Method for constructing a box model for the generation of organic compounds with a full range of volatility from the oxidation of benzene derivatives

Abstract

The present invention relates to a method for constructing a box model of full-volatility range organic matter generated by oxidation of benzene series, and belongs to the field of atmospheric environmental science. The method comprises constructing a comprehensive two-dimensional volatility interval model of benzene series; obtaining preliminary comparison results based on the comparison of simulated variable values ​​and observed variable values; optimizing and adjusting the model parameters of the comprehensive two-dimensional volatility interval model according to the preliminary comparison results and the normalized average error between the simulated variable values ​​and the observed variable values; and obtaining a box model of full-volatility range organic matter generated by oxidation of benzene series based on the optimization and adjustment results. The model of the present invention integrates the oxidation products of benzene series within the full volatility range generated by multiple pathways, and simultaneously reproduces the OOMs and SOA generated by benzene series in the smoke box experiment; and improves the problem that the full-volatility oxidation products of benzene series cannot be simulated due to the lack of chemical mechanism in other previous methods.

Description

Technical Field

[0001] The invention belongs to the field of atmospheric environmental science, and particularly relates to a method for constructing a box model of organic substances with a full volatility range generated by oxidation of benzene series. Background Art

[0002] Atmospheric fine particulate matter is one of the main pollutants of atmospheric complex pollution, and has important impacts on air pollution, human health, climate change, etc. Among them, the generation of new particles is the main source of atmospheric particulate matter number concentration. When the generated new particles grow to a particle size range of 50 to 100 nanometers or more, they will contribute to cloud condensation nuclei, thereby affecting cloud formation and producing climate effects. The condensation of organic matter in the particle phase is the main driving factor for the growth of 3-nanometer particles to the scale of cloud condensation nuclei, and organic matter of different volatility in the atmosphere will contribute to different stages of new particle generation. This type of organic matter can be divided into the following categories according to the logarithm of saturation vapor concentration (log C*): ULVOC (Ultra-lowVolatile Organic Compounds, log C*≤-8.3), ELVOC (Extreme-lowVolatile Organic Compounds, -8.3≤log C*≤-4.3), LVOC (LowVolatile Organic Compounds, -4.3≤log C*≤-0.3), SVOC (Semi Volatile Organic Compounds, -0.3≤logC*≤2.7), etc. Among them, ULVOC and ELVOC contribute to the generation and initial growth of new particles, while LVOC and SVOC contribute to the growth of new particles with larger particle sizes. Therefore, in order to fully understand the contribution of new particle generation to particulate pollution and climate change, it is necessary to have a comprehensive scientific understanding of the generation of organic matter in the full volatility range. These low-volatility organic compounds are generated by atmospheric oxidation reactions of VOC or IVOC precursors, and usually have low volatility and high oxidation state (oxygen-carbon ratio, O:C). This type of organic matter is usually called highly oxygenated organic matter (Oxygenated Organic Molecules, OOMs), which is the most important type of organic matter in the full volatility range that contributes to the generation of new particles. The sources of OOMs precursors are relatively complex, and the main precursors include volatile organic compounds such as monoterpenes, benzene series, alkanes, and polycyclic aromatic hydrocarbons. In urban atmospheres with a high degree of pollution, benzene series are the most important precursors of OOMs. Observations show that OOMs produced by benzene series in urban atmospheres can explain most of the new particle growth rate and contribute 30-50% of the secondary organic aerosol (SOA) generation.In addition, the mechanism of the generation of OOMs from benzene is relatively complex. Generally speaking, the main oxidation reactions that generate OOMs include unconventional atmospheric free radical reactions such as self-oxidation and dimerization. However, the smoke chamber experiment on the generation of OOMs from benzene shows that both unconventional free radical reactions and traditional OH radical-driven multi-stage oxidation reactions contribute significantly to the generation of OOMs from benzene. Therefore, the quantification of the generation of OOMs from benzene is crucial for the study of new particle generation and air pollution in urban areas, and is the focus and difficulty in the research related to the generation of OOMs.

[0003] The existing technologies for obtaining the full volatility distribution of OOMs generated by benzene series are mainly divided into two types: observation and simulation. In terms of observation, the most important instrument for measuring OOMs is the Chemical Ionization Mass Spectrometer (CIMS), which can be divided into Nitrate-CIMS, Iodine-CIMS, Amine-CIMS, etc. according to its ionization source. However, since CIMS can only obtain information such as the molecular formula, molecular weight and concentration of OOMs, in order to obtain the source and volatility distribution of OOMs by means of observation, and then obtain the volatility distribution of OOMs generated by benzene series, it is necessary to further analyze the molecular formula of OOMs. For the volatility distribution of OOMs, the volatility of the molecule can be estimated by the number of C, O, N, and H atoms in the molecule, thereby obtaining the concentration distribution of OOMs within the full volatility range. As for the source of OOMs, since the molecular structures of benzene series and other precursors such as monoterpenes and alkanes are quite different, the precursor categories can be identified by the unsaturation of OOMs molecules. For example, Nie et al. (Secondary Organic Aerosol Formed by Condensing Anthropogenic Vapours over China's Megacities. Nat. Geosci. 2022, 15 (4), 255–261) believed that the products with an intramolecular unsaturation greater than 2 or an unsaturation equal to 2 and a number of oxygen atoms greater than 6 after the nitrate group is removed from the molecule come from benzene series. Qiao et al. (Precursor Apportionment of Atmospheric Oxygenated Organic Molecules Using a Machine Learning Method. Environ. Sci.: Atmos. 2023, 3 (1), 230–237.) constructed the relationship between benzene series and OOMs generation based on machine learning clustering methods, and distinguished the proportion of benzene series in the observed OOMs. However, no matter what kind of ionization source CIMS uses, it cannot guarantee that all products that contribute to the generation and growth of new particles will be measured. For example, Nitrate-CIMS is more sensitive to organic matter with high oxygen content, but not very sensitive to products with medium oxygen content, while Iodine-CIMS is significantly more sensitive to products with medium oxygen content than to products with high oxygen content. In addition, this semi-empirical method of determining the source and volatility distribution of OOMs based on molecular formula also has high uncertainty, which affects the accuracy of the total volatility distribution of OOMs generated by benzene series.In addition, the observation and analysis of OOMs are costly and computationally complex, and particle growth studies based on observations of organic vapor may only be conducted in limited locations, difficult to generalize to large areas, and unable to study historical or future conditions.

[0004] In terms of simulation, most of the existing numerical models for OOMs generation are based on the simulation framework of explicit free radical reactions to simulate the free radical reactions that generate OOMs, that is, based on the MCM mechanism model, self-oxidation, dimerization and other reactions related to the generation of precursor OOMs are implanted into it, and the reaction system is treated with a set of ordinary differential equations. At present, most of the box models and three-dimensional models are based on explicit simulation frameworks, which can preliminarily reproduce the OOMs generated by natural volatile organic compounds such as monoterpenes and isoprene in smoke box experiments and field observations. However, for benzene series, the current mainstream numerical models do not take into account the possible unconventional free radical reactions. In addition, the explicit simulation framework will significantly increase the cost of the three-dimensional model, thereby preventing its large-scale application in the numerical simulation of atmospheric OOMs in a large area. The two-dimensional volatility basis set (2D-VBS) model based on volatility-oxidation state classifies organic matter according to volatility (logC*) and oxygen-carbon ratio (O:C), and forms a two-dimensional space with fixed logC* and O:C intervals, which can simulate the traditional multi-stage oxidation reaction of organic matter with a small amount of calculation. However, the free radical 2D-VBS model (Radical 2D-VBS, R2D-VBS) based on the 2D-VBS framework has only been developed for the unconventional free radical reaction of monoterpenes, and the existing BTEX oxidation model based on the 2D-VBS framework also does not consider the unconventional free radical reaction of BTEX RO2 free radical. As mentioned above, both traditional multi-stage oxidation reactions and unconventional free radical reactions have a large contribution to the generation of BTEX OOMs, and currently no model can simulate these two types of reactions at the same time. Therefore, due to the lack of chemical mechanism in the model, the current numerical model cannot simulate the OOMs generated by BTEX well, and therefore cannot simulate the organic matter with a full volatility range generated by BTEX.

