A method and system for simulating the formation of particulate nitrate based on an improved OBM

Abstract

This invention discloses an improved OBM-based method and system for simulating particulate nitrate formation, belonging to the field of computer-aided simulation technology. The method includes: constructing an observational constraint input dataset containing temperature, relative humidity, NO and NO2, photolysis frequency, particulate matter observation data, and NH3 concentration; inputting this dataset into an observational constraint box model coupled with gas-phase chemical mechanisms to simulate gaseous precursor species such as HNO3 and N2O5; calculating the aerosol surface area concentration based on particulate matter observation data; coupling a dynamic particulate nitrate formation module into the model to establish the daytime HNO3 gas-particle conversion pathway and the nighttime N2O5 heterogeneous hydrolysis pathway, dynamically determining the conversion or hydrolysis rate, and outputting the simulated particulate nitrate concentration. Through the technical solution of this invention, the diurnal formation process of particulate nitrate can be characterized based on OBM gas-phase simulation, improving the simulation accuracy of diurnal variations, especially the diurnal rise process.

Description

Technical Field

[0001] This invention relates to the field of computer-aided simulation technology, and in particular to an improved OBM-based simulation method for particulate nitrate formation and an improved OBM-based simulation system for particulate nitrate formation. Background Technology

[0002] Particulate nitrate (NA) is a major component of urban atmospheric PM2.5 during winter. 2.5 Nitrate is an important secondary inorganic component in the atmosphere, and its formation process is typically influenced simultaneously by gas-phase photochemical reactions, gas-to-particle conversion processes, and nighttime heterogeneous reactions. During the day, atmospheric NO2 can generate gaseous HNO3 under the action of OH radicals. Under certain conditions of relative humidity, temperature, NH3 concentration, and aerosol surface area, HNO3 further transforms into the particulate phase, forming particulate nitrates. At night, NO2 reacts with O3 to generate NO3 radicals, which further react with NO2 to form N2O5. N2O5 can undergo heterogeneous hydrolysis on the aerosol surface to generate nitrates. Therefore, the diurnal variation of particulate nitrate concentration depends not only on the formation of gas-phase precursor species but also on aerosol surface area, relative humidity, NH3 neutralization capacity, and temperature conditions.

[0003] Existing observation-based model (OBM) models typically use measured meteorological parameters, common gaseous pollutants, VOCs, HONO, and photolysis frequencies as observational constraint inputs, and are coupled with gas-phase chemical mechanisms such as MCM (Master Chemical Mechanism) to simulate key gas-phase species such as OH, HO2, RO2, NO3, N2O5, and HNO3. These models can describe gas-phase photochemical processes with relatively high precision and are suitable for ozone generation, free radical cycling, and NO... x While it analyzes the conversion process, it primarily focuses on gas-phase chemical simulation and typically lacks a dedicated module for the dynamic generation of particulate nitrates, making it difficult to directly output the dynamic simulated concentration of particulate nitrates.

[0004] Existing inorganic aerosol thermodynamic equilibrium models can calculate the equilibrium distribution relationships of gaseous particles such as HNO3, NH3, and NH4NO3 based on temperature, relative humidity, and inorganic ionic composition. However, these models typically focus on equilibrium distribution and cannot directly characterize the distribution caused by VOCs photochemistry, OH radicals, and NO in observation-constrained box models. x The dynamic HNO3 generation process driven by the cycle is also difficult to directly form a process diagnosis of the diurnal nitrate generation pathway by combining it with the results of OBM gas phase simulation.

[0005] Furthermore, while three-dimensional air quality models or Earth system models can simulate the regional spatial distribution of nitrate aerosols, they typically rely on emission inventories, meteorological forcing, boundary conditions, vertical mixing, and regional transport processes, resulting in high computational complexity. This makes them unsuitable for direct diagnosis of deviations in diurnal variations of particulate nitrate under single-site or local high temporal resolution observation conditions. For the diurnal rise in particulate nitrate concentrations commonly seen in urban atmospheres during winter, existing OBMs (On-Balance Models) often only simulate gaseous HNO3 without dynamically coupling its conversion to particulate nitrate with aerosol surface area, relative humidity, NH3 concentration, and temperature. This can easily lead to a discrepancy where the simulated diurnal concentration decreases while the observed concentration increases. Summary of the Invention

[0006] To address the aforementioned problems, this invention provides an improved OBM-based simulation method and system for particulate nitrate formation. By coupling a dynamic particulate nitrate formation module into the observation constraint box model, this application can further convert the HNO3 and N2O5 output from the original OBM gas-phase photochemical simulation into particulate nitrate simulation results. By introducing the daytime HNO3 gas-to-particle conversion pathway and the nighttime N2O5 heterogeneous hydrolysis pathway, and making the daytime conversion rate dynamically change with aerosol surface area concentration, relative humidity, NH3 concentration, and temperature, this invention can overcome the shortcomings of existing OBM models that only focus on gas-phase chemistry and are difficult to characterize particulate nitrate formation. This improves the model's simulation accuracy and physical interpretability for the diurnal variation process of particulate nitrate, especially the daytime concentration increase process.

[0007] To achieve the above objectives, this invention provides a method for simulating the formation of particulate nitrates based on an improved OBM (Original Material Manufacturing) method, comprising: Construct an observation constraint input dataset for the target observation area or observation station. The observation constraint input dataset shall include at least temperature, relative humidity, gaseous pollutants including at least NO and NO2, photolysis frequency, particulate matter observation data, and NH3 concentration. The observation constraint input dataset is input into an observation constraint box model coupled with a gas phase chemical mechanism to simulate gas phase precursor species related to the formation of particulate nitrate, wherein the gas phase precursor species include at least HNO3 and N2O5. Calculate the aerosol surface area concentration based on the particulate matter observation data; The observation constraint box model is coupled with a dynamic particulate nitrate generation module, which includes a gas-particle conversion pathway for the conversion of HNO3 to particulate nitrate during the day and a heterogeneous reaction pathway for the heterogeneous hydrolysis of N2O5 to generate particulate nitrate at night. The conversion rate of HNO3 to particulate nitrate during the day is determined based on the effective uptake coefficient of HNO3, the average thermal rate of HNO3 molecules, the aerosol surface area concentration, relative humidity, NH3 concentration, and temperature dynamics. The hydrolysis rate of N2O5 to particulate nitrate at night is determined based on the N2O5 uptake coefficient, the average thermal rate of N2O5 molecules, and the aerosol surface area concentration. Run the observation constraint box model coupled with the dynamic generation module of particulate nitrate to output the simulated concentration of particulate nitrate.

