Quantitative method for environmental vocs chemical loss based on air quality model on-off chemical mechanism

Abstract

The present application relates to a kind of environmental VOCs chemical loss quantitative method based on air quality mode switch chemical mechanism, comprising the following steps: step S1: environmental VOCs online observation data preprocessing and quality control;Step S2: parameter setting and simulation debugging of air quality mode;Step S3: simulation opens the VOCs lumped species concentration under the scenario of chemical mechanism;Step S4: simulation closes the VOCs lumped species concentration under the scenario of chemical mechanism;Step S5: quantification of the chemical loss rate (RLR) of specific VOCs species;Step S6: quantification of the chemical loss of environmental VOCs species.The present application can be based on the field data and chemical loss rate of target receptor point observation Quantification VOC's chemical loss concentration, high accuracy, and can estimate the chemical loss of VOCs all day.

Description

Technical Field

[0001] This invention relates to the field of air pollution control, and in particular to a quantitative method for the chemical depletion of environmental VOCs based on the air quality mode switching chemical mechanism. Background Technology

[0002] Volatile organic compounds (VOCs) are key precursors to the formation of ozone (O3) and secondary organic aerosols (SOA) in the atmosphere, and their chemical transformation typically occurs during transport from source emissions to receiver observation sites. Therefore, these VOCs that are depleted through reaction are the actual contributors to the formation of O3 and SOA in the atmosphere. With the significant increase in O3 and SOA concentrations in many cities worldwide in recent years, quantitative methods for studying the depletion of VOCs that form these compounds have received increasing attention.

[0003] Current methods for quantifying the chemical depletion of VOCs primarily utilize photochemical age parameters, which assume that the chemical depletion of environmental VOCs arises solely from the photochemical reactions between VOCs and ·OH radicals in the daytime atmosphere. This method mainly quantifies the daytime chemical depletion of VOCs by estimating the photochemical reaction age of VOC species and combining this with the reaction rate constant between VOC species and ·OH radicals. However, this empirical parameter method for quantifying the chemical depletion of VOC species has high uncertainty because it assumes that all VOC species observed at the same time have the same photochemical reaction age, ignores the influence of fresh emissions, and generally sets the reaction rate constant to a constant value at a specific temperature. Furthermore, the chemical reactions of VOC species with O3 and NO3 radicals at night are not considered.

[0004] In contrast, current air quality models can couple multiple atmospheric VOC chemical reaction mechanisms (including chemical reactions with ·OH radicals, O3 and NO3 radicals, etc.) through numerical expressions, and are now widely used in simulation analysis of the causes of environmental VOC and O3 pollution. Simultaneously, they can utilize meteorological field simulations and gridded emission inventory inputs to simulate the emission and transport processes of VOC species from different sources. These advantages effectively compensate for many shortcomings of traditional methods based on photochemical age parameters, such as the uniform photochemical reaction ages of VOC species from different sources, neglect of the influence of fresh pollutant emissions, a single reaction rate constant, and consideration only of photochemical reactions with ·OH radicals.

[0005] Therefore, it is necessary to provide a novel quantitative method for the chemical depletion of environmental VOCs based on the air quality model switching chemical reaction mechanism, in order to explore the conversion mechanism of environmental VOCs with O3 and SOA and its influencing factors. This is crucial for supporting the understanding of ozone and PM2.5. 2.5 The coordinated treatment of compound pollution plays an important role and has significance. Summary of the Invention

[0006] The purpose of this invention is to address the problems of high uncertainty and difficulty in estimating nighttime VOC chemical reaction losses in current traditional methods for quantifying VOC species chemical losses, and to provide a method for quantifying environmental VOC chemical losses based on the air quality mode switching chemical mechanism.

[0007] To achieve the above objectives, this invention provides a method for quantifying the chemical depletion of environmental VOCs based on an air quality mode switching chemical mechanism, comprising the following steps:

[0008] Step S1: Preprocessing and quality control of online environmental VOCs observation data. The environmental VOCs species concentration data comes from the target receptor monitoring points, with a time resolution of 1 hour. After selecting the VOCs species concentration data for the study period, abnormal data values ​​are deleted.

