Source item release inversion prediction method based on CALPUFF model

Abstract

The invention discloses a source item release inversion prediction method based on a CALPUFF model, and the method comprises the following steps: determining a pollution source and a sensitive point in a to-be-simulated region through the CALPUFF model, collecting the basic data of the to-be-simulated region, and carrying out the preprocessing of the basic data, and obtaining the meteorological data; collecting data information of a pollution source and data information of a sensitive point, inputting the data information of the pollution source and the data information of the sensitive point into a CALPUFF model together with meteorological data, and performing simulation by using the CALPUFF model to obtain simulation data; reprocessing the simulation data on the CALPOST module, and extracting simulation results of the odor concentration of the sensitive points and the concentration of the characteristic malodorous substances; obtaining a stink occurrence frequency value and an evaluation value of human health risk evaluation; and comparing the stink generation frequency value and the evaluation value of the human health risk evaluation with the evaluation and control indexes, and if the stink generation frequency value and the evaluation value accord with the evaluation and control indexes, determining the pollution concentration corresponding to the stink concentration and the characteristic stink substance concentration as the pollutant control concentration.

Description

Technical Field

[0001] The invention belongs to the technical field of environmental pollution inversion, and in particular relates to a source term release inversion prediction method based on a CALPUFF model. Background Art

[0002] Simulation of atmospheric pollutant diffusion is an important research direction in the field of environmental science and atmospheric science. It aims to simulate and predict the diffusion, transmission and deposition of pollutants in the atmosphere through mathematical models and calculation methods, and provide a scientific basis for environmental management and pollution control. With the rapid development of industrialization and urbanization, air pollution has posed a huge threat to human health and the ecological environment.

[0003] Therefore, developing an efficient and accurate atmospheric pollutant diffusion simulation model is of great significance for assessing pollution impacts and formulating emission reduction strategies. CALPUFF (California Puff Model) is an atmospheric pollutant diffusion model based on Lagrangian particle diffusion theory, jointly developed by the California Air Resources Board (CARB) and Sigma Research. The model can accurately predict the concentration distribution and impact range of pollutants in the atmosphere by simulating the emission, diffusion, and deposition of pollutants.

[0004] The CALPUFF model is mainly used to simulate the impact of pollutant emission points on sensitive points, that is, the impact of pollutant emissions at point A on point B (i.e., sensitive points). It is automated through some programming codes. The CALPUFF model is mainly used for forward diffusion simulation, and there is little research on inverse evaluation methods.

[0005] In response to the long-standing odor nuisance problem in some environmentally sensitive areas, it has been determined through source tracing that the pollution source is a specific organized emission outlet of the enterprise. In order to achieve precise control, it is necessary to scientifically define the threshold of pollutant emission concentration to ensure that the odor perception in sensitive areas is eliminated. In the current governance practice, simply taking extreme measures such as shutting down pollution sources to achieve zero emissions is beyond the technical and economic tolerance of enterprises and will also lead to systemic risks in production and operation.

[0006] The olfactory threshold is the minimum amount of a substance that causes olfactory stimulation to a person. It is divided into the sensory threshold and the identification threshold. The sensory threshold can be simply understood as the lowest concentration at which an odor can be smelled. It is obtained through experimental olfactory identification test analysis. However, the olfactory threshold data disclosed by different countries and teams are not comprehensive, and the data for the same odorous substance are also different. In addition, due to differences in temperature, mood, and even on-site environment, people's perception of odors also varies greatly, resulting in a large difference between the olfactory threshold of odorous substances and the actual complaint concentration of sensitive points. Therefore, relying solely on controlling the concentration of substances to below the olfactory threshold cannot completely solve the odor problem at sensitive points.

[0007] In addition, among the past consideration indicators, only the sensory evaluation of the odor at sensitive points seems to have been considered, and the evaluation has not been combined with the human health risk. Odorous substances not only cause discomfort to the human senses, but may also have a certain impact on human health. In view of this, on the basis of completing pollution source tracing, there are very few applications of further supporting the refined control of pollution sources using the CALPUFF model.