[0008] In the above technical solution, preferably, the meteorological parameters in the observation constraint input dataset further include at least one of air pressure and boundary layer height, the gaseous pollutants further include at least one of O3, CO, SO2, HONO, and VOCs, and the particulate matter observation data includes PM2.5. 2.5 Particulate NO3 - NH4 + SO4 2- Cl - Na + K + Ca 2+ and Mg 2+ At least one of them; When constructing the observation constraint input dataset, the observation data is subjected to at least one of the following processing methods: time alignment, outlier removal, missing value handling, unit conversion, and quality control.

[0009] In the above technical solution, preferably, the gas phase chemical mechanism adopts the master chemical mechanism (MCM); the observation constraint box model uses O3, NO, NO2, CO, SO2, HONO, VOCs, temperature, relative humidity, air pressure and photolysis frequency as constraint inputs to simulate N2O5 and HNO3, and simulate at least one key species among OH, HO2, RO2, O3 and NO3.

[0010] In the above technical solution, preferably, calculating the aerosol surface area concentration based on the particulate matter observation data includes: When the particulate matter observation data includes particle size distribution data, the aerosol surface area concentration is calculated according to the following formula: ; in, D p,i For the first i Particle diameter in each particle size range N i For the first i Particle number concentration for each particle size range; When the particulate matter observation data does not include particle size distribution data but includes PM2.5 When the mass concentration is used, the aerosol surface area concentration is estimated using the following formula: ; in, ρ For particulate matter density, D p The equivalent particle size.

[0011] In the above technical solution, preferably, the heterogeneous reaction pathway for the formation of particulate nitrate by the heterogeneous hydrolysis of N2O5 at night is represented in the reaction mechanism as N2O5→2HNO3, or in the particulate nitrate tracer simulation as N2O5→2NA, where NA represents the particulate nitrate tracer. The hydrolysis rate constant for the formation of particulate nitrates from the heterogeneous hydrolysis of N2O5 at night is determined according to the following formula: ; Wherein, γN₂O₅ is the N₂O₅ uptake coefficient, cN₂O₅ is the average thermal rate of N₂O₅ molecules, and AERO SA This represents the aerosol surface area concentration.

[0012] In the above technical solution, preferably, the conversion rate of daytime HNO3 to particulate nitrate is determined by the original collision uptake rate of HNO3 and then corrected to an effective conversion rate. The initial collisional uptake rate of HNO3 is determined according to the following formula: ; Wherein, γHNO3 is the effective uptake coefficient of HNO3, cHNO3 is the average thermal rate of HNO3 molecules, and AERO SA This refers to the aerosol surface area concentration. The effective conversion rate is determined according to the following formula: ; Where fRH is the relative humidity correction factor, fNH3 is the ammonia correction factor, and fT is the temperature correction factor.

[0013] In the above technical solution, preferably, the relative humidity correction factor fRH is determined according to fRH=(RH-35) / (85-35) and limited to the range of 0 to 1; The ammonia correction factor fNH3 is according to Confirmed, among which The half-saturation parameter of ammonia gas characterizing the neutralization capacity of NH3; The temperature correction factor fT is determined according to fT=exp[-0.06×(T-273.15)] and is limited to the range of 0.2 to 3.0; The effective conversion rate A rate limit is set to prevent the non-physical rapid loss of HNO3.

[0014] In the above technical solution, preferably, the particulate nitrate dynamic generation module further includes a reverse conversion pathway for the volatilization of particulate nitrate to HNO3, and the volatilization rate of the reverse conversion pathway is determined according to the following formula: KNAHNO3=Kbase×fDry×fWarm×(1-fNH3); Wherein, Kbase is the basic anti-volatilization rate, fDry is the drying correction factor, fWarm is the temperature correction factor, and fNH3 is the ammonia correction factor; The drying correction factor fDry = 1 - fRH, and the heating correction factor fWarm = exp[0.08 × (T - 273.15)] are limited to the range of 0.2 to 5.0.

[0015] In the above technical solution, preferably, after running the observation constraint box model coupled with the particulate nitrate dynamic generation module, at least one diagnostic variable is output from the following: daytime HNO3 conversion contribution, nighttime N2O5 hydrolysis contribution, HNO3 gas phase generation rate, daytime effective conversion rate of HNO3 to particulate nitrate, nighttime N2O5 heterogeneous hydrolysis to particulate nitrate hydrolysis rate constant, relative humidity correction factor, ammonia correction factor, temperature correction factor, and aerosol surface area concentration. The simulated concentration of particulate nitrate is compared with the observed concentration of particulate nitrate, and at least one of the following evaluation indicators is calculated: simulation bias, mean absolute error, root mean square error, correlation coefficient, and diurnal concentration change slope, to evaluate the simulation effect of the observation constraint box model on the diurnal variation process and diurnal concentration rise process of particulate nitrate.

[0016] This invention also proposes an improved particulate nitrate formation simulation system based on OBM, comprising: The observation data construction module is used to construct the observation constraint input dataset for the target observation area or observation station. The observation constraint input dataset includes at least temperature, relative humidity, gaseous pollutants including at least NO and NO2, photolysis frequency, particulate matter observation data, and NH3 concentration. The gas phase simulation module is used to input the observation constraint input dataset into an observation constraint box model coupled with gas phase chemical mechanisms to simulate gas phase precursor species related to the formation of particulate nitrates. The gas phase precursor species include at least HNO3 and N2O5. The aerosol surface area calculation module is used to calculate the aerosol surface area concentration based on the particulate matter observation data. The nitrate dynamic generation module is used to couple the dynamic generation process of particulate nitrate, which includes the gas-particle conversion pathway of HNO3 to particulate nitrate during the day and the heterogeneous reaction pathway of N2O5 to particulate nitrate at night, to the observation constraint box model. The conversion rate of HNO3 to particulate nitrate during the day is dynamically determined based on the effective uptake coefficient of HNO3, the average thermal rate of HNO3 molecules, the aerosol surface area concentration, relative humidity, NH3 concentration, and temperature. The hydrolysis rate of N2O5 to particulate nitrate at night is determined based on the N2O5 uptake coefficient, the average thermal rate of N2O5 molecules, and the aerosol surface area concentration. The output module is used to run the observation constraint box model coupled with the particulate nitrate dynamic generation module and output the simulated concentration of particulate nitrate.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) By coupling the dynamic generation module of particulate nitrate in the observation constraint box model, the OBM can output the simulated concentration of particulate nitrate on the basis of simulating gaseous precursor species such as HNO3 and N2O5. This can make up for the fact that the traditional OBM mainly focuses on gaseous photochemical processes and is difficult to directly characterize the dynamic generation of particulate nitrate.

[0018] (2) By setting up the gas-particle conversion pathway of HNO3 to particulate nitrate during the day, and making the conversion rate determined by the effective uptake coefficient of HNO3, the average thermal rate of HNO3 molecules, the surface area concentration of aerosol, relative humidity, NH3 concentration and temperature, the model can reflect the process of HNO3 entering the particulate phase after photochemical generation due to the influence of humidity, ammonia neutralization capacity and temperature stability, thereby improving the simulation effect of the daytime nitrate concentration rise process.