[0009] Step S2: Setting and calibrating the air quality model parameters, selecting the air quality model, coupling the carbon bond reaction mechanism, and simulating the chemical process of VOCs reaction in the environment;

[0010] Step S3: Simulate the lumped concentration of VOCs under the scenario of activating the chemical mechanism, and perform scenario simulation (CSS) analysis to obtain the lumped concentration of VOCs at the target receptor monitoring site. lumped,CSS );

[0011] Step S4: Simulate the lumped concentration of VOCs under the scenario of chemical mechanism shutdown, perform scenario simulation (SS) analysis to obtain the lumped concentration of VOCs at the target receptor monitoring site (C lumped,DSS );

[0012] Step S5: Quantify the chemical loss rate (RLR) of a specific VOC species. Calculate the environmental concentrations of a specific VOC species at the target receptor monitoring site after chemical loss and before chemical loss, and obtain the chemical loss rate (RLR) of the specific VOC species based on the environmental concentrations after and before chemical loss.

[0013] Step S6: Quantify the chemical depletion of environmental VOC species. Subtract the chemical depletion from the initial concentration to obtain the observed concentration of a specific VOC species. At the same time, the chemical depletion is equal to the product of the initial concentration and the chemical depletion rate (RLR) to obtain the chemical depletion of environmental VOC species.

[0014] Preferably, the air quality model adopts the Lambert projection coordinate system, is set as a double-layer nested grid, the center of the nested simulation domain is located at the target receptor monitoring point, the pollution source input data of the grid includes emission inventory data of common source types, the WRF model is used to generate meteorological field and underlying surface data for the air quality model simulation, and each simulation starts 72 hours in advance.

[0015] Preferably, step S2 further includes: evaluating the model simulation effect after parameter setting, wherein the model simulation effect is evaluated by comparing the observed values ​​of meteorological and pollutant concentrations at the receptor observation points with the simulated values ​​of the model, and the evaluation indicators include the correlation coefficient (R), normalized mean deviation (NMB), root mean square error (RMSE), and mean deviation (NB) between the simulation and measurement results.

[0016] Preferably, in step S5, the step of calculating the environmental concentrations of specific VOCs species at the target receptor monitoring site after chemical reaction loss and without chemical reaction loss includes: using equation (1) to calculate the lumped species concentration (CL) of the receptor monitoring site obtained in steps S3 and S4. lumped,CSS ) and (C lumped,DSS This is inversely converted to the concentration of a specific VOC species at that receptor site:

[0017]

[0018] Among them, C i,specific C represents the concentration of a specific VOC species i. j,lumped nij represents the concentration of ensemble VOC species j; nij represents the number of ensemble species j in a specific species i; Nj represents the total number of ensemble species j in all specific species.

[0019] Preferably, in step S5, the step of obtaining the chemical attrition rate (RLR) of a specific VOC species based on the environmental concentrations of the specific VOC species after chemical attrition and those without includes:

[0020] First, from DSS(C) i,specific,DSS Subtract the specific species from the CSS (C) i,specific,CSS The concentration of ) was then determined by dividing the difference by C. i,specific,DSS To obtain the RLR of a specific VOC species, calculate equation (2):

[0021]

[0022] Preferably, in step S6, the step of obtaining the chemical depletion of environmental VOC species includes: the chemical depletion of environmental VOC species is calculated by equation (3):

[0023]

[0024] Among them, RL i,obs RLR represents the observed chemical loss of VOC species i. i,specific C represents the chemical loss rate of a specific VOC species i. i,obs The observed concentration represents VOC species i.