[0005] In summary, the current understanding of the generation of organic compounds with a full volatile range from benzene derivatives has certain limitations, mainly because the existing observation instruments have large uncertainties in the determination of OOMs concentration, molecular formula, volatility distribution and source, and the existing models are missing the chemical process of the generation of OOMs from benzene derivatives.

[0006] First, in terms of atmospheric observation, due to the defects of the observation instruments, different CIMS instruments have different sensitivities to different types of OOMs molecules, so the measured OOMs molecules cannot represent all low-volatility oxidation products in the atmosphere. In addition, since the observation data cannot directly obtain the volatility distribution and source of OOMs, it is necessary to analyze the observation data of OOMs based on empirical formulas, which causes greater uncertainty in the volatility distribution results of OOMs generated by benzene series.

[0007] In terms of model simulation, the current mainstream box model and three-dimensional model lack the main pathways for the generation of OOMs from benzene series, and no model can simultaneously simulate the important contributions of traditional multi-stage oxidation and unconventional free radical reactions to the generation of OOMs from benzene series. Therefore, the benzene series oxidation products obtained by the model are difficult to represent the low-volatility OOMs products generated by benzene series in the real atmosphere, and the real volatility distribution of benzene series OOMs cannot be obtained.

[0008] Therefore, it is very important to develop a numerical model to simulate the chemical process of BTEX generation into OOMs, so as to clarify the full volatility distribution of organic products generated from BTEX. Summary of the invention

[0009] In view of the above problems, the present invention provides a method and system for constructing a box model for generating organic substances with a full volatility range through oxidation of benzene series.

[0010] The first object of the present invention is to provide a method for constructing a box model of a benzene series product oxidized to generate organic compounds with a full volatility range, comprising:

[0011] Based on the existing smoke chamber experiments, chemical kinetics and theoretical calculations of BTEX oxidation, a comprehensive two-dimensional volatility interval model of BTEX was constructed.

[0012] Based on the comparison between the simulated variable values ​​and the observed variable values, a preliminary comparison result is obtained, wherein the simulated variable values ​​are obtained by simulating the oxidation of benzene series to generate highly oxygenated organic matter and secondary organic aerosols using a comprehensive two-dimensional volatility interval model;

[0013] Based on the results of the preliminary comparison and the normalized mean errors between the simulated and observed variable values, the model parameters of the comprehensive two-dimensional volatility interval model are optimized and adjusted;

[0014] Based on the optimization and adjustment results, a box model for the oxidation of benzene series to generate organic compounds with a full volatile range was obtained.

[0015] In a specific embodiment of the present invention, the variables include volatility and oxygen-carbon ratio distribution of OOMs carbon yield, yield and oxygen-carbon ratio of SOA, wherein OOMs is the abbreviation of highly oxygenated organic matter, and SOA is the abbreviation of secondary organic aerosol.

[0016] In a specific embodiment of the present invention, the comprehensive two-dimensional volatility interval model of benzene series is constructed based on the existing smoke box experiment, chemical kinetics and theoretical calculation of benzene series oxidation, including:

[0017] Based on the existing smoke chamber experiments, chemical kinetics and theoretical calculations of BTEX oxidation, a primary oxidation module of the comprehensive two-dimensional volatility interval model of BTEX was established.

[0018] Based on the existing smoke chamber experiments, chemical kinetics and theoretical calculations of BTEX oxidation, a free radical reaction module of the comprehensive two-dimensional volatility interval model of BTEX was established.

[0019] Based on the existing smoke chamber experiments, chemical kinetics and theoretical calculations of BTEX oxidation, a multi-stage oxidation module of a comprehensive two-dimensional volatility interval model for BTEX was established.

[0020] In a specific embodiment of the present invention, the primary oxidation module is used to simulate the process of forming first-stage stable molecules or free radicals under the oxidation of organic matter by OH / O3 / NO3 free radicals.

[0021] In a specific embodiment of the present invention, the free radical reaction module is used to simulate the chain oxidation and termination reactions of different free radicals; when the free radical reaction module is established, different free radical module parameters are used to distinguish between monosubstituted benzene series and polysubstituted benzene series.

[0022] In a specific embodiment of the present invention, the multi-stage oxidation module is used to simulate the multi-stage oxidation of products driven by OH in the two-dimensional space of volatility and oxygen-carbon ratio.

[0023] In a specific embodiment of the present invention, the multi-stage oxidation module is established, comprising:

[0024] Determine upper and lower limits for volatility and oxygen-to-carbon ratio;

[0025] Set volatility ranges and intervals;

[0026] The range and interval of oxygen-carbon ratio will be set;

[0027] Determine the distribution matrix of multi-stage oxidation products.

[0028] In a specific embodiment of the present invention, the comparison between the simulated parameter value and the observed parameter value to obtain a preliminary comparison result includes:

[0029] Collect data from smoke chamber or oxidation flow tube experiments;

[0030] Correct and reprocess the collected experimental data;

[0031] The corrected and reprocessed data were input into a comprehensive two-dimensional volatility interval model of BTEX to simulate the experiment of BTEX oxidation to generate highly oxygenated organic matter and secondary organic aerosols;

[0032] The simulated parameter values ​​obtained from the simulation are compared with the observed parameter values.

[0033] In a specific embodiment of the present invention, based on the results of the preliminary comparison and the normalized mean error between the simulated variable values ​​and the observed variable values, the model parameters of the comprehensive two-dimensional volatility interval model are optimized and adjusted, including:

[0034] According to the results of the preliminary comparison and the normalized mean error between the simulated variable values ​​and the observed variable values, the model parameters to be optimized are determined, and the model parameters to be optimized are adjusted;

[0035] Repeating the steps of “simulating an experiment to generate high oxygen organic matter and secondary organic aerosol”, “comparing simulated variable values ​​with observed variable values” and “determining model parameters to be optimized and adjusting model parameters to be optimized” in a loop until the normalized average error between the simulated variable values ​​and the observed variable values ​​is within a predetermined range;

[0036] Conduct sensitivity analysis of model parameters to verify the rationality of optimized model parameters.

[0037] In a specific embodiment of the present invention, the model parameters include cracking ratio, gas phase oxidation rate, heterogeneous oxidation rate, self-oxidizable free radical ratio, dimerization ratio, self-oxidation rate, dimerization rate and free radical chain termination reaction rate.

[0038] In a specific embodiment of the present invention, the calculation formula of the normalized average error between the simulated variable value and the observed variable value is as follows:

[0039]

[0040] Where NMB is the abbreviation of normalized mean error, Si refers to the value of the i-th simulation parameter, Oi refers to the observed value of the i-th parameter, and n is the number of volatility / oxygen-carbon ratio intervals.