[0019] (3) By setting the reaction pathway for the heterogeneous hydrolysis of N2O5 to generate particulate nitrate at night, and determining the hydrolysis rate by the N2O5 uptake coefficient, the average thermal rate of N2O5 molecules and the aerosol surface area concentration, the model can simultaneously describe the nitrate generation process at night, thereby improving the ability to characterize the diurnal variation pathway of particulate nitrate.

[0020] (4) Calculate the aerosol surface area concentration based on particulate matter observation data so that HNO3 conversion and N2O5 hydrolysis are both constrained by the actual particulate matter surface area conditions, thereby enhancing the correspondence between simulation parameters and observation environment and improving the physical rationality of model simulation results.

[0021] (5) By outputting the daytime HNO3 conversion contribution, the nighttime N2O5 hydrolysis contribution, various correction factors and model evaluation indicators, it is helpful to identify the effects of humidity promotion, NH3 limitation, temperature regulation and aerosol surface area on the formation of particulate nitrate, and provide a basis for the analysis of the causes of urban nitrate pollution in winter and the formulation of control strategies. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the technical route of a particulate nitrate generation simulation method based on OBM improvement disclosed in one embodiment of the present invention; Figure 2 This is a schematic diagram of the day-night path of particulate nitrates disclosed in one embodiment of the present invention. Detailed Implementation

[0023] 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.

[0024] The present invention will now be described in further detail with reference to the accompanying drawings: like Figure 1 As shown, a method for simulating particulate nitrate formation based on OBM improvement according to the present invention includes: Construct an observation constraint input dataset for the target observation area or observation station. This dataset should include at least temperature, relative humidity, gaseous pollutants including at least NO and NO2, photolysis frequency, particulate matter observation data, and NH3 concentration. This ensures that subsequent models can obtain NO... x The gas-phase confinement required for photochemical conversion can also provide the humidity, temperature, NH3, and particulate matter conditions necessary for the conversion of HNO3 to particulate nitrate. Among these, NO and NO2 are involved in the conversion of NO... x The chemical composition of the aerosols determines the core species responsible for the formation rates of NO3 radicals and N2O5; the photolysis frequency determines the photochemical driving force, directly affecting the formation rate of OH radicals; NH3 concentration is the key neutralizing gas controlling the conversion of HNO3 to particulate matter; and particulate matter observation data is used for subsequent aerosol surface area calculations. The establishment of this minimum dataset ensures that the method can still operate even under limited observation conditions.

[0025] The observation-constrained input dataset is fed into an observation-constrained box model (OBM) coupled with gas-phase chemical mechanisms. Under observation-constrained conditions, gas-phase precursor species related to particulate nitrate formation are simulated. These gas-phase precursor species include at least HNO3 and N2O5. HNO3 is a direct product of the daytime OH+NO2 oxidation pathway and a direct precursor to the gas-to-particle conversion of NH4NO3. N2O5 is a product of the combination of NO3 radicals and NO2 at night and a precursor to the heterogeneous hydrolysis of nitrates at night. Accurate simulation of these two key gas-phase precursors by the OBM is a prerequisite for the accurate parameterization of the two nitrate formation pathways during the day and night.

[0026] The aerosol surface area concentration is calculated based on particulate matter observation data. This aerosol surface area concentration serves as a particle surface constraint parameter for HNO3 collisional uptake and N2O5 heterogeneous hydrolysis, and is a key physical parameter for the heterogeneous reaction rate, characterizing the effective contact area between gas molecules and the particulate matter surface. A higher aerosol surface area concentration corresponds to a faster heterogeneous reaction rate. In this embodiment, the aerosol surface area concentration is calculated from particulate matter observation data, replacing a fixed, assumed value with actual observational constraints, enabling the model to dynamically respond to changes in particulate matter concentration.

[0027] Subsequently, a dynamic particulate nitrate generation module was coupled into the observation constraint box model. The dynamic particulate nitrate generation module includes two parallel particulate nitrate generation pathways: one is the gas-particle conversion pathway of HNO3 to particulate nitrate during the day, and the other is the heterogeneous reaction pathway of N2O5 to generate particulate nitrate at night. This allows HNO3 to generate particulate nitrate during the day through the gas-particle conversion pathway, and N2O5 to generate particulate nitrate at night through the heterogeneous hydrolysis pathway.

[0028] The daytime conversion rate of HNO3 to particulate nitrate is determined dynamically based on the effective uptake coefficient of HNO3, the average thermal rate of HNO3 molecules, aerosol surface area concentration, relative humidity, NH3 concentration, and temperature, covering all key atmospheric factors affecting HNO3 particulate formation. The nighttime hydrolysis rate of N2O5 to particulate nitrate is determined based on the N2O5 uptake coefficient, the average thermal rate of N2O5 molecules, and aerosol surface area concentration, reflecting the kinetic mechanism of N2O5 hydrolysis on the aerosol surface at night. The simultaneous introduction of these two pathways allows the model to fully describe the diurnal and contemporaneous contributions of particulate nitrate, overcoming the limitation of existing OBMs which can only simulate the gas phase and cannot provide data on particulate NO3. - The fundamental flaw of dynamic concentration.

[0029] The observation constraint box model, after running the coupled particulate nitrate dynamic generation module, outputs the simulated concentration of particulate nitrate.

[0030] In this embodiment, the particulate nitrate generation process can be further established on the basis of the original OBM gas phase photochemical simulation, so that the model can output the simulated concentration of particulate nitrate instead of just gas phase precursors such as HNO3 and N2O5. This improves the problem that traditional OBM is difficult to reproduce the rise in urban daytime nitrate concentration in winter.

[0031] In the above embodiments, preferably, the meteorological parameters in the observation constraint input dataset also include at least one of air pressure and boundary layer height. Air pressure is used to accurately calculate the concentration of gas molecules (converting the volume mixing ratio into molecular number density) and affects the absolute temperature-pressure relationship in the average thermal rate of gas molecules. Boundary layer height reflects the diffusion and dilution capacity of the near-surface atmosphere and can be used to diagnose the relative strength of nitrate accumulation and diffusion. It is an important supplementary parameter for understanding the physical mechanism of diurnal concentration variation.

[0032] In addition to NO and NO2, gaseous pollutants include at least one of O3, CO, SO2, HONO, and VOCs. O3 is a direct oxidant of NO3 radicals and is crucial for the formation of N2O5 at night. CO is an important consuming species of OH radicals, limiting the concentration level of free radicals. SO2 participates in gas-phase and liquid-phase oxidation processes, affecting the acidity and alkalinity of aerosols. HONO is an important non-photolytic source of OH radicals during the day, significantly contributing to daytime photochemical intensity. VOCs are core precursors of OH radicals and RO2 radicals, determining gas-phase oxidation capacity and the rate of HNO3 formation. The constraint input of these species makes OBM gas-phase chemical simulations more accurate, thereby improving the simulation accuracy of HNO3 and N2O5.