[0008] In the prior art, generally, it is necessary to build a database and use the database to quickly calculate the diffusion direction, range and severity of future pollution, assist decision-makers in issuing early warnings in a targeted manner, and formulate emergency response plans, which are mainly applied in radioactive leakage accidents; or use the CALPUFF model for forward simulation. Summary of the Invention

[0009] Aiming at the problems existing in the prior art, the present invention provides a source term release inversion prediction method based on the CALPUFF model, which performs inversion prediction through the CALPUFF model and combines human health risk assessment to form a malodor pollution control evaluation method, in order to achieve refined control of pollution emissions.

[0010] The technical solution of the present invention is realized as follows:

[0011] A source term release inversion prediction method based on the CALPUFF model, the CALPUFF model includes a CALMET module, a CALPUFF module and a CALPOST module; it includes the following steps:

[0012] S1. Determine pollution sources and sensitive points in the area to be simulated through the CALPUFF model, collect basic data of the area to be simulated, and preprocess the basic data through the CALMET module to obtain meteorological data;

[0013] S2. Collect data information of pollution sources and data information of sensitive points, and input the pollution source data information, sensitive point data information and meteorological data into the CALPUFF model, and use the CALPUFF module to perform simulation and obtain simulation data;

[0014] S3. Reprocess the simulation data on the CALPOST module, and extract the simulation results of the odor concentration and the concentration of characteristic malodorous substances at the sensitive points;

[0015] S4. Obtain the odor occurrence frequency value according to the odor concentration, evaluate the human health risk according to the concentration of characteristic malodorous substances, and obtain the evaluation value of the human health risk assessment;

[0016] S5. Compare the odor occurrence frequency value and the evaluation value of the human health risk assessment with their corresponding evaluation and control indicators. If they meet the evaluation and control indicators, the odor concentration and the pollution concentration corresponding to the characteristic odor substances are the pollutant control concentrations; if they do not meet the evaluation and control indicators, adjust the information and data input into the CALPUFF model and conduct the simulation again until they meet the evaluation and control indicators, and the corresponding pollution concentration obtained is the pollutant control concentration.

[0017] Further, in the S1, the basic data includes topographic data, land use data, surface meteorological data, and mesoscale meteorological data. Input the basic data into the CALMET module for preprocessing, and the output is meteorological data.

[0018] Further, in the S2, the data information of the pollution sources includes the x-axis coordinate of the pollution source, the y-axis coordinate of the pollution source, the altitude of the pollution source, the height from the ground of the pollution source, the outlet diameter, the outlet flow velocity, the outlet flue gas temperature, the types of pollutants, the odor concentration, and the concentration of characteristic odor substances;

[0019] The sensitive point information includes the x-axis coordinate of the sensitive point, the y-axis coordinate of the sensitive point, the height from the ground of the sensitive point, and the altitude of the sensitive point.

[0020] Further, in the S3, the odor concentration of the pollution source to the sensitive point includes the hourly pollution concentration, and the concentration of the characteristic odor substances of the pollution source to the sensitive point includes the long-term average pollution concentration.

[0021] Further, in the S4, to obtain the odor occurrence frequency value, specifically, it includes calculating the odor occurrence frequency according to the odor concentration and obtaining the odor peak concentration by using the pollution diffusion model, that is:

[0022] C p =C m ×F;

[0023] Wherein, C p represents the odor peak concentration. Different atmospheric stability levels are obtained through the air quality model. C m is the odor concentration under different atmospheric stability levels, and F is the peak mean factor under different atmospheric stability levels. The peak mean factor is the conversion factor for converting the mean concentration into the instantaneous concentration.

[0024] Further, if the odor peak concentration is greater than 1 OU / m 3 , the corresponding time is the time when the odor occurs. Count the number of hours when the odor peak concentration is greater than 1 OU / m 3 within the annual cycle, and calculate the odor occurrence frequency value. The calculation method is:

[0025] Odor occurrence frequency value = the number of hours when the odor peak concentration is greater than 1 OU / m 3 within the annual cycle / 8760;

[0026] Obtain the frequency value of malodor occurrence.