[0025] Based on the above technical solution, the advantages of the present invention are:

[0026] This invention proposes a quantitative method for the chemical depletion of environmental VOCs based on the switching chemical reaction mechanism of air quality models. Since VOC chemical depletion is a significant contributor to secondary pollutants in the atmosphere, this invention leverages the advantages of air quality models, using an air quality model coupled with carbon bond chemical mechanisms to obtain detailed chemical depletion rates for specific species. This invention can accurately quantify the chemical depletion concentration of VOCs based on field data and chemical depletion rates observed at target receptor sites, fully considering the atmospheric physical and chemical processes that VOCs undergo from emission sources to monitoring sites. It boasts high accuracy and can estimate the chemical depletion of VOCs throughout the day. Attached Figure Description

[0027] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0028] Figure 1 This is a flowchart illustrating the steps of the method for quantifying the chemical depletion of environmental VOCs based on the air quality mode switching chemical mechanism of the present invention.

[0029] Figure 2 This is a flowchart illustrating the principle of the present invention: a quantitative method for environmental VOCs chemical loss based on an air quality mode switching chemical mechanism. Detailed Implementation

[0030] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0031] This invention provides a quantitative method for environmental VOCs chemical depletion based on an air quality mode switching chemical mechanism, such as... Figure 1 , Figure 2 As shown, a preferred embodiment of the present invention is illustrated.

[0032] Specifically, such as Figure 1 As shown, the method for quantifying the chemical depletion of environmental VOCs includes the following steps:

[0033] Step S1: Preprocessing and quality control of online environmental VOCs observation data. The environmental VOCs species concentration data comes from the target receptor monitoring points, with a time resolution of 1 hour. After selecting the VOCs species concentration data for the study period, abnormal data values ​​are deleted.

[0034] Environmental VOCs species concentration data were primarily obtained from target receptor monitoring sites, with a time resolution of 1 hour, and mainly included alkanes, alkenes, aromatics, and alkynes. After selecting VOCs species concentration data for the study period, quality control processing was performed to remove outlier values.

[0035] Step S2: Parameter setting and simulation debugging of air quality model, select air quality model, couple carbon bond reaction mechanism, and simulate the chemical process of VOCs reaction in the environment.

[0036] The air quality model uses the Lambert projected coordinate system and is configured with a double-nested grid, with the center of the nested simulation domain located at the target receptor monitoring point. The pollution source input data for the grid includes emission inventory data for common source classes. The WRF model is used to generate meteorological fields and underlying surface data for the air quality model simulation. Each simulation begins 72 hours in advance to eliminate the influence of initial and boundary conditions. Air quality models are primarily used to simulate the emission, transport, chemical reactions, and removal of tropospheric pollutants. The WRF model, short for Weather Research and Forecasting, is a numerical weather prediction tool that provides meteorological input data for air quality models.

[0037] To ensure reasonable parameter settings and excellent simulation results for the air quality model, the model's simulation performance is evaluated after parameter settings are configured. The model's simulation performance can be evaluated by comparing the observed meteorological and pollutant concentrations at the receptor observation points with the model's simulated values. Evaluation indicators mainly include the correlation coefficient (R), normalized mean deviation (NMB), root mean square error (RMSE), and mean deviation (NB) between the simulation and measurement results.

[0038] Step S3: Simulate the lumped concentration of VOCs under the scenario of activating the chemical mechanism, and perform scenario simulation (CSS) analysis to obtain the lumped concentration of VOCs at the target receptor monitoring site. lumped,CSS ).

[0039] After setting and debugging the air quality model parameters, a scenario simulation (CSS) analysis with the chemical reaction mechanism activated was first performed to obtain the lumped VOCs species concentration (C0.05) at the target receptor monitoring points. lumped,CSSVOCs concentration represents the environmental concentration after chemical reactions and losses during their transmission from source emissions to the target receptor monitoring point. Lumped species, in particular, refers to species derived from the aggregation and transformation of specific VOCs species according to different rules using inductive chemistry mechanisms in air quality models.