[0041] A second object of the present invention is to provide a system for constructing a box model of a full-volatility range of organic compounds generated by oxidation of benzene series, comprising:

[0042] The model framework building module is used to construct a comprehensive two-dimensional volatility interval model of benzene series based on the existing smoke chamber experiments, chemical kinetics and theoretical calculations of benzene series oxidation;

[0043] The comparison module is used to obtain preliminary comparison results based on the comparison of simulated variable values ​​and observed variable values, wherein the simulated variable values ​​are obtained by simulating the oxidation of benzene series to generate high oxygen organic matter and secondary organic aerosols through a comprehensive two-dimensional volatility interval model;

[0044] The optimization and adjustment module is used to optimize and adjust the model parameters of the comprehensive two-dimensional volatility interval model according to the results of the preliminary comparison and the normalized average error between the simulated variable values ​​and the observed variable values;

[0045] The completion module is used to obtain a box model for generating full-volatile organic compounds by oxidation of benzene series based on the optimization adjustment results.

[0046] Beneficial effects of the present invention:

[0047] The present invention provides a method for constructing a box model for generating organic substances with a full volatility range by oxidation of benzene series, which has the following beneficial effects:

[0048] 1) The model simulates the generation of organic compounds in the full volatility range of benzene series. The model integrates the oxidation products of benzene series in the full volatility range generated by multiple pathways, and reproduces the OOMs and SOA generated by benzene series in the smoke chamber experiment; it improves the problem that other previous methods cannot simulate the full volatility oxidation products of benzene series due to the lack of chemical mechanism.

[0049] 2) Based on this model, by constructing a real atmospheric scenario, the composition and formation pathways of oxidation products of the entire volatile range of BTEX in the atmosphere are quantitatively evaluated, which is difficult to achieve with other methods. This is very important for explaining the formation mechanism of OOMs generated by chemical BTEX, and also plays an important role in explaining the nucleation growth of new particles and the generation of SOA in the polluted atmosphere.

[0050] 3) The model established by the present invention systematically simulates atmospheric physical and chemical processes while keeping the computational cost within a small range, making it suitable for inclusion in three-dimensional air quality or climate models to study a wider range of air pollution and climate change issues.

[0051] 4) In addition, this mechanistic model can also simulate the generation of full-volatility organic compounds of benzene series under different scenarios; the model can simulate the full-volatility range of VOC oxidation products based only on VOCs and other easily available atmospheric data, without relying on OOMs that are difficult to measure and analyze. This allows simulations to be conducted at different times, locations, and scenarios, which will help evaluate the effectiveness of future air pollution control strategies.

[0052] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 A flow chart of a method for constructing a box model for generating organic substances with a full volatility range by oxidation of benzene series according to an embodiment of the present invention is shown;

[0055] Figure 2 A simulation framework diagram of a comprehensive two-dimensional volatility model constructed according to an embodiment of the present invention is shown;

[0056] Figure 3 One of the diagrams showing the volatility distribution of the carbon yield of OOMs of each experiment simulated by the comprehensive two-dimensional volatility model according to an embodiment of the present invention and its comparison with the observed results;

[0057] Figure 4 One of the graphs showing the oxygen-carbon ratio distribution of the OOMs carbon yield of each experiment simulated by the comprehensive two-dimensional volatility model according to an embodiment of the present invention and its comparison with the observed results;

[0058] Figure 5 A framework diagram of a system for constructing a box model for generating organic substances with a full volatility range by oxidizing benzene series according to an embodiment of the present invention is shown;

[0059] In the figure: model framework building module 1; comparison module 2; optimization and adjustment module 3; completion module 4. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] like Figure 1As shown, the method for constructing a box model of generating organic substances with a full volatility range by oxidation of benzene series according to an embodiment of the present invention includes:

[0062] Step S1, constructing a comprehensive two-dimensional volatility interval model of benzene series based on existing smoke box experiments, chemical kinetics and theoretical calculations of benzene series oxidation;

[0063] Step S2, based on the comparison of the simulated variable values ​​and the observed variable values, a preliminary comparison result is obtained, wherein the simulated variable values ​​are obtained by simulating the oxidation of benzene series to generate high oxygen organic matter and secondary organic aerosols using a comprehensive two-dimensional volatility interval model, and the variables include the volatility and oxygen-carbon ratio distribution of the OOMs carbon yield, the yield and oxygen-carbon ratio of SOA;

[0064] Step S3, optimizing and adjusting the model parameters of the comprehensive two-dimensional volatility interval model according to the preliminary comparison results and the normalized mean error between the simulated variable values ​​and the observed variable values;

[0065] Step S4: Based on the optimization adjustment results, a box model of the full volatile range organic matter generated by oxidation of benzene series is obtained.

[0066] In step S1, a comprehensive two-dimensional volatility interval model of benzene series is constructed based on existing smoke box experiments, chemical kinetics and theoretical calculations of benzene series oxidation;

[0067] Compared with the box model that explicitly expresses free radical reactions, the 2D-VBS framework can directly express the volatility and O:C of species, and can achieve the simulation of the fully volatile oxidation products of benzene series with a smaller amount of calculation. Therefore, the present invention is based on the 2D-VBS framework, integrates the unconventional free radical reaction mechanism, constructs an integrated two-dimensional volatility interval model (Integrated 2D-VBS, hereinafter referred to as I2D-VBS), and integrates the process of benzene series generating OOMs through unconventional free radical reactions such as self-oxidation and dimerization and multi-stage oxidation reactions into the same model, thereby achieving the development and establishment of a box model of organic matter in the full volatility range of benzene series. The I2D-VBS model integrates the reaction processes such as the primary oxidation of precursors, unconventional free radical reactions and multi-stage oxidation reactions, so Figure 2 As shown, the I2D-VBS model has three main modules: primary oxidation module, free radical reaction module and multi-stage oxidation module.

[0068] In a specific implementation, step S1 may include the following steps:

[0069] Step S11, based on the existing smoke box experiment, chemical kinetics and theoretical calculation of the oxidation of benzene series, a comprehensive two-dimensional volatility interval model of benzene series is constructed, comprising:

[0070] Step S12, based on the existing smoke box experiments, chemical kinetics and theoretical calculations of the oxidation of benzene series, a primary oxidation module of the comprehensive two-dimensional volatility interval model of benzene series is established. Step S13, based on the existing smoke box experiments, chemical kinetics and theoretical calculations of the oxidation of benzene series, a free radical reaction module of the comprehensive two-dimensional volatility interval model of benzene series is established;

[0071] Step S14: Based on the existing smoke box experiments, chemical kinetics and theoretical calculations of the oxidation of benzene series, a multi-stage oxidation module of the comprehensive two-dimensional volatility interval model of benzene series is established.

[0072] In step S12, the primary oxidation module is used to simulate the process of organic matter forming the first-level stable molecules or free radicals under the oxidation of OH / O3 / NO3 free radicals; since the primary oxidation process is relatively simple, the process mainly refers to the reaction path and reaction rate of the MCM model. Through the products of the primary oxidation, combined with the existing scientific understanding of the OOMs generation mechanism, it can be judged whether the products of the path have the potential for self-oxidation. If the path does not have the potential for self-oxidation, it is necessary to trace the first-level stable products of low nitrogen oxides and high nitrogen oxides and estimate their volatility according to the SIMPOL model to allocate them to the volatility and oxygen-carbon ratio two-dimensional space (hereinafter referred to as C*-O:C two-dimensional space), and input them into the multi-stage oxidation module. If the path has the potential for self-oxidation, it is necessary to determine the chemical properties of the RO2 free radicals in the path and input them into the free radical reaction module. Here, toluene is used as an example to illustrate the primary oxidation module: toluene reacts with OH to generate RO2, which can be divided into two categories, one is RO2 that can be self-oxidized, and the other is not self-oxidized (hereinafter referred to as noOxRO2). The reaction equation is shown in formula (1):

[0073] TOL+OH→f bcp_Ox RO2+f bcp_noOx noOxRO2+(1-f bcp )Other products (1)

[0074] In formula (1), f bcp represents the ratio of the bcp pathway, i.e., the ratio of bicyclic products in the four pathways of primary oxidation of benzene series. bcp_Ox represents the proportion of RO2 that can be self-oxidized in this part of the product, f bcp_noOx represents the proportion of non-self-oxidizable RO2 in this part of the product, where f bcp_Ox +f bcp_noOx =f bcp The Other products represent the products of other primary oxidation pathways.