[0027] Furthermore, in step S4, the human health risk is evaluated, including carcinogenic risk assessment and non-carcinogenic risk assessment using the concentration of characteristic malodorous substances, and the carcinogenic risk assessment value and non-carcinogenic risk assessment value are obtained.

[0028] Furthermore, the carcinogenic risk assessment using the concentration of characteristic malodorous substances specifically includes: According to the long-term average pollution concentration C i , calculate the exposure dose CDI of the carcinogenic pollutant, and the calculation method is:

[0029] CDI = (C i ×IR×ET×EF×ED) / (365×BW×AT);

[0030] Wherein, IR represents the adult breathing rate (m 3 ·h -1 ), ET represents the daily exposure time (h·d -1 ), EF represents the exposure frequency (d·a -1 ), ED represents the exposure duration (a), BW represents the adult body mass (kg), and AT represents the average exposure time (a);

[0031] Exposure dose (Chronic Daily Intak, abbreviated as CDI);

[0032] Through the exposure dose CDI of the carcinogenic pollutant, obtain the lifetime carcinogenic risk LCR, and the calculation method is:

[0033] LCR = CDI×SF;

[0034] Wherein, SF represents the carcinogenic slope factor of the pollutant; Using the lifetime carcinogenic risk LCR as a measure of the carcinogenic risk assessment, the obtained lifetime carcinogenic risk LCR is the carcinogenic risk assessment value.

[0035] Furthermore, the non-carcinogenic risk assessment using characteristic malodorous substances specifically includes: Through the exposure dose CDI of the carcinogenic pollutant, calculate the hazard index HI, and the calculation method is:

[0036] HI = CDI / RfD;

[0037] Wherein, RfD represents the non-carcinogenic reference dose of the pollutant; Using the hazard index HI as a measure of the non-carcinogenic risk assessment, the obtained hazard index HI is the non-carcinogenic risk assessment value.

[0038] Further, in S5, the frequency value of malodor occurrence and the evaluation value of human health risk assessment are compared with the corresponding evaluation and control indicators. Specifically, the method is to compare the frequency value of malodor occurrence, the carcinogenic risk assessment value, and the non-carcinogenic risk assessment value with the threshold values of the evaluation and control indicators respectively:

[0039] If the frequency value of malodor occurrence, the carcinogenic risk assessment value, and the non-carcinogenic risk assessment value are all lower than the threshold values of the evaluation and control indicators, then the odor concentration and the pollution concentration corresponding to the characteristic malodor substances are the pollutant control concentrations;

[0040] If the frequency value of malodor occurrence, the carcinogenic risk assessment value, and the non-carcinogenic risk assessment value cannot all be lower than the threshold values of the evaluation and control indicators, then reduce the input data value, and execute S2 again. Use the CALPUFF model to re-simulate until it meets the evaluation and control indicators simultaneously, and obtain the corresponding pollutant control concentrations.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The present invention provides a source term release inversion prediction method based on the CALPUFF model. By collecting data of pollution sources, using the CALPUFF model for processing and simulation, extracting the odor concentration and data of characteristic malodor substances of the pollution sources on sensitive points, the malodor concentration is used for calculating the frequency of malodor occurrence to obtain the odor sensory evaluation value; the concentration of characteristic pollutants is used for calculating human health risk assessment. Compare the odor sensory evaluation value and the human health risk assessment result with the evaluation and control indicators to obtain the control concentrations of the odor concentration and characteristic malodor substances; form a malodor pollution control evaluation method to achieve refined management and control of pollution emissions; a method for determining the combination scheme of "parameters such as characteristic malodor substances and odor concentration" that requires the frequency value of malodor occurrence and human health risk on sensitive points to be lower than the established management and control objectives simultaneously through model simulation. Description of the Drawings