[0040] Step S4: Simulate the lumped concentration of VOCs under the scenario of chemical mechanism shutdown, perform scenario simulation (SS) analysis to obtain the lumped concentration of VOCs at the target receptor monitoring site (C lumped,DSS ), which represents the environmental concentration that has not been lost through chemical reactions during the transmission process from the source emission to the target receptor monitoring point.

[0041] Step S5: Quantify the chemical loss rate (RLR) of a specific VOC species. Calculate the environmental concentrations of a specific VOC species at the target receptor monitoring site after chemical loss and before chemical loss, and obtain the chemical loss rate (RLR) of the specific VOC species based on the environmental concentrations after and before chemical loss.

[0042] like Figure 2 As shown, estimating the chemical depletion rate of a specific VOC species includes the following two sub-steps:

[0043] (1) Calculate the environmental concentration of specific VOC species at the target receptor monitoring site after chemical reaction loss and before chemical reaction loss.

[0044] To ensure that the VOC species simulated using different chemical mechanisms match the species observed at the target receptor monitoring sites, specific species defined in carbon-bonding mechanisms (such as CB05) can be used to summarize the lumped species conversion coefficient. The lumped species concentration (C05) of the receptor monitoring sites simulated in steps S3 and S4 above can then be used as the conversion coefficient. lumped,CSS and C lumped,DSS The concentration of VOCs at the receptor site is converted in reverse to the concentration of a specific VOC species, i.e., the VOC species consistent with the receptor monitoring site. The specific conversion method is shown in equation (1) below:

[0045]

[0046] Where C i,specific C represents the concentration of a specific VOC species i. j,lumped The concentration of VOC species j represents the total concentration of the lumped-total VOC species; n ij N represents the number of species j in a specific species i; j It represents the total number of ensemble species j among all specific species.

[0047] (2) Quantitative chemical loss rate (RLR) of specific VOC species.

[0048] First, from DSS(C i,specific,DSS Subtract the specific species from the CSS (C) i,specific,CSS The concentration of ). Then, by dividing the difference by C i,specific,DSS The RLR of a specific VOC species was obtained. The calculation method is detailed in equation (2):

[0049]

[0050] Step S6: Quantify the chemical depletion of environmental VOC species. Subtract the chemical depletion from the initial concentration to obtain the observed concentration of a specific VOC species. At the same time, the chemical depletion is equal to the product of the initial concentration and the chemical depletion rate (RLR) to obtain the chemical depletion of environmental VOC species.

[0051] Since the observed concentration of a specific VOC species can be obtained by subtracting chemical loss from the initial concentration (i.e., the environmental concentration before chemical loss occurs), and chemical loss can be equal to the initial concentration multiplied by RLR, the chemical loss of environmental VOC species can be calculated by the following formula (3) through formula derivation:

[0052]

[0053] RL i,obs RLR represents the observed chemical loss of VOC species i. i,specific C represents the chemical loss rate of a specific VOC species i. i,obs The observed concentration represents VOC species i.

[0054] This invention proposes a quantitative method for environmental VOCs chemical depletion based on the switching chemical reaction mechanism of air quality models. Since VOCs chemical depletion is a significant contributor to secondary pollutants in the atmosphere, this invention leverages the advantages of air quality models, using an air quality model coupled with carbon bond chemical mechanisms to obtain detailed chemical depletion rates for specific species. This invention can accurately quantify VOCs chemical depletion concentrations based on field data and chemical depletion rates from target receptor sites, achieving high accuracy, and can estimate VOCs chemical depletion throughout the day.