[0075] In step S13, when the free radical reaction module is established:

[0076] In the free radical reaction module, the model generally assumes that RO2 will undergo a certain number of self-oxidation and explicitly simulates the chain oxidation and termination reactions of different free radicals, including self-oxidation, dimerization, the generation of nitrate esters and peroxides, etc. Here, it is assumed that the peroxybicyclic RO2 free radicals generated by benzene derivatives may undergo tertiary self-oxidation, and the reaction equations are shown in equations (2)-(6):

[0077] Ox n RO2→Ox n+1 RO2 (2)

[0078] Ox n RO2+Ox m RO2→f dimer Ox n Ox m ROOR+(1-f dimer )Ox m RO+(1-f dimer )Ox n RO+O2 (3)

[0080] Ox n RO2+HO2→Ox n ROOH+O2 (4)

[0081] Ox n RO2+NO→f(RONO2)Ox n RONO2+(1-f(RONO2))Ox n RO+(1-f(RONO2))

[0082] NO2(5)

[0083] Ox n RO2→Ox n RO (6)

[0084] In formulas (2)-(6), n and m can be 0, 1, 2, or 3 (determined by the number of times the self-oxidation can occur), and f dimerRefers to the proportion of dimers generated when two RO2s react. noOxRO2 will not undergo self-oxidation, but other free radical reactions will occur. In addition, because the chemical properties of monosubstituted benzene series and polysubstituted benzene series are relatively different, they are distinguished in this model. The RO2 free radical produced by monosubstituted benzene series is named ARO2, and the free radical produced by polysubstituted benzene series is named BRO2. The two adopt different free radical module parameters. Unlike R2D-VBS, the stable molecular products produced by the I2D-VBS free radical module will be distributed into the C*-O:C two-dimensional space based on their O:C and SIMPOL estimated volatility, rather than the assumed product distribution. Among them, the RO free radical product will undergo one functionalization or cracking. For the functionalized product model, it is assumed that the functionalized products ROH and R=O each account for 50%, and the functionalized products are distributed into the two-dimensional space based on the volatility and O:C of ROH and R=O. The distribution of RO cracking products follows the assumption of 2D-VBS. The products distributed into the C*-O:C space will be input into the multi-stage oxidation module.

[0085] In step S14, the multi-stage oxidation module simulates the multi-stage oxidation of the product driven by OH in the C*-O:C two-dimensional space. When the multi-stage oxidation module is established:

[0086] a. First, we need to determine the upper and lower limits of C* and O:C to determine the two-dimensional space;

[0087] b. Setting the interval and interval of volatility. In the example of the present invention, illustratively, the interval of logC* in the model is set to [-10, 8], and the interval is 1;

[0088] c. Setting the oxygen-carbon ratio interval and interval. In the example of the present invention, the O:C interval in the model is set to [0, 2], with an interval of 0.1. The key properties of the product in each two-dimensional interval, such as the number of C and O and the change of volatility with temperature, are inferred from previous chemical thermodynamic studies;

[0089] d. It is then necessary to determine the distribution matrix of the multi-stage oxidation products.

[0090] The multi-stage oxidation process includes functionalization and cracking. The former reduces the volatility of the product and increases the number of oxygen atoms in the product, while the latter increases or decreases the volatility of the product while increasing the number of oxygen atoms. Based on this assumption, the volatility and oxygen number of each multi-stage oxidation of the product can be specified. Assuming that the probability of adding a -OH and =O is equal, the logC* reduction caused by each additional oxygen atom in functionalization is about 1.7. For cracking, the distribution of the product with reduced volatility is similar to that of functionalization, but the O:C of the part with increased volatility is distributed in the range of O:C from the O:C of the reactant to the maximum O:C value in 2D-VBS. Based on this assumption, the distribution matrix of product functionalization and cracking can be obtained. Finally, it is necessary to simulate the multi-stage oxidation and gas-particle distribution of gas-phase and particle-phase products. For the smoke box experiment, it is also necessary to simulate its wall loss process. The 2D-VBS model calculates the concentration and distribution of particle-phase and gas-phase products at each time interval using the following formula, as shown in formulas (7)-(12):

[0091] f frag =(O:C) n-frag f func =1-f fragg (7)

[0092] T=f frag -T frag +f func -T func (8)

[0093]

[0094]

[0095]

[0096]

[0097] Formula (7)-(12), f frag 、f func represents the ratio of cleavage and functionalization, T frag and T func represents the product distribution matrix of cleavage and functionalization, which is determined as described above, and T represents the total distribution of products after a multi-stage oxidation occurs once. basis , k het , k vap , k wall represent the gas phase oxidation rate constant, heterogeneous oxidation rate constant, gas phase wall loss rate and particle phase wall loss rate, respectively, C vap , C parrepresents the product concentration distribution matrix of the gas phase and the particle phase respectively (calculated by the mass concentration of carbon); in the last formula, i represents a "grid" with a specific C* and O:C, which represents the proportion of the particle phase in the "grid", C i (μg / m 3 ) represents the total concentration of the gas phase and particle phase of the “grid”, C * i (μg / m 3 ) is the saturated vapor concentration of i, C OA (μg / m 3 ) is the total concentration of organic aerosol. The calculated products are then distributed through gas-particle equilibrium to obtain the gas-particle product distribution. For the generation of organic nitrates in multi-stage oxidation, it is assumed that organic matter will only add one additional -ONO2 group in the multi-stage oxidation, so the high NO x The products under these conditions are divided into two layers, one layer is the product without nitrogen, and the other layer is the product without nitrogen. Therefore, the original concentration matrix is ​​expanded to. The condition for the model to judge that the oxidation system belongs to high nitrogen is that the precursor and NO x The ratio of concentrations is used to refer to the competition between HO2 and NO2. If under high nitrogen conditions, the ratio of nitrogen-containing products produced is determined based on this ratio. Generally speaking, the ratio of nitrate produced by the RO2+NO reaction is about 0.3, so the model assumes that the maximum ratio of nitrate products produced under high nitrogen conditions is 0.3. The conversion matrix of the multi-stage oxidation process of these two layers of products will also be expanded, and its expression is shown in formula (13):

[0098]

[0099] represents the transformation matrix containing zero nitrogen products, which represents the superposition of the functionalization and cleavage matrices. represents the transformation matrix from 0 nitrogen products to 1 nitrogen product, representing the generation of nitrate, It means that organic nitrates are decomposed into nitrogen-free products. represents the functionalization and cleavage of nitrates. This transformation matrix is ​​input into the multi-stage oxidation formula for further calculations. The RONO2 products generated by the free radical mechanism are input into the nitrogen layer of the matrix and the subsequent multi-stage oxidation reactions are calculated.