[0033] Particulate matter observation data includes PM 2.5 Particulate NO3 - NH4 + SO4 2- Cl - Na + K + Ca 2+ and Mg 2+ At least one of them; wherein, PM 2.5 Used to estimate aerosol surface area concentration when particle size distribution data is unavailable; particulate NO3 - NH4 was used directly as the baseline observation for model validation. + SO4 2- Inorganic ionic components reflect the acid-base state and ion balance of aerosols, and can help diagnose whether the supply of NH3 is sufficient to support the conversion of HNO3 to NH4NO3.

[0034] When constructing the observation constraint input dataset, time alignment, outlier removal, missing value handling, unit conversion, and quality control can be performed on data from different instruments, with different time resolutions, and in different units, so that various types of observation data can be used as OBM constraint inputs on the same time axis. Specifically, time alignment unifies data from different instruments and with different time resolutions to the same time axis; outlier removal removes outliers caused by instrument malfunctions or calibration deviations; missing value handling ensures the continuity of model operation through interpolation or filling with preceding and following values; and unit conversion unifies the concentrations of various species to the standard units required for model calculation. For example, gaseous pollutants, particulate matter components, and meteorological data can be unified to an hourly scale, instrument malfunction values ​​can be removed, and short-term missing data can be appropriately interpolated.

[0035] In this implementation, the integrity and consistency of the observation constraint input dataset can be improved, providing a reliable data foundation for subsequent processes such as HNO3 generation, N2O5 formation, NH3 neutralization, humidity promotion, and particulate matter surface area constraint, thereby improving the credibility of the particulate nitrate simulation results.

[0036] like Figure 2 As shown, in the above embodiments, preferably, the gas-phase chemical mechanism employs the master chemical mechanism (MCM). MCM is one of the most detailed explicit gas-phase chemical mechanisms to date, encompassing thousands of VOC oxidation pathways and capable of precisely describing the complete reaction chain from primary VOC emissions to final oxidation products. Using MCM enables OBM to progressively track OH, HO2, RO2, and NO. x The chemical evolution, in the composition of various VOCs and NO x Both methods can accurately describe the generation flux of OH + NO2 → HNO3 and the nighttime chain reaction of NO + O3 → NO2 → NO3 → N2O5, providing a reliable gas-phase chemical basis for the precursor concentrations of the two particulate nitrate generation pathways.

[0037] The observation-constrained box model uses O3, NO, NO2, CO, SO2, HONO, VOCs, temperature, relative humidity, air pressure, and photolysis frequency as constraint inputs. These species directly drive the model based on observations, with chemical integration only applied to unconstrained species (such as OH, HO2, RO2, NO3, N2O5, and HNO3), thus avoiding model dependence on emission inventories and meteorological models. Among these, the photolysis frequency directly determines the photochemical driving intensity and has a decisive impact on the simulation accuracy of daytime OH generation and HNO3 formation rates. Including O3 and NO... x Simultaneous constraints ensure that OBM accurately characterizes the NO3 free radical generation rate during different pollution periods.

[0038] In terms of simulated species, OBM simulates N2O5 and HNO3, and at least one key species among OH, HO2, RO2, O3, and NO3. OH is the core free radical driving daytime gas-phase oxidation, and its simulation accuracy directly affects the HNO3 formation rate; HO2 and RO2 participate in the free radical cycle and NO3. x The regeneration process; NO3 is a direct precursor to N2O5 formation at night, and its concentration level determines the intensity of the heterogeneous hydrolysis pathway at night. Accurate simulation of these key species ensures that the dynamic particulate nitrate generation module can obtain accurate gas-phase input.

[0039] In this process, NO2 reacts with OH to generate HNO3, NO2 reacts with O3 to form NO3 free radicals, and NO3 further reacts with NO2 to form N2O5, thus providing a gaseous precursor source for the subsequent daytime and nighttime particulate nitrate formation pathways.

[0040] In this embodiment, the ability of MCM to precisely describe gas-phase photochemical processes can be utilized to accurately obtain precursor species such as HNO3 and N2O5, providing physicochemically meaningful inputs for the dynamic generation module of particulate nitrates, thus avoiding insufficient process interpretation caused by fitting based solely on empirical concentrations.

[0041] In the above embodiments, preferably, calculating the aerosol surface area concentration based on particulate matter observation data includes: When particulate matter observation data includes particle size distribution data, the aerosol surface area concentration is calculated using the following formula. This method can directly characterize the surface area of ​​particulate matter that can participate in gas-particle conversion and heterogeneous reactions using the measured particle size distribution: ; in, D p,i For the first i Particle diameter (in μm) for each particle size range. N i For the first i Particle number concentration in each particle size range (in particles cm⁻¹) -3 ), The unit is cm 2 cm -3 This formula is based on the assumption of spherical particles, sums the values ​​across the entire particle size spectrum, and then integrates precisely to obtain the total surface area of ​​particulate matter per unit volume of air.

[0042] When particulate matter observation data does not include particle size distribution data but includes PM 2.5 When considering mass concentration, the aerosol surface area concentration can be approximately estimated using spherical particles according to the following formula: ; in, ρ The particulate matter density (preferably 1.5 g / cm³) -3 (representing the typical density of mixed aerosols in the city). D p The equivalent particle size is (preferably 0.3 μm, representing the typical average particle size of urban fine particulate matter). This method only requires PM... 2.5 Mass concentration can be used to estimate aerosol surface area, for those with only conventional PM2.5 concentration. 2.5 The monitoring stations (such as national control stations) have important practical value, which enables this method to be widely used in stations with limited observation conditions.

[0043] In this embodiment, aerosol surface area concentration can be obtained under both observation conditions with and without particle size distribution, so that the HNO3 uptake and N2O5 hydrolysis processes are constrained by the surface area of ​​particulate matter, thereby enhancing the model's adaptability to different pollution levels and different particulate matter load conditions.

[0044] In the above embodiments, preferably, the heterogeneous reaction pathway for the heterogeneous hydrolysis of N2O5 to generate particulate nitrate at night is represented in the reaction mechanism as N2O5→2HNO3, to reflect the stoichiometric relationship of the two HNO3 molecules ultimately generated by the hydrolysis of N2O5, which is suitable for general simulation scenarios where it is not necessary to distinguish between gaseous HNO3 and particulate nitrate; or it is represented in the particulate nitrate tracer simulation as N2O5→2NA, where NA represents particulate nitrate tracer, used to accurately count the contribution of the N2O5 pathway to particulate nitrate, which is convenient for separation and diagnosis from the contribution of the daytime HNO3 conversion pathway.