[0055] To further illustrate the quantitative method for environmental VOCs chemical depletion based on the air quality mode switching chemical mechanism of the present invention, the Nankai University Atmospheric Pollutant Super Observation Station in Jinnan District, Tianjin (hereinafter referred to as "Nankai University Super Station") was used as the research site. Three typical ozone pollution events from August 6th to 12th, 2021 (i.e., Pollution Event 1#, August 6-12, 143 hours; Pollution Event 2#: August 16-17, 15 hours; Pollution Event 3#: August 23, 23 hours) were used as examples to illustrate the specific implementation process of the present invention as follows:

[0056] Step S1: Preprocessing and quality control of online environmental VOCs monitoring data:

[0057] The hourly resolution VOC species monitored by the Nankai University superstation included 52 PAMS species (i.e., 27 alkanes, 10 alkenes, 14 aromatics, and 1 alkyne). Specifically, the VOC species included: ethane, propane, isobutane, n-butane, 2,2-dimethylbutane, 2,3-dimethylbutane, n-pentane, isopentane, cyclopentane, methylcyclopentane, 2-methylpentane, 3-methylpentane, 2,4-dimethylpentane, 2,3-dimethylpentane, 2,3,4-trimethylpentane, and 2,2,4-trimethylpentane. n-Hexane, 3-methylhexane, methylcyclohexane, cyclohexane, 2-methylhexane, n-heptane, 2-methylheptane, 3-methylheptane, n-octane, n-nonane, n-decane, ethylene, propylene, trans-2-butene, 1-butene, cis-2-butene, 1-pentene, trans-2-pentene, cis-2-pentene, isoprene, 1-hexene, benzene, toluene, ethylbenzene, o-xylene, cumene, n-propylbenzene, m-ethyltoluene, p-ethyltoluene, o-ethyltoluene, mesitylene, 1,2,4-trimethylbenzene, trimethylbenzene, m-diethylbenzene, p-diethylbenzene, acetylene.

[0058] Data quality control was performed on the VOCs species concentration data for the study period to remove outlier data and ensure the quality of the analytical data.

[0059] Step S2: Setting and simulating the parameters of the air quality mode;

[0060] The CAMx air quality model, coupled with the CB05 carbon bond mechanism, was used to simulate the chemical processes of VOCs reaction in the environment. The model was configured with a two-layer nested grid, with the center of the nested simulation domain located at the Nankai University supersite site. The pollution source input data for the first layer grid needed to include emission inventory data for common source types, such as power, industry, residential, transportation, agriculture, and natural sources. The emission source inventory for the target area required by the model was a dynamic, high-resolution gridded pollution source inventory, while the emission inventory for the surrounding area used the MEIC inventory.

[0061] Specifically, in this embodiment, the air quality model used is the 3D Integrated Air Quality Model Extended (CAMx-640) model. The system adopts the Lambert projection coordinate system, and the model is set as a two-layer nested mesh with mesh resolutions of 27 km × 27 km and 9 km × 9 km, respectively. The center of the nested simulation domain is located at the Nankai University superstation, with the two mesh layers having numbers of 87 × 87 and 113 × 113, respectively, and the model is divided into 39 layers in the vertical direction.

[0062] Specifically, the pollution source data for the first layer of the grid consists of anthropogenic emission data from the 2017 China Multi-Resolution Emission Inventory (MEIC), including emissions from five sources (power, industry, residential, transportation, and agriculture). Natural source emission data are derived from simulations using the natural gas and aerosol emission model version v2.04 (MEGAN v2.04).

[0063] Specifically, the WRF model was used to generate meteorological fields and underlying surface data for the CAMx-640 model. The initial and boundary conditions for the WRF model were based on North American regional reanalysis data archived by the National Center for Atmospheric Research (NCAR). Input data for the WRF model came from NCEP's FNL (Final Operational Global Analysis data) global reanalysis data, with a horizontal resolution of 1°×1° and a time interval of 6 hours. Underlying surface data were derived from USGS 30 global topography / MODIS underlying surface classification data. The vertical resolution of both the WRF and CAMx models included 39 layers from the Earth's surface to the tropopause (~100 mbar). Each simulation began 72 hours in advance to eliminate the influence of initial and boundary conditions.

