglyoxal oxidation pathway, glyoxal oxidation pathway, aromatic hydrocarbon oxidation product pathway, olefin oxidation product pathway, and biogenic VOCs oxidation product pathway.

8. The PAN precursor contribution quantification method based on real atmospheric observation constraints according to claim 1, characterized in that, The reverse tracing method adopts a generational recursive approach: The first generation is the direct PAN generation layer, the second generation is the peroxyacetyl radical source layer, the third generation is the generation layer of peroxyacetyl radical source species, and the fourth generation and above are multi-generation oxidation layers from upstream volatile organic compounds or carbonyl compounds to generate their oxidation products. Each generation determines its contribution allocation ratio based on the actual reaction flux recorded by the observation constraint box model.

9. The PAN precursor contribution quantification method based on real atmospheric observation constraints according to claim 1, characterized in that, It also includes a result verification step: The contribution of each observed precursor is summed to obtain the reconstructed PAN generation flux. The reconstructed PAN generation flux is compared with the chemical generation rate of PAN at the target time. If the deviation is lower than a preset threshold, the calculated contribution of each precursor to PAN generation and the contribution rate result are valid and verified. The preset threshold is 5% to 30%.

10. The PAN precursor contribution quantification method based on real atmospheric observation constraints according to claim 1, characterized in that, The output formats of the contribution amount and contribution rate include: The contribution of different observed precursors to PAN formation at each target time point, contribution rate, contribution ranking, main reaction pathway, main oxidation products, and generational tracing relationship; For continuous observation periods, time series of PAN precursor contributions are generated at the minute or hour level.

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