[0100] In step S2, the comparison between the simulated variable values ​​and the observed variable values ​​is used to obtain preliminary comparison results, wherein the simulated variable values ​​are obtained by simulating the oxidation of benzene series to generate highly oxygenated organic matter and secondary organic aerosols using a comprehensive two-dimensional volatility interval model, and the variables include volatility and oxygen-carbon ratio distribution of OOMs carbon yield, and yield and oxygen-carbon ratio of SOA;

[0101] The model parameters such as the rate and product distribution of multi-stage oxidation and free radical reaction in I2D-VBS are uncertain, which will lead to large errors in the simulation results of I2D-VBS. The I2D-VBS model mainly has the following adjustable parameters: cracking ratio, gas phase oxidation rate, heterogeneous oxidation rate, self-oxidizable free radical ratio, dimerization ratio, self-oxidation rate, dimerization rate, free radical chain termination reaction rate, etc., that is, the model parameters include cracking ratio, gas phase oxidation rate, heterogeneous oxidation rate, self-oxidizable free radical ratio, dimerization ratio, self-oxidation rate, dimerization rate and free radical chain termination reaction rate. The initial values ​​of these parameters come from the literature, such as theoretical calculations, kinetic analysis of smoke chamber data, and the original model parameters of traditional 2D-VBS and R2D-VBS. In order to reduce the uncertainty of model parameters, after the model is constructed, the experimental data of smoke box experiments measuring the oxidation of benzene series to generate OOMs and SOA will be collected and simulated, with the goal of reproducing the volatility of the OOMs yield, the oxygen-carbon ratio distribution, and the concentration and oxygen-carbon ratio of SOA in the experiment. After collecting the experimental data, the experimental data needs to be processed to convert the observed OOMs molecular formula and concentration into the yield distribution in the volatility and oxygen-carbon ratio range, and correct the effects of wall loss and particle phase loss on the OOMs concentration. Finally, the oxidant exposure, reaction time, precursor concentration, nitrogen oxide concentration and other conditions of the smoke box or oxidation flow tube experiment need to be input into the model to obtain the simulated OOMs and SOA and compare them with the observations.

[0102] In a specific implementation, step S2 may include:

[0103] S21. Collecting experimental data from smoke chamber or oxidation flow tube;

[0104] S22. Correct and reprocess the collected experimental data;

[0105] S23. Input the corrected and reprocessed data into the comprehensive two-dimensional volatility interval model of BTEX to simulate the experiment of BTEX oxidation to generate high oxygen organic matter and secondary organic aerosol;

[0106] S24. Compare the simulated variable values ​​obtained by simulation with the observed variable values.

[0107] In step S21, the experimental data of the smoke box or oxidation flow tube are collected, specifically:

[0108] First, it is necessary to collect experimental data to measure the generation of OOMs and SOA by the oxidation of BTEX. To reduce the uncertainty of parameters, the present invention will select smoke chamber and flow tube experiments with different oxidant exposure levels, precursor concentrations, and residence times for comparison. For the experiment of measuring OOMs, the embodiments of the present invention selected the experimental results of Molteni et al. (Formation of highlyoxygenated organic molecules from aromatic compounds. Atmospheric Chemistry and Physics 2018, 18, (3), 1909-1921) and Cheng et al. (Highly oxygenated organic molecules produced by the oxidation of benzene and toluene in a wide range of OH exposure and NOx conditions. Atmospheric Chemistry and Physics 2021, 21, (15), 12005-12019), wherein the OH exposure in the former experiment was low, there was no nitrogen oxide input, and four precursors of toluene, ethylbenzene, xylene and trimethylbenzene were covered, while the latter experiment was carried out under higher OH exposure, and experiments with no nitrogen oxides and high nitrogen oxides were carried out respectively, and the precursor was only toluene. The observation instruments for these experiments are all Nitrate-CIMS, and the molecular formula and concentration of the substances measured need to be collected. For the experiment of measuring SOA, the present invention embodiment conducted a set of oxidation flow tube experiments and collected the smoke box experimental data of Hildebrandt Ruiz et al. (Formation and aging of secondary organic aerosol from toluene: changes in chemical composition, volatility, and hygroscopicity. Atmospheric Chemistry and Physics 2015, 15, (14), 8301-8313). The precursors of both experiments were toluene, and seeds were added in the experiments to achieve gas-particle equilibrium within the reaction time. The former was conducted under the condition of no nitrogen oxides, and a set of experiments was conducted under low to high OH exposure. The latter was conducted under high nitrogen oxide conditions, and its maximum OH exposure was lower than the former. The data required for both experiments are the changes in SOA concentration and oxygen-carbon ratio with oxidation time.

[0109] In step S22, the collected experimental data are corrected and reprocessed, specifically:

[0110] After collecting experimental data, since the raw data of OOMs are molecular formula and concentration, it is necessary to calculate the volatility of OOMs molecules, oxygen-carbon ratio, and calculate their concentration or yield in different volatility ranges. Previous studies usually use the number of C, O, and N in the molecule to estimate the volatility of the molecule. Generally, the volatility of certain molecules is estimated by direct measurement or using the SIMPOL model through the functional groups of the molecule, and the regression coefficient is obtained by linear regression, resulting in the formula shown in formula (14):

[0111]

[0112] In formula (14), is the logarithm of the saturated vapor concentration of the molecule at 300K, representing the volatility of the molecule, n C , n O and n N Represents the number of molecules C, O, and N, n 0 C =25, b C , b O and b N is the linear regression coefficient, and its physical meaning is the decrease in volatility (logarithm of saturated vapor concentration) caused by adding one C, O or N atom molecule. Different parameters for estimating molecular volatility are proposed for different precursors, and previous studies on benzene series believed that b C , b O and b N . However, the peroxybicyclic product of benzene series has certain particularities. There are two peroxide groups in the molecule. According to the SIMPOL model, the volatility will be reduced by 0.4 (logC* is reduced by 0.4), while according to formula (14), the two peroxide groups will reduce the volatility by 6.2. In addition, the self-oxidation of the peroxybicyclic free radical will add a hydrogen peroxide group to the molecule. In SIMPOL, this will reduce the volatility by 2.4, while the volatility reduction calculated according to formula (14) is 3.1. Therefore, it is necessary to revise the volatility estimation method of the peroxybicyclic product. The volatility estimation method for the peroxybicyclic product takes into account Ox n All possible RO and ROOH products of RO2. For example, C7H9O5(Ox0RO2) has three possible products RO(C7H8O4), ROH(C7H 10 O4) and ROOH(C7H 10O6), compared with Ox0RO2, their log C* reduction values ​​are: -0.8 (increase carbonyl group, removal of peroxide), -2.0 (increase hydroxyl group, removal of peroxide) and -2.2 (increase hydroperoxide group, removal of peroxide). n RO2 and Ox n+1 The volatility change between RO2 and its corresponding products is attributed to the addition of hydroperoxide groups. Subsequently, the volatility, C, O, and N numbers estimated by SIMPOL for all these products are listed and linear regression is performed. For the monomers of the peroxybicyclic products, b O Take 1.2, b C Take 0.52, and for the double body of the peroxybicyclic product, b C Take 0.89. For other observed products, still adopt the scheme before correction.