[0064] The model simulation performance can be evaluated by comparing it with meteorological data (temperature, relative humidity, wind speed, wind direction) and TVOCs concentration data measured at the super-site monitoring station of Nankai University. The evaluation indicators mainly include the correlation coefficient (R) between the simulation and measurement results, their normalized mean deviation (NMB), root mean square error (RMSE), and mean deviation (NB). The results of these indicators for the three O3 pollution cases are shown in Table 3. The R values ​​between the meteorological data (temperature and relative humidity) simulated by the WRF model and the measurement data at the receptor site are relatively high, ranging from 0.81 to 0.91 and 0.63 to 0.90, respectively. The MB values ​​between the simulated and measured wind speeds range from 1.14 to 2.33 m / s, and the RMSE values ​​range from 13.9 to 25.2, both within reasonable ranges. The normalized mean deviations (NMB) of the simulated and measured TVOCs concentrations are 35.8%, -23.7%, and 53.7%, respectively, all within reasonable ranges (-55% to 85%). This demonstrates that the air quality model in this embodiment has an acceptable simulation effect on meteorological factors and pollutant concentrations.

[0065] Step S3: Simulate the total VOCs species concentration under the scenario of activating the chemical mechanism.

[0066] By conducting scenario simulation (CSS) analysis to initiate chemical reaction mechanisms, the lumped VOCs species concentration (CL) at the Nankai University supersite during three typical ozone pollution processes was obtained. lumped,CSS ), which represents the environmental concentration after chemical reaction loss during the transmission process from the source emission to the Nankai University supersite site.

[0067] Lumped species: refers to the species derived from the aggregation and transformation of specific VOCs species according to different rules using inductive chemistry mechanisms in air quality models. Step S4: Simulate the concentration of lumped VOC species under the scenario of shutting down the chemistry mechanism.

[0068] By conducting scenario simulation (SS) analysis of shutting down chemical mechanisms, the total VOCs species concentration (C) at the Nankai University superstation was obtained during three typical ozone pollution processes. lumped,DSS ), which represents the environmental concentration that has not been lost through chemical reactions during the transmission process from the source emission to the Nankai University supersite site.

[0069] Step S5: Quantify the chemical attrition rate (RLR) of a specific VOC species, including the following two sub-steps:

[0070] (1) Calculate the environmental concentrations of specific VOC species at the supersite of Nankai University after chemical reaction loss and without chemical reaction loss.

[0071] To ensure that the VOC species simulated by the CB05 chemical mechanism match the species observed at the Nankai University supersite, the transformation coefficients of the lumped species were summarized using specific species defined in the CB05 carbon bond mechanism (see Table 5). The lumped species concentrations (Cg) of the Nankai University supersite obtained from the simulations in steps S3 and S4 were then used as the basis for this calculation. lumped,CSS and C lumped,DSS The concentration of VOCs species in the environment at that location is then converted back to the concentration observed at that location. The specific conversion method is shown in equation (1) below:

[0072]

[0073] Where C i,specific Represents specific VOC species i The concentration of C j,lumped The concentration of VOC species j represents the total concentration of the lumped-total VOC species; n ij N represents the number of species j in a specific species i; j It represents the total number of ensemble species j among all specific species.

[0074] (2) Quantitative analysis of the chemical loss rate (RLR) of specific VOC species;

[0075] First, from DSS(C i,specific,DSS Subtract the specific species from the CSS (C) i,specific,CSS The concentration of ). Then, by dividing the difference by C i,specific,DSS The RLR for a specific species is obtained. The calculation method is detailed in equation (2):

[0076]

[0077] Step S6: Quantify the chemical depletion of environmental VOC species.

[0078] Since the observed concentration of a specific VOC species can be obtained by subtracting chemical loss from the initial concentration (i.e., the environmental concentration before chemical loss occurs), and chemical loss can be equal to the initial concentration multiplied by RLR, the chemical loss of environmental VOC species can be calculated by formula (3) through formula derivation:

[0079]

[0080] RL i,obs RLR represents the observed chemical loss of a specific VOC species i. i,specific C represents the chemical loss rate of a specific VOC species i. i,obs The observed concentration represents a specific VOC species i.