[0045] At night, NO2 reacts with O3 to generate NO3 radicals, which further react with NO2 to form N2O5. N2O5 undergoes heterogeneous hydrolysis on the aerosol surface and contributes to the formation of nitrates.

[0046] The hydrolysis rate constant for the formation of particulate nitrates from the heterogeneous hydrolysis of N2O5 at night is determined by the following formula: ; Wherein, γN₂O₅ is the N₂O₅ uptake coefficient, representing the probability of hydrolysis after N₂O₅ molecules collide with the aerosol surface; cN₂O₅ is the average thermal rate of N₂O₅ molecules (cm / s). -1 ), AERO SA This represents the aerosol surface area concentration. cN₂O₅ can be calculated based on temperature and the molar mass of N₂O₅, for example, using the formula cN₂O₅ = sqrt(8RT / (πMWN₂O₅)) × 100, where MWN₂O₅ is taken as 10⁸ × 10⁻⁶. -3 kg mol -1 .

[0047] In this embodiment, the OBM can describe not only daytime tidal oxidation but also the heterogeneous hydrolysis process of N2O5 on the surface of particulate matter at night, thereby improving the model's ability to characterize the diurnal variation path of particulate nitrate.

[0048] In the above embodiments, preferably, the conversion rate of daytime HNO3 to particulate nitrate is determined by the original collision uptake rate of HNO3, and the corrected effective conversion rate is determined by the conversion rate of HNO3. The initial collisional uptake rate of HNO3 is determined according to the following formula: ; Wherein, γHNO3 is the effective uptake coefficient of HNO3 (preferably 0.003, representing the probability of HNO3 uptake on the surface of a typical urban aerosol), and cHNO3 is the average thermal velocity of HNO3 molecules, which can be calculated as cHNO3=sqrt(8RT / (πMWHNO3))×100, where MWHNO3 is taken as 63×10. -3 kg mol -1 AERO SA The aerosol surface area concentration is represented by this primitive rate, which characterizes the maximum theoretical rate of HNO3 transformation to the particulate phase at the molecular collision level without considering atmospheric limitations. It reflects the fundamental ability of HNO3 molecules to collide with and be taken up by the aerosol surface.

[0049] Furthermore, regarding the effective conversion rate, the collisional uptake rate alone is insufficient to accurately describe the actual process of HNO3 conversion to particulate form during the day. This is because HNO3 conversion is controlled not only by the molecular collision frequency but also by the aerosol liquid water content (determined by RH), the neutralizing capacity of NH3, and the thermodynamic stability of NH4NO3 (determined by temperature). Therefore, three correction factors, fRH, fNH3, and fT, are introduced, and the effective conversion rate is determined according to the following formula: ; Where fRH is the relative humidity correction factor, fNH3 is the ammonia correction factor, and fT is the temperature correction factor. Through these corrections, the conversion of HNO3 to particulate nitrate is no longer solely controlled by molecular collisions, but rather simultaneously responds to aerosol liquid water, the neutralizing capacity of NH3, and the thermodynamic stability of NH4NO3.

[0050] In this embodiment, dynamic parameterization can be established for the process of HNO3 generation in urban areas during winter and its continued entry into the particulate phase, enabling the model to more reasonably reproduce the common daytime nitrate rise process in actual measurements.

[0051] In the above embodiments, preferably, the relative humidity correction factor fRH is determined according to fRH=(RH-35) / (85-35) and limited to the range of 0 to 1; when RH is low, there is insufficient liquid water in the aerosol, and the uptake of HNO3 and the formation of NH4NO3 are inhibited; when RH is high, the liquid water in the aerosol increases, which promotes the entry of HNO3 into the particulate phase.

[0052] In winter, atmospheric relative humidity (RH) in urban areas typically fluctuates frequently within the range of 50% to 80%, making fRH one of the core correction factors controlling the time-varying nature of diurnal HNO3 conversion. Specifically, when RH < 35%, fRH = 0, indicating that under extremely dry conditions, the liquid water content of particulate matter is extremely low, NH4NO3 cannot stably exist in the particulate phase, and HNO3 hardly undergoes particulate phase transformation; when RH > 85%, fRH = 1, indicating that under high humidity conditions, aerosol liquid water is sufficient, and HNO3 can be efficiently taken up and participate in the NH4NO3 formation reaction; within the transition range of 35% to 85%, fRH increases linearly with RH.

[0053] Ammonia correction factor fNH3 according to Confirmed, among which The ammonia half-saturation parameter (preferably 2 ppb) characterizes the neutralization capacity of NH3; this correction factor reflects the limiting effect of NH3 on the phase transformation of HNO3 particles. The more abundant the NH3, the higher the probability of HNO3 forming particulate nitrates. Specifically, the above function is in the form of the Michaelis-Menten equation: when the NH3 concentration is much lower than... When NH3 << 2 ppb, fNH3 approaches 0, indicating a severe shortage of NH3 supply. Even with a high HNO3 concentration, it is difficult to neutralize and form NH4NO3. When the NH3 concentration is much higher than... When (NH3 >> 2ppb), fNH3 approaches 1, indicating that NH3 is sufficient and does not restrict the particle formation of HNO3; =2ppb corresponds to the median value of atmospheric NH3 observed in northern cities during winter, which allows fNH3 to reasonably distinguish between NH3-limited and NH3-sufficient conditions.

[0054] The temperature correction factor fT was determined according to fT = exp[-0.06 × (T - 273.15)] and limited to the range of 0.2 to 3.0. This correction factor is used to characterize the temperature sensitivity of NH4NO3. Low temperatures promote the stable existence of particulate ammonium nitrate, while high temperatures promote its volatilization and decomposition. Specifically, when the temperature is below 273.15 K (0 °C), fT > 1, and low temperature is conducive to the stable existence of NH4NO3 in the particulate phase, promoting HNO3 granulation; when the temperature is above 273.15 K, fT < 1, and high temperature causes the gas-solid equilibrium of NH4NO3 to shift towards the gaseous state, inhibiting granulation. 0.06 K -1The exponential coefficient is based on the experimental value of the temperature coefficient of the gas-solid equilibrium constant of NH4NO3, giving fT a clear thermodynamic basis. The diurnal temperature range in urban atmospheres during winter is significant, reaching 10–20 K, making fT one of the important driving factors for the diurnal variation of particulate nitrates. The upper limit fT = 3.0 and the lower limit fT = 0.2 are set to prevent rate distortion under extreme temperature conditions.

[0055] Effective conversion rate A rate limit is set, for example, 5.0×10. -4 s -1 To avoid the simultaneous effects of extremely high relative humidity, abundant NH3, and extremely low temperature, Exceeding the physically reasonable range causes non-physical rapid loss of HNO3, ensuring the numerical stability and physical rationality of the model.