[0113] In addition, for the measured concentration of OOMs, it is necessary to consider its loss in the particle phase. For example, in the experiment of Cheng et al. (Highly oxygenated organic molecules produced by the oxidation of benzene and toluene in a wide range of OH exposure and NOx conditions. Atmospheric Chemistry and Physics 2021, 21, (15), 12005-12019), due to the high OH exposure, more SOA was generated during the experiment, and OOMs molecules would be lost on the surface of particles, resulting in the measured OOMs concentration being lower than the OOMs generated by the precursor in the gas phase reaction. In order to correct for such losses, it is necessary to combine the experimentally measured condensation sink (CS) size of OOMs in the particle phase and calculate the correction factor of OOMs yield according to the method proposed by Cheng et al. (Highly oxygenated organic molecules produced by the oxidation of benzene and toluene in a wide range of OH exposure and NOx conditions. Atmospheric Chemistry and Physics 2021, 21, (15), 12005-12019). The corrected OOMs concentration is obtained from formula (15):

[0114]

[0115] In formula (15), c irefers to the concentration of the i-th OOMs molecule measured, [OOMs] is the total OOMs concentration measured, ΔVOC is the concentration of the precursor consumed in the experiment, and γ is the correction factor. In addition, the wall loss of gas phase and particle phase materials needs to be considered. For experiments that do not consider wall loss, the k mentioned above is determined according to its experimental conditions. wall and k vap The size of the wall loss is simulated in the model.

[0116] In steps S23-S24, the corrected and reprocessed data are input into the comprehensive two-dimensional volatility interval model of benzene series to simulate the experiment of benzene series oxidation to generate high oxygen organic matter and secondary organic aerosol; the simulated variable values ​​obtained by simulation are compared with the observed variable values, specifically:

[0117] After collecting the experimental data and making corrections, the data can be input into the comprehensive two-dimensional volatility interval model of benzene series according to the conditions such as the oxidant exposure amount, reaction time, precursor concentration, nitrogen oxide concentration, etc. to simulate the generation of benzene series OOMs and SOA. That is, the data obtained by correction and reprocessing are used as the values ​​of the initial model parameters to carry out simulation experiments. The model parameters include the cracking ratio, gas phase oxidation rate, heterogeneous oxidation rate, the ratio of self-oxidizable free radicals, the ratio of dimers generated, the self-oxidation rate, the dimerization rate and the free radical chain termination reaction rate as mentioned above.

[0118] The volatility of OOMs yield, oxygen-carbon ratio distribution, SOA concentration, and oxygen-carbon ratio obtained by simulation need to be compared with the observed variable values. In the comparison process, the normalized mean bias (NMB) between simulation and observation needs to be calculated to quantitatively evaluate the deviation between simulation and observation, as shown in formula (16), where S i refers to the simulated value of the ith experiment (volatility of OOMs yield, oxygen-carbon ratio distribution or SOA concentration, oxygen-carbon ratio), O i refers to the observed value of the ith experiment, and n is the number of volatility / oxygen-carbon ratio intervals:

[0119]

[0120] However, due to the different sensitivities of CIMS to different OOMs molecules, the sensitivity to some products with low oxygen content and high volatility is low, so the errors in the observed concentrations of these products may be large. Therefore, when calculating the NMB of OOMs yield, only products with low volatility and high oxygen-carbon ratio are considered. For monomers and nitrates, the NMB of observed and simulated OOMs yields with log C*≤-1, O:C≥0.6 is calculated; for dimers, the NMB of observed and simulated OOMs yields with log C*≤-2, O:C≥0.3 is calculated. The observed OOMs yield only needs to take the yield in the corresponding volatility and oxygen-carbon ratio range, while the simulated yield needs to determine the products involved in the NMB calculation based on the sensitivity of CIMS to different products. The model can distinguish between products that have not undergone self-oxidation, first, second, and third stage self-oxidation. For products that have undergone self-oxidation, it is generally believed that CIMS has a higher sensitivity to them and they are easy to measure. However, for non-self-oxidized products with certain specific structures, such as non-self-oxidized dimers, R=O and R-OH products that are not self-oxidized under high nitrogen oxide conditions, since there are not many oxygen groups in their molecules and their sensitivity is lower than that of other main products, such products are considered to be partial detection products. When calculating NMB, only products that are easy to measure are considered, and partial detection products are not considered. To calculate the NMB of SOA concentration and oxygen-carbon ratio, it is only necessary to directly compare the simulated value with the observed value. Generally speaking, it is believed that the acceptable error range of NMB for OOMs yield is -0.5 to 1, and the NMB for SOA concentration and oxygen-carbon ratio is acceptable at around ±0.1.

[0121] After step S2, a preliminary comparison result is obtained, and then step S3 is entered;

[0122] In step S3, based on the preliminary comparison results and the normalized mean error between the simulated variable values ​​and the observed variable values, the model parameters of the comprehensive two-dimensional volatility interval model are optimized and adjusted, including:

[0123] S31, determining the model parameters to be optimized and adjusting the model parameters to be optimized according to the results of the preliminary comparison and the normalized mean error between the simulated variable values ​​and the observed variable values;

[0124] S32, cyclically repeating the steps of “simulating the generation of an experiment of high oxygen organic matter and secondary organic aerosol”, “comparing the simulated variable value with the observed variable value”, and “determining the model parameter to be optimized, and adjusting the model parameter to be optimized” until the normalized mean error between the simulated variable value and the observed variable value is within a predetermined range;

[0125] S33. Conduct sensitivity analysis on model parameters to verify the rationality of the optimized model parameters.

[0126] In S31, the model parameters to be optimized and the adjustment direction of the model parameters to be optimized are determined according to the results of the preliminary comparison, specifically:

[0127] According to step S2, a preliminary comparison result is obtained, which will show the deviation between the simulated variable value and the observed variable value (in the embodiment of the present invention, NMB is used to well characterize the deviation between the two), and the relationship between the deviation and the predetermined range is determined. The predetermined range is determined based on existing research. In this embodiment, as mentioned above, it is believed that the NMB acceptable error range of OOMs yield is -0.5 to 1, and the NMB of SOA concentration and oxygen-carbon ratio is acceptable at about ±0.1;

[0128] If the deviation is within the predetermined range, it is determined that the model parameters corresponding to the deviation do not need to be optimized and adjusted;

[0129] If the deviation is not within the predetermined range, the model parameter corresponding to the deviation is determined as the model parameter to be optimized, and the model parameter to be optimized is increased or decreased;

[0130] The present embodiment provides the preliminary comparison results obtained through step S2, as shown in Table 1.

[0131] Table 1

[0132]

[0133] Table 3 Figure 3 and Figure 4Molteni et al., 2018, (toluene, ethylbenzene, trimethylbenzene, xylene) are derived from the literature Formation of highly oxygenated organic molecules from aromatic compounds. Atmospheric Chemistry and Physics 2018, 18, (3), 1909-1921; Cheng et al., 2021, (low nitrogen oxides, high nitrogen oxides) are derived from the literature Highly oxygenated organic molecules produced by the oxidation of benzene and toluene in a wide range of OH exposure and NOx conditions. Atmospheric Chemistry and Physics 2021, 21, (15), 12005-12019, and Hildebrandt Ruiz et al., 2015 in Table 3 are derived from the literature Formation and aging of secondary organic aerosol from toluene: changes in chemical composition, volatility, and hygroscopicity. Atmospheric Chemistry and Physics 2015,15,(14),8301-8313.