[0081] In this embodiment, the comparison results of the chemical depletion of VOCs species estimated using the novel method for quantifying the chemical depletion of environmental VOCs based on the air quality mode switching chemical mechanism of the present invention and the traditional method based on photochemical age parameters (hereinafter referred to as the traditional method) are shown in Table 6. Compared with other VOCs species, olefins had the highest chemical depletion concentrations (7.7-42.7 ppbv) in the three O3 pollution cases, accounting for 73.3%-90.8% of the total depletion. In the three O3 pollution cases, the chemical depletion ranges of alkanes, aromatic hydrocarbons, and alkynes were 0.94-2.33, 0.85-1.91, and 0.01-0.10 ppbv, respectively. Isoprene, cis-2-butene, and ethylene showed the highest chemical losses in all three O3 pollution cases, with average losses of 2.47–19.3, 3.51–20.0, and 1.47–2.5 ppbv, respectively, accounting for 23.5%–41.0%, 33.3%–42.5%, and 5.34%–16.41% of total TVOCs.

[0082] In this embodiment, to evaluate the advantages of the novel method for quantifying environmental VOCs chemical depletion based on the air quality mode switching chemical mechanism of the present invention compared with traditional methods, the chemical depletion concentrations of VOC species calculated by the two methods were compared. Since the traditional method based on photochemical age parameters can only estimate daytime chemical depletion, the differences in daytime chemical depletion estimated by the two methods in three pollution cases were compared.

[0083] The results showed that the abnormally high values ​​of olefin species estimated by the photochemical age-based parametric method were significantly higher than those of the novel method of this invention. For example, in pollution process 1#, the photochemical depletion concentrations of ethylene and isoprene estimated by the photochemical age-based parametric method were 22.0% and 8.06% higher than those of this method, respectively; in pollution process 2#, they were 17.0% and 27.2% higher, respectively; and in pollution process 3#, they were 27.5% and 25.5% higher, respectively.

[0084] Furthermore, this invention considers the atmospheric physical and chemical processes that VOCs undergo from the emission source to the environmental monitoring site, including the dynamics of photochemical depletion of freshly emitted VOCs in the atmosphere. VOCs emitted from vegetation sources in the surrounding green spaces are considered fresh pollutants. The key reactive species from these vegetation sources is isoprene. Therefore, the differences between the isoprene reaction losses estimated by this invention and those estimated using photochemical age parameter methods were compared in three pollution event cases. The results show that the estimation results of this invention are 17.5%, 53.6%, and 85.8% lower than those using photochemical age parameter methods, respectively. This fully demonstrates the applicability of this invention for estimating VOC chemical depletion in local pollution events.

[0085] Furthermore, this invention considers VOC reactions beyond ·OH radicals. Therefore, the chemical depletion of nighttime VOC species, neglected by parametric methods based on photochemical age, can be estimated. In this embodiment, the results of this invention show that in three O3 pollution cases, the nighttime reactivity depletion of TVOCs was 45.9, 10.5, and 9.97 ppbv, respectively, which were 4.57%, 0.00%, and 0.81% lower than during the day, respectively.

[0086] Table 1. Average concentrations and proportions of VOC species during three typical ozone pollution episodes.

[0087]

[0088]

[0089]

[0090] Table 2. Parameter settings for the air quality model in this embodiment.

[0091]

[0092]

[0093] Table 3 Evaluation results of model simulation effect

[0094]

[0095]

[0096]

[0097]

[0098] Table 6. Transformation coefficients between lumped species and specific species in the CB05 lumpedity mechanism.

[0099]

[0100]

[0101]

[0102]

[0103]

[0104]

[0105] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them; although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications can still be made to the specific implementation of the present invention or equivalent substitutions can be made to some technical features without departing from the spirit of the technical solutions of the present invention, and all such modifications and substitutions should be covered within the scope of the technical solutions claimed in the present invention.