[0056] In this embodiment, three key environmental factors—relative humidity, NH3 concentration, and temperature—can be converted into calculable dynamic correction factors, thereby improving the physical interpretability and numerical stability of the daytime HNO3 gas-particle conversion rate.

[0057] In the above embodiments, preferably, the particulate nitrate dynamic generation module further includes a reverse conversion path of particulate nitrate to HNO3 volatilization, which can be used to describe the process of particulate nitrate being converted back to gaseous HNO3 under relatively dry, high temperature or NH3-deficient conditions.

[0058] In actual atmospheric conditions, particulate nitrates (primarily in the form of NH4NO3) are not irreversibly formed and permanently retained in the particulate phase. Instead, they volatilize back into gaseous HNO3 when temperatures rise, relative humidity decreases, or NH3 levels drop. During winter, when urban atmospheric temperatures rise and relative humidity decreases in the afternoon, the observed concentration of particulate nitrates typically decreases. If the model only includes the formation pathway without the volatilization process, long-term model integration or PM2.5 concentrations will lead to a decrease in particulate nitrate concentrations. 2.5 At high concentrations, particulate nitrates may continue to accumulate to unreasonable levels; in addition, if the model's ability to reproduce the afternoon decrease in particulate nitrates is insufficient, it also indicates a lack of description of the anti-volatilization process.

[0059] The anti-volatilization rate of the reverse conversion pathway is determined according to the following formula: KNAHNO3=Kbase×fDry×fWarm×(1-fNH3); Wherein, Kbase is the basic anti-evaporation rate (preferably 2.0 × 10⁻⁶). -5 s -1 fDry is the drying correction factor, fWarm is the temperature correction factor, and fNH3 is the ammonia correction factor. The drying correction factor fDry = 1 - fRH, and the temperature correction factor fWarm = exp[0.08 × (T - 273.15)] are limited to the range of 0.2 to 5.0.

[0060] Specifically, the drying correction factor fDry = 1 - fRH reflects the promoting effect of dryness on volatilization; the lower the RH, the larger the fDry, the drier the particulate phase, and the easier it is for NH4NO3 to lose water and volatilize. fWarm reflects the promoting effect of temperature increase on NH4NO3 volatilization, with an exponential coefficient of 0.08 K. -1 The temperature dependence of the NH4NO3 decomposition equilibrium constant is shown; (1-fNH3) reflects the inhibitory effect of NH3 concentration on anti-volatilization. The more abundant the NH3, the stronger the inhibition of HNO3 volatilization, reflecting the locking effect of NH3 in maintaining the stability of particulate NH4NO3. The product form of the three factors ensures that anti-volatilization is significant only under dry, high-temperature conditions and when NH3 is insufficient, which is consistent with the atmospheric thermodynamic mechanism.

[0061] In practice, when the simulated concentration of particulate nitrate in the model is too high, or the simulated decrease in the afternoon is insufficient, this reverse conversion pathway can be introduced to allow the concentration of particulate nitrate to decrease reasonably with changes in meteorological and NH3 conditions.

[0062] In this embodiment, the continuous unidirectional accumulation of particulate nitrate in the model can be avoided, the physical rationality of the simulation results can be improved, and the simulation effect of nitrate change trend under afternoon or higher temperature conditions can be improved.

[0063] In the above embodiments, preferably, after running the observation constraint box model coupled with the dynamic generation module of particulate nitrate, in addition to outputting the simulated concentration of particulate nitrate, it also outputs the daytime HNO3 conversion contribution, the nighttime N2O5 hydrolysis contribution, the HNO3 gas phase generation rate, and the effective conversion rate of HNO3 to particulate nitrate during the day. The hydrolysis rate constant for the formation of particulate nitrates from the heterogeneous hydrolysis of N2O5 at night, relative humidity correction factor, ammonia correction factor, temperature correction factor, and aerosol surface area concentration. At least one diagnostic variable; Specifically, the contributions of daytime HNO3 conversion and nighttime N2O5 hydrolysis can directly quantify the relative importance of the two formation pathways, providing a quantitative basis for the diurnal and nighttime source analysis of particulate nitrate; the HNO3 gas phase formation rate reflects the degree to which photochemical oxidation capacity determines the supply of nitrate precursors. The time-varying curves reflect the time-varying characteristics of the diurnal gas-particle conversion rate under the synergistic drive of three factors: RH, NH3, and temperature. They can directly pinpoint the key periods and control factors that lead to the diurnal concentration increase. The time-varying sequences reflect the dynamic constraints of particulate surface area on heterogeneous reactions. These diagnostic variables can identify key controlling factors for particulate nitrate formation, including the limiting degree of NH3 neutralization capacity, the promoting effect of relative humidity, and the influence of temperature on the thermodynamic stability of NH4NO3. This allows for targeted control of nitrate and PM2.5 formation. 2.5 Pollution control strategies provide a scientific basis.

[0064] During model evaluation, the simulated concentration of particulate nitrate is compared with the observed concentration. At least one of the following evaluation metrics is calculated: simulation bias, mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (R), and diurnal concentration change slope. This evaluates the simulation effectiveness of the observation-constrained box model (OBM) for the diurnal variation and diurnal concentration increase of particulate nitrate. The diurnal concentration change slope can be used to determine whether the model has shifted from the simulated decreasing trend of diurnal nitrate in the original OBM to a diurnal increasing trend consistent with observations. If the slope was negative before the improvement (simulating a diurnal decrease), the slope after the improvement should become positive (simulating a diurnal increase), and the absolute value of the slope should be close to the observed value. This indicates that the model's ability to reproduce the diurnal nitrate concentration increase process has been substantially improved.

[0065] This implementation can simultaneously provide concentration simulation results and process contribution diagnostic results, which helps to identify daytime HNO3 pathways, nighttime N2O5 pathways, NH3 limitation, humidity promotion, temperature regulation, and aerosol surface area effects, providing a basis for analyzing the causes of urban nitrate pollution in winter and formulating control strategies.