[0134] For example, the simulation of the initial model parameters found that the yields of OOMs monomers and dimers were greatly underestimated, which may be caused by the low self-oxidation rate of the initial model parameters, or the low multi-stage oxidation rate. However, according to the observed OOMs mass spectrum, a relatively regular peak with a difference of 2 O was found, which indicates that a considerable part of the products in the experiment were produced by self-oxidation, so it is judged that the self-oxidation rate of the initial parameters may be low. In addition, the SOA concentration of the initial parameters was significantly overestimated, while the oxygen-carbon ratio was significantly underestimated. This can be judged that the yield of low-volatility and low-oxygen-carbon ratio substances may be overestimated in the simulation, so it can be judged that the dimer without self-oxidation is overestimated, and the dimerization ratio of RO2 radicals without self-oxidation may need to be lowered in the optimization parameters. For the experiment under high nitrogen conditions, the simulated values ​​of OOMs and SOA are consistent with the experimental values. In the experiment, the concentration of nitrogen oxides is high, and unconventional free radical reactions such as self-oxidation are suppressed. This shows that the deviation caused by the parameters of the primary oxidation and multi-stage oxidation modules of the model may not be obvious, so parameter optimization may need to focus on the free radical reaction module. According to the highly underestimated monomers and dimers in the simulation results, the self-oxidation rate of the model needs to be increased. When the simulation results of the self-oxidized monomers can basically meet the experimental values, the dimerization rate and ratio are adjusted so that the yield of the self-oxidized dimers can meet the experimental values. Table 2 shows the optimized values ​​of the important parameters after adjustment:

[0135] Table 2

[0136]

[0137]

[0138]

[0139] In step S32, the steps of "simulating the generation of high oxygen organic matter and secondary organic aerosol experiment", "comparing the simulated variable values ​​with the observed variable values" and "determining the model parameters to be optimized and adjusting the optimization parameters" are repeated cyclically until the normalized mean error between the simulated variable values ​​and the observed variable values ​​is within a predetermined range, that is:

[0140] The model parameters optimized and adjusted in step S31 are assigned to the comprehensive two-dimensional volatility interval model of benzene series again, and the OOMs and SOA production experiment simulation is performed; in the process of assigning the comprehensive two-dimensional volatility interval model of benzene series again, the initial assignment of the model parameters that have not been adjusted is retained;

[0141] And compare the simulated variable values ​​with the observed variable values;

[0142] If the comparison results show that the normalized mean error between the simulated variable value and the observed variable value is within the predetermined range, exit;

[0143] If the comparison result shows that the normalized mean error between the simulated variable value and the observed variable value is not within the predetermined range, the steps of "determining the model parameters to be optimized and adjusting the optimization parameters based on the comparison result", "simulating the experiment of generating high oxygen organic matter and secondary organic aerosol" and "comparing the simulated variable value and the observed variable value" are repeated cyclically until the normalized mean error between the simulated variable value and the observed variable value is within the predetermined range, and the optimization adjustment of all model parameters is completed.

[0144] In the embodiments of the present invention, NMB values ​​compared after secondary simulation of the embodiments in Table 1 and Table 2 are shown by way of example, as shown in Table 3.

[0145] Table 3

[0146]

[0147] From the NMB values ​​of each experiment, it can be seen that the optimized parameters can better simulate the yield of OOMs, SOA concentration, and oxygen-carbon ratio. Figure 3 and Figure 4 As shown in the figure, the volatility and oxygen-carbon ratio distributions of the OOMs yield are compared, and it is found that the simulated values ​​can reproduce the observed OOMs yield distribution well, thus proving that the model can reproduce the distribution of OOMs products in two dimensions: volatility and oxygen-carbon ratio.

[0148] In step S33, a sensitivity analysis of model parameters is performed to verify the rationality of the optimized model parameters, specifically:

[0149] In order to verify the rationality of the optimized parameters, it is also necessary to perform a model parameter sensitivity analysis on the parameters in the three modules of the model and compare them with the simulation results after optimizing the parameters. In the embodiment of the present invention, the peroxybicyclic path ratio of the primary oxidation module, the self-oxidation rate, dimerization rate, dimerization ratio and nitrate generation ratio of the free radical reaction module, and the gas phase oxidation rate and cracking ratio of the multi-stage oxidation module are exemplarily selected for model parameter sensitivity analysis, and the results are shown in Table 4. From the results of the sensitivity analysis, it is found that changing the optimized model parameters within a reasonable range will cause the simulation error to be higher than the optimized parameters, thereby proving the rationality of the optimized parameters;

[0150] If the results of the model parameter sensitivity analysis prove that the optimization of the optimization parameters is unreasonable, then return to the model parameter optimization adjustment step. In the actual optimization process, the steps of "simulating the generation of high oxygen organic matter and secondary organic aerosol experiments", "comparison of simulated variable values ​​and observed variable values" and "determining the model parameters to be optimized and adjusting the optimization parameters" are repeated in a loop according to step S32 until the normalized mean error between the simulated variable values ​​and the observed variable values ​​is within a predetermined range, and then the model parameter sensitivity analysis results are performed. The analysis results do not show the irrationality of the optimization parameters, which shows the rationality of the optimization parameters.

[0151] Table 4

[0152]

[0153]

[0154] In step S4, based on the optimization and adjustment results, a box model for the oxidation of benzene series to generate organic substances with a full volatile range is obtained, that is, step S3 is completed, the optimization and adjustment of the model parameters in the I2D-VBS model is completed, and finally a box model for the oxidation of benzene series to generate organic substances with a full volatile range is obtained.

[0155] like Figure 5 As shown, the system for constructing a box model of generating full-volatile organic compounds by oxidation of benzene series according to an embodiment of the present invention includes:

[0156] Model framework construction module 1 is used to construct a comprehensive two-dimensional volatility interval model of benzene series based on the existing smoke chamber experiments, chemical kinetics and theoretical calculations of benzene series oxidation;

[0157] The comparison module 2 is used to obtain preliminary comparison results based on the comparison of the simulated parameter values ​​and the observed parameter values, wherein the simulated parameter values ​​are obtained by simulating the oxidation of benzene series to generate high oxygen-containing organic matter and secondary organic aerosols through a comprehensive two-dimensional volatility interval model;

[0158] The optimization and adjustment module 3 is used to optimize and adjust the model parameters of the comprehensive two-dimensional volatility interval model according to the results of the preliminary comparison and the normalized average error between the simulated variable value and the observed variable value;

[0159] The completion module 4 is used to obtain a box model of full-volatile organic compounds generated by oxidation of benzene series based on the optimization adjustment results.