Claims

1. A quantitative method for environmental VOCs chemical depletion based on the air quality mode switching chemical mechanism, characterized in that: Includes the following steps: Step S1: Preprocessing and quality control of online environmental VOCs observation data. The environmental VOCs species concentration data comes from the target receptor monitoring points, with a time resolution of 1 hour. After selecting the VOCs species concentration data for the study period, abnormal data values ​​are deleted. Step S2: Parameter setting and simulation debugging of the air quality model. After parameter setting, the model simulation effect is evaluated. The model simulation effect is evaluated by comparing the observed values ​​of meteorological and pollutant concentrations at the receptor observation point with the simulated values ​​of the model. The evaluation indicators include the correlation coefficient (R), normalized mean deviation (NMB), root mean square error (RMSE), and mean deviation (NB) between the simulation and measurement results. An air quality model is selected, coupled with the carbon bond reaction mechanism, to simulate the chemical process of VOCs reaction in the environment. The air quality model adopts the Lambert projection coordinate system and is set as a double-layer nested grid. The center of the nested simulation domain is located at the target receptor monitoring point. The pollution source input data of the grid includes emission inventory data of common source types. The WRF model is used to generate meteorological field and underlying surface data for the air quality model simulation, and each simulation starts 72 hours in advance. Step S3: Simulate the lumped concentration of VOCs under the scenario of activating the chemical mechanism, and perform scenario simulation (CSS) analysis to obtain the lumped concentration of VOCs at the target receptor monitoring site. lumped,CSS ); Step S4: Simulate the lumped concentration of VOCs under the scenario of chemical mechanism shutdown, perform scenario simulation (SS) analysis to obtain the lumped concentration of VOCs at the target receptor monitoring site (C lumped,DSS ); Step S5: Quantify the chemical loss rate (RLR) of a specific VOC species. Calculate the environmental concentrations of a specific VOC species at the target receptor monitoring site after chemical loss and before chemical loss, and obtain the chemical loss rate (RLR) of the specific VOC species based on the environmental concentrations after and before chemical loss. Step S6: Quantify the chemical depletion of environmental VOC species. Subtract the chemical depletion from the initial concentration to obtain the observed concentration of a specific VOC species. At the same time, the chemical depletion is equal to the product of the initial concentration and the chemical depletion rate (RLR) to obtain the chemical depletion of environmental VOC species.

2. The method for quantifying the chemical depletion of environmental VOCs according to claim 1, characterized in that: In step S5, the step of calculating the environmental concentrations of specific VOC species at the target receptor monitoring site after chemical reaction loss and without chemical reaction loss includes: using equation (1) to calculate the lumped species concentration (C) of the receptor monitoring site obtained in steps S3 and S4. lumped,CSS ) and (C lumped,DSS This is inversely converted to the concentration of a specific VOC species at that receptor site: Among them, C i,specific C represents the concentration of a specific VOC species i. j,lumped nij represents the concentration of ensemble VOC species j; nij represents the number of ensemble species j in a specific species i; Nj represents the total number of ensemble species j in all specific species.

3. The method for quantifying the chemical depletion of environmental VOCs according to claim 2, characterized in that: In step S5, the step of obtaining the chemical attrition rate (RLR) of a specific VOC species based on the environmental concentrations of the VOC species after chemical attrition and those without includes: First, from DSS(C) i,specific,DSS Subtract the specific species from the CSS (C) i,specific,CSS The concentration of ) was then determined by dividing the difference by C. i,specific,DSS The chemical loss rate (RLR) of a specific VOC species was obtained, and equation (2) was calculated:

4. The method for quantifying the chemical depletion of environmental VOCs according to claim 3, characterized in that: In step S6, the step of obtaining the chemical depletion of environmental VOC species includes: the chemical depletion of environmental VOC species is calculated by equation (3): Among them, RL i,obs RLR represents the observed chemical loss of VOC species i. i,specific C represents the chemical loss rate of a specific VOC species i. i,obs The observed concentration represents VOC species i.

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