[0066] This invention also proposes an improved particulate nitrate formation simulation system based on OBM, comprising: The observation data construction module is used to construct the observation constraint input dataset for the target observation area or observation station. The observation constraint input dataset includes at least temperature, relative humidity, gaseous pollutants including at least NO and NO2, photolysis frequency, particulate matter observation data, and NH3 concentration. The gas phase simulation module is used to input the observation constraint input dataset into the observation constraint box model coupled with the gas phase chemical mechanism to simulate the gas phase precursor species related to the formation of particulate nitrate. The gas phase precursor species include at least HNO3 and N2O5. The aerosol surface area calculation module is used to calculate the aerosol surface area concentration based on particulate matter observation data, providing particulate surface area constraints for subsequent HNO3 uptake and N2O5 hydrolysis. The nitrate dynamic generation module is used to couple the dynamic generation process of particulate nitrate to the observation constraint box model, which includes the gas-particle conversion pathway of HNO3 to particulate nitrate during the day and the heterogeneous reaction pathway of N2O5 to particulate nitrate at night. The conversion rate of HNO3 to particulate nitrate during the day is dynamically determined based on the effective uptake coefficient of HNO3, the average thermal rate of HNO3 molecules, the aerosol surface area concentration, relative humidity, NH3 concentration, and temperature. The hydrolysis rate of N2O5 to particulate nitrate at night is determined based on the N2O5 uptake coefficient, the average thermal rate of N2O5 molecules, and the aerosol surface area concentration. The output module is used to run the observation constraint box model after the coupled particulate nitrate dynamic generation module, and output the simulated concentration of particulate nitrate.

[0067] In this implementation, the modules work collaboratively in the order of observation constraint input, gas phase precursor simulation, aerosol surface area calculation, nitrate dynamic generation, and simulation result output, which can form a complete simulation process for particulate nitrate generation and improve OBM's ability to simulate the diurnal variation and day-night generation path of particulate nitrate.

[0068] The functions of each module of the OBM-based particulate nitrate generation simulation system disclosed in the above embodiments correspond to the steps of the OBM-based particulate nitrate generation simulation method disclosed in the above embodiments. In the implementation process, the operation is carried out with reference to the above embodiments, and will not be repeated here.

[0069] The technical features of the above-described particulate nitrate generation simulation system based on OBM improvement disclosed in the above embodiments are illustrated by the following examples.

[0070] Example 1: High temporal resolution (TLR) observation data from a winter observation station in a northern city for 24 consecutive hours (08:00 to 08:00 the following day) was selected as input, with a temporal resolution of 1 hour. The input data included: temperature T (range -5℃ to 8℃), relative humidity RH (40% to 85%), air pressure P (approximately 1010 hPa), O3 (approximately 30 ppb), NO (approximately 10–60 ppb), NO2 (approximately 40–80 ppb), CO (approximately 1.5 ppm), SO2 (approximately 5 ppb), HONO (approximately 1 ppb), VOCs (approximately 50 types including toluene and ethylene), and photolysis frequency (J). NO2 Approximately 0 to 1×10 -3 s -1 ), NH3 (approximately 5–30 ppb), PM 2.5 (approximately 80–150 μg m) -3 ) and particulate NO3- (approximately 10–30 μg m) -3 The data was incorporated into the input dataset after time alignment, outlier removal, and unit conversion.

[0071] The gas phase chemistry fundamental model uses OBM-MCM, with the aforementioned meteorological and pollutant observations as constraint inputs, to simulate OH radicals (approximately 3 × 10⁻⁶). 6 molec cm -3 (midday peak), HO2 (approximately 3 × 10⁻⁶) 8 molec cm -3 Hourly concentrations of NO3 (approximately 5 ppt, peak at night), N2O5 (approximately 0.5 ppb, peak at night), and HNO3 (approximately 2–5 ppb, peak in the afternoon during the day).

[0072] Aerosol surface area concentration based on PM 2.5 Mass concentration estimation: =6×10 -8 ×PM 2.5 / (1.5×0.3), in PM 2.5 =100μg m -3 hour Approximately 1.33 × 10 -5 cm 2 cm -3 And with PM 2.5 The changes are updated hourly.

[0073] The parameters for the dynamic generation module of particulate nitrate are set as follows: γN₂O₅ = 0.003, γHNO₃ = 0.003. =2ppb, Upper limit = 5.0 × 10 -4 s -1 The basic anti-evaporation rate Kbase = 2.0 × 10 -5 s -1 The correction factors were calculated hourly: taking RH=70% as an example, fRH=(70-35) / (85-35)=0.70; taking NH3=10ppb as an example, fNH3=10 / (10+2)=0.83; taking T=275K (2℃) as an example, fT=exp[-0.06×(275-273.15)]=exp[-0.111]=0.895; the product of the three factors is 0.70×0.83×0.895=0.52, indicating that the actual conversion rate of HNO3 under this meteorological condition is about 52% of the original collision uptake rate.

[0074] The model results show that after coupling the dynamic generation module of particulate nitrate, the simulated particulate nitrate concentration exhibits a significant increasing trend during the daytime (8:00–16:00), from approximately 8 μg m³. -3 Increased to approximately 22 μg m -3 This aligns with observed trends; the daytime HNO3 conversion pathway contributes approximately 75% of daytime generation, while the nighttime N2O5 hydrolysis pathway contributes approximately 60% of nighttime generation. In contrast, the original OBM simulation without the dynamic generation module shows a decreasing trend in daytime particulate nitrate, with a daytime variation slope of approximately -0.8 μg m -3 h -1 , compared with the observed +1.5μg m -3 h -1 On the contrary; the improved diurnal variation slope is approximately +1.3 μgm. -3 h -1 The values ​​were close to the observed values, and the correlation coefficient R increased from 0.35 to 0.82. The diagnostic results indicate that the diurnal increase in particulate nitrate at this site during winter is mainly driven by the combined effects of diurnal photochemical oxidation to HNO3 and gas-particle conversion. Among these factors, relative humidity (fRH) and NH3 sufficiency (fNH3) are the key limiting factors controlling the HNO3 particulate formation rate.

[0075] 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 simulating the formation of particulate nitrates based on an improved OBM (Original Material Manufacturing) method, characterized in that, include: Construct an observation constraint input dataset for the target observation area or observation station. The observation constraint input dataset shall include at least temperature, relative humidity, gaseous pollutants including at least NO and NO2, photolysis frequency, particulate matter observation data, and NH3 concentration. The observation constraint input dataset is input into an observation constraint box model coupled with a gas phase chemical mechanism to simulate gas phase precursor species related to the formation of particulate nitrate, wherein the gas phase precursor species include at least HNO3 and N2O5. Calculate the aerosol surface area concentration based on the particulate matter observation data; The observation constraint box model is coupled with a dynamic particulate nitrate generation module, which includes a gas-particle conversion pathway for the conversion of HNO3 to particulate nitrate during the day and a heterogeneous reaction pathway for the heterogeneous hydrolysis of N2O5 to generate particulate nitrate at night. The conversion rate of HNO3 to particulate nitrate during the day is determined based on the effective uptake coefficient of HNO3, the average thermal rate of HNO3 molecules, the aerosol surface area concentration, relative humidity, NH3 concentration, and temperature dynamics. The hydrolysis rate of N2O5 to particulate nitrate at night is determined based on the N2O5 uptake coefficient, the average thermal rate of N2O5 molecules, and the aerosol surface area concentration. Run the observation constraint box model coupled with the dynamic generation module of particulate nitrate to output the simulated concentration of particulate nitrate.