[0160] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for constructing a box model of generating organic compounds with a full volatility range by oxidation of benzene series, characterized in that: include: Based on the existing smoke chamber experiments, chemical kinetics and theoretical calculations of BTEX oxidation, a comprehensive two-dimensional volatility interval model of BTEX was constructed. Based on the comparison between the simulated variable values ​​and the observed variable values, a preliminary comparison result is obtained, wherein the simulated variable values ​​are obtained by simulating the oxidation of benzene series to generate highly oxygenated organic matter and secondary organic aerosols using a comprehensive two-dimensional volatility interval model; Based on the results of the preliminary comparison and the normalized mean errors between the simulated and observed variable values, the model parameters of the comprehensive two-dimensional volatility interval model are optimized and adjusted; Based on the optimization and adjustment results, a box model of full-volatile organic compounds generated by oxidation of benzene series was obtained; The method is based on the existing smoke box experiment, chemical kinetics and theoretical calculation of the oxidation of benzene series to construct a comprehensive two-dimensional volatility interval model of benzene series, including: Based on the existing smoke chamber experiments, chemical kinetics and theoretical calculations of BTEX oxidation, a primary oxidation module of the comprehensive two-dimensional volatility interval model of BTEX was established. Based on the existing smoke chamber experiments, chemical kinetics and theoretical calculations of BTEX oxidation, a free radical reaction module of the comprehensive two-dimensional volatility interval model of BTEX was established. Based on the existing smoke chamber experiments, chemical kinetics and theoretical calculations of BTEX oxidation, a multi-stage oxidation module of the comprehensive two-dimensional volatility interval model of BTEX was established. The primary oxidation module is used to simulate the process of organic matter forming first-level stable molecules or free radicals under the oxidation of OH / O3 / NO3 free radicals; The free radical reaction module is used to simulate the chain oxidation and termination reactions of different free radicals; when the free radical reaction module is established, different free radical module parameters are used to distinguish between monosubstituted benzene series and polysubstituted benzene series; The model parameters of the box model for the oxidation of benzene series to generate organic compounds with a full volatility range include f bcp 、f cresol , f benzaldehyde , f bcp_Ox , k1 , k2, k3 , k00 , k11 , k22, k33 , f 0,0 , f 0,n , f n,n , k(RO 2 +HO 2 ) , k(RO 2 +NO) , f (RONO 2 ) , k basis , k het and n_frag; in, f bcp is the ratio of peroxybicyclic products, f cresol is the ratio of the cresol pathway, f benzaldehyde is the ratio of the benzaldehyde pathway, f bcp_Ox is the ratio of self-oxidizable peroxybicyclic radicals, all of which are the ratio of primary oxidation products; k1 , k2, k3 They correspond to the first-order self-oxidation rate, the second-order self-oxidation rate and the third-order self-oxidation rate, respectively, and all belong to the self-oxidation rate; k00 , k11 , k22, k33 They correspond to the dimerization rate of RO2 without self-oxidation, the dimerization rate of RO2 of the first stage of self-oxidation, the dimerization rate of RO2 of the second stage of self-oxidation and the dimerization rate of RO2 of the third stage of self-oxidation, all of which belong to the dimerization rate; f 0,0 , f 0,n , f n,n They correspond to different dimerization ratios, where n = 1, 2, and 3; k(RO 2 +HO 2 ) is the RO2+HO2 termination reaction rate; k(RO 2 +NO) is the RO2+NO termination reaction rate; f(RONO 2 ) is the nitrate production ratio; k basis is the gas phase oxidation rate; k het is the heterogeneous oxidation rate; n_frag is the cracking ratio.

2. The method for constructing a box model of full-volatility organic compounds generated by oxidation of benzene series according to claim 1, characterized in that: The variables include volatility and oxygen-carbon ratio distribution of OOMs carbon yield, yield and oxygen-carbon ratio of SOA, wherein OOMs is the abbreviation of highly oxygenated organic matter, and SOA is the abbreviation of secondary organic aerosol.

3. The method for constructing a box model of generating full-volatility organic compounds by oxidation of benzene series according to claim 1, characterized in that: The multi-stage oxidation module is used to simulate the multi-stage oxidation of products driven by OH in the two-dimensional space of volatility and oxygen-carbon ratio.

4. The method for constructing a box model of generating full-volatility organic compounds by oxidation of benzene series according to claim 1, characterized in that: The multi-stage oxidation module is established, comprising: Determine upper and lower limits for volatility and oxygen-to-carbon ratio; Set volatility ranges and intervals; The range and interval of oxygen-carbon ratio will be set; Determine the distribution matrix of multi-stage oxidation products.

5. The method for constructing a box model of generating full-volatility organic compounds by oxidation of benzene series according to claim 1, characterized in that: The comparison between the simulated parameter values ​​and the observed parameter values ​​provides preliminary comparison results, including: Collect data from smoke chamber or oxidation flow tube experiments; Correct and reprocess the collected experimental data; The corrected and reprocessed data were input into a comprehensive two-dimensional volatility interval model of BTEX to simulate the experiment of BTEX oxidation to generate highly oxygenated organic matter and secondary organic aerosols; The simulated parameter values ​​obtained from the simulation are compared with the observed parameter values.

6. The method for constructing a box model of generating full-volatility organic compounds by oxidation of benzene series according to claim 1, characterized in that: Based on the results of the preliminary comparison and the normalized mean error between the simulated and observed variable values, the model parameters of the comprehensive two-dimensional volatility interval model are optimized and adjusted, including: According to the results of the preliminary comparison and the normalized mean error between the simulated variable values ​​and the observed variable values, the model parameters to be optimized are determined, and the model parameters to be optimized are adjusted; Repeating the steps of "simulating the generation of high oxygen organic matter and secondary organic aerosol experiment", "comparing the simulated variable value with the observed variable value" and "determining the model parameter to be optimized and adjusting the model parameter to be optimized" in a loop until the normalized mean error between the simulated variable value and the observed variable value is within a predetermined range; Conduct sensitivity analysis of model parameters to verify the rationality of optimized model parameters.

7. The method for constructing a box model of generating full-volatility organic compounds by oxidation of benzene series according to claim 6, characterized in that: The model parameters include the fragmentation ratio, gas phase oxidation rate, heterogeneous oxidation rate, self-oxidizable free radical ratio, dimerization ratio, self-oxidation rate, dimerization rate and free radical chain termination reaction rate.

8. The method for constructing a box model of generating full-volatile organic compounds by oxidation of benzene series according to any one of claims 1 to 7, characterized in that: The calculation formula for the normalized mean error between the simulated variable value and the observed variable value is as follows: Where NMB is the abbreviation of normalized mean error, Si refers to the value of the i-th simulation parameter, Oi refers to the observed value of the i-th parameter, and n is the number of volatility / oxygen-carbon ratio intervals.

9. The system of the method for constructing a box model of full-volatile organic compounds generated by oxidation of benzene series according to claim 1, characterized in that: include: The model framework building module is used to construct a comprehensive two-dimensional volatility interval model of benzene series based on the existing smoke chamber experiments, chemical kinetics and theoretical calculations of benzene series oxidation; The comparison module is used to obtain preliminary comparison results based on the comparison of simulated variable values ​​and observed variable values, wherein the simulated variable values ​​are obtained by simulating the oxidation of benzene series to generate high oxygen organic matter and secondary organic aerosols through a comprehensive two-dimensional volatility interval model; The optimization and adjustment module is used to optimize and adjust the model parameters of the comprehensive two-dimensional volatility interval model according to the results of the preliminary comparison and the normalized average error between the simulated variable values ​​and the observed variable values; The completion module is used to obtain a box model of full-volatile organic compounds generated by oxidation of benzene series based on the optimization adjustment results; The method constructs a comprehensive two-dimensional volatility interval model of benzene series based on the existing smoke box experiment, chemical kinetics and theoretical calculation of benzene series oxidation, including: Based on the existing smoke chamber experiments, chemical kinetics and theoretical calculations of BTEX oxidation, a primary oxidation module of the comprehensive two-dimensional volatility interval model of BTEX was established. Based on the existing smoke chamber experiments, chemical kinetics and theoretical calculations of BTEX oxidation, a free radical reaction module of the comprehensive two-dimensional volatility interval model of BTEX was established. Based on the existing smoke chamber experiments, chemical kinetics and theoretical calculations of BTEX oxidation, a multi-stage oxidation module of the comprehensive two-dimensional volatility interval model of BTEX was established. The primary oxidation module is used to simulate the process of organic matter forming first-level stable molecules or free radicals under the oxidation of OH / O3 / NO3 free radicals; The free radical reaction module is used to simulate the chain oxidation and termination reactions of different free radicals; when the free radical reaction module is established, different free radical module parameters are used to distinguish between monosubstituted benzene series and polysubstituted benzene series.

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