2. The method for simulating particulate nitrate formation based on OBM improvement according to claim 1, characterized in that, The meteorological parameters in the observation constraint input dataset also include at least one of air pressure and boundary layer height; the gaseous pollutants also include at least one of O3, CO, SO2, HONO, and VOCs; and the particulate matter observation data includes PM2.

5. 2.5 Particulate NO3 - NH4 + SO4 2- Cl - Na + K + Ca 2+ and Mg 2+ At least one of them; When constructing the observation constraint input dataset, the observation data is subjected to at least one of the following processing methods: time alignment, outlier removal, missing value handling, unit conversion, and quality control.

3. The method for simulating particulate nitrate formation based on OBM improvement according to claim 1, characterized in that, The gas-phase chemical mechanism adopts the master chemical mechanism (MCM); the observation constraint box model uses O3, NO, NO2, CO, SO2, HONO, VOCs, temperature, relative humidity, air pressure and photolysis frequency as constraint inputs to simulate N2O5 and HNO3, and simulate at least one key species among OH, HO2, RO2, O3 and NO3.

4. The method for simulating particulate nitrate formation based on OBM improvement according to claim 1, characterized in that, The calculation of aerosol surface area concentration based on the particulate matter observation data includes: When the particulate matter observation data includes particle size distribution data, the aerosol surface area concentration is calculated according to the following formula: ； in, D p,i For the first i Particle diameter in each particle size range N i For the first i Particle number concentration for each particle size range; When the particulate matter observation data does not include particle size distribution data but includes PM 2.5 When the mass concentration is used, the aerosol surface area concentration is estimated using the following formula: ； in, ρ For particulate matter density, D p The equivalent particle size.

5. The method for simulating particulate nitrate formation based on OBM improvement according to claim 1, characterized in that, The heterogeneous reaction pathway for the formation of particulate nitrates by the heterogeneous hydrolysis of N2O5 at night is represented as N2O5→2HNO3 in the reaction mechanism, or as N2O5→2NA in the particulate nitrate tracer simulation, where NA represents the particulate nitrate tracer. The hydrolysis rate constant for the formation of particulate nitrates from the heterogeneous hydrolysis of N2O5 at night is determined according to the following formula: ； Wherein, γN₂O₅ is the N₂O₅ uptake coefficient, cN₂O₅ is the average thermal rate of N₂O₅ molecules, and AERO SA This represents the aerosol surface area concentration.

6. The method for simulating particulate nitrate formation based on OBM improvement according to claim 1, characterized in that, The conversion rate of daytime HNO3 to particulate nitrate was determined by the original collision uptake rate of HNO3, and the corrected effective conversion rate was obtained. The initial collisional uptake rate of HNO3 is determined according to the following formula: ； Wherein, γHNO3 is the effective uptake coefficient of HNO3, cHNO3 is the average thermal rate of HNO3 molecules, and AERO SA This refers to the aerosol surface area concentration. The effective conversion rate is determined according to the following formula: ； Where fRH is the relative humidity correction factor, fNH3 is the ammonia correction factor, and fT is the temperature correction factor.

7. The method for simulating particulate nitrate formation based on OBM improvement according to claim 6, characterized in that, The relative humidity correction factor fRH is determined according to fRH=(RH-35) / (85-35) and limited to the range of 0 to 1; The ammonia correction factor fNH3 is according to Confirmed, among which The half-saturation parameter of ammonia gas characterizing the neutralization capacity of NH3; The temperature correction factor fT is determined according to fT=exp[-0.06×(T-273.15)] and is limited to the range of 0.2 to 3.0; The effective conversion rate A rate limit is set to prevent the non-physical rapid loss of HNO3.

8. The method for simulating particulate nitrate formation based on OBM improvement according to claim 1, characterized in that, The dynamic generation module for particulate nitrates also includes a reverse conversion pathway for the volatilization of particulate nitrates into HNO3. The volatilization rate of the reverse conversion pathway is determined according to the following formula: KNAHNO3=Kbase×fDry×fWarm×(1-fNH3); Wherein, Kbase is the basic anti-volatilization rate, fDry is the drying correction factor, fWarm is the temperature correction factor, and fNH3 is the ammonia correction factor; The drying correction factor fDry = 1 - fRH, and the heating correction factor fWarm = exp[0.08 × (T - 273.15)] are limited to the range of 0.2 to 5.

0.

9. The method for simulating particulate nitrate formation based on OBM improvement according to claim 1, characterized in that, After running the observation constraint box model coupled with the dynamic generation module of particulate nitrate, it also outputs at least one of the following diagnostic variables: daytime HNO3 conversion contribution, nighttime N2O5 hydrolysis contribution, HNO3 gas phase generation rate, daytime effective conversion rate of HNO3 to particulate nitrate, nighttime N2O5 heterogeneous hydrolysis to particulate nitrate hydrolysis rate constant, relative humidity correction factor, ammonia correction factor, temperature correction factor, and aerosol surface area concentration. The simulated concentration of particulate nitrate is compared with the observed concentration of particulate nitrate, and at least one of the following evaluation indicators is calculated: simulation bias, mean absolute error, root mean square error, correlation coefficient, and diurnal concentration change slope, to evaluate the simulation effect of the observation constraint box model on the diurnal variation process and diurnal concentration rise process of particulate nitrate.

10. A particulate nitrate generation simulation system based on OBM improvement, characterized in that, include: The observation data construction module is used to construct the observation constraint input dataset for the target observation area or observation station. The observation constraint input dataset includes at least temperature, relative humidity, gaseous pollutants including at least NO and NO2, photolysis frequency, particulate matter observation data, and NH3 concentration. The gas phase simulation module is used to input the observation constraint input dataset into an observation constraint box model coupled with gas phase chemical mechanisms to simulate gas phase precursor species related to the formation of particulate nitrates. The gas phase precursor species include at least HNO3 and N2O5. The aerosol surface area calculation module is used to calculate the aerosol surface area concentration based on the particulate matter observation data. The nitrate dynamic generation module is used to couple the dynamic generation process of particulate nitrate, which includes the gas-particle conversion pathway of HNO3 to particulate nitrate during the day and the heterogeneous reaction pathway of N2O5 to particulate nitrate at night, to the observation constraint box model. The conversion rate of HNO3 to particulate nitrate during the day is dynamically determined based on the effective uptake coefficient of HNO3, the average thermal rate of HNO3 molecules, the aerosol surface area concentration, relative humidity, NH3 concentration, and temperature. The hydrolysis rate of N2O5 to particulate nitrate at night is determined based on the N2O5 uptake coefficient, the average thermal rate of N2O5 molecules, and the aerosol surface area concentration. The output module is used to run the observation constraint box model coupled with the particulate nitrate dynamic generation module and output the simulated concentration of particulate nitrate.

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