[0043] Figure 1 is a working diagram of a source term release inversion prediction method based on the CALPUFF model provided by an embodiment of the present invention;

[0044] Figure 2 is a schematic diagram of pollution sources and sensitive points in a source term release inversion prediction method based on the CALPUFF model provided by an embodiment of the present invention;

[0045] Figure 3 is the CALPUFF interface of a source term release inversion prediction method based on the CALPUFF model provided by an embodiment of the present invention. Detailed Embodiments

[0046] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0047] Embodiment

[0048] For example Figures 1 to 3 , a source term release inversion prediction method based on the CALPUFF model, where the CALPUFF model includes a CALMET module, a CALPUFF module, and a CALPOST module; the method includes the following steps:

[0049] S1. Determine pollution sources and sensitive points in the area to be simulated through the CALPUFF model, collect the basic data of the area to be simulated, and preprocess the basic data through the CALMET module to obtain meteorological data;

[0050] The basic data includes terrain data, land use data, surface meteorological data, and mesoscale meteorological data. The basic data is input into the CALMET module for preprocessing, and the output is meteorological data.

[0051] The CALPUFF model is an air pollution dispersion model. Simply understood, A represents the pollution source and B represents the sensitive point. For example Figure 2 , the CALPUFF model is used to simulate the impact of pollutant emissions at point A on point B. The existing sensitive point has been troubled by peculiar smells all year round. Through the previous work of peculiar smell source tracing, it has been determined that the pollution source A is the specific discharge port of Organization A-1 of Enterprise A.

[0052] Perform data preprocessing on the CALMET module: input terrain data terrel.DAT, land use data lu.DAT, surface meteorological data suf.DAT, and mesoscale meteorological data 3D.DAT, and output meteorological data CALMET.DAT.

[0053] S2. Collect the data information of the pollution source and the data information of the sensitive point, and input the pollution source data information, the sensitive point data information, and the meteorological data into the CALPUFF model, and use the CALPUFF module to perform simulation and obtain simulation data;

[0054] The data information of the pollution source includes the x-axis coordinate of the pollution source, the y-axis coordinate of the pollution source, the altitude of the pollution source, the height of the pollution source from the ground, the outlet diameter, the outlet flow rate, the outlet flue gas temperature, the types of pollutants, the odor concentration, and the emission concentration of characteristic odorous substances;

[0055] The sensitive point information includes the x-axis coordinate of the sensitive point, the y-axis coordinate of the sensitive point, the height of the sensitive point from the ground, and the altitude of the sensitive point.

[0056] Perform simulations on the CALPUFF module and input the pollution source information: x-axis coordinate (km), Y-axis coordinate (km), altitude (m), height from the ground (m), outlet diameter (m), outlet flow velocity (m / s), outlet flue gas temperature (K), select the required pollutant types from the pollutant types, odor concentration (odor), and emission concentration of characteristic odor substances (kg / Hr);

[0057] Input the sensitive point information: x-axis coordinate (km), Y-axis coordinate (km), height from the ground (m), altitude (m). Different altitudes can be set for the altitude to explore the impact of pollution sources on sensitive points at different heights. Input the meteorological data CALMET.DAT; output CALPUFF.DAT

[0058] S3. Reprocess the simulation data on the CALPOST module and extract the simulation results of the odor concentration and the concentration of characteristic odor substances at the sensitive points;

[0059] The odor concentration of the pollution source at the sensitive point includes the hourly pollution concentration Hi (U / m 3 ), and the concentration of characteristic odor substances of the pollution source at the sensitive point includes the long-term average pollution concentration Ci (μg / m 3 ).

[0060] S4. Calculate the odor occurrence frequency based on the odor concentration to obtain the odor occurrence frequency value, which can also be called the odor perception evaluation value; evaluate the human health risk based on the concentration of characteristic odor substances to obtain the evaluation value of the human health risk assessment;

[0061] Obtain the odor occurrence frequency value according to the odor concentration, specifically including calculating the odor occurrence frequency based on the odor concentration and using the odor peak concentration model to obtain the odor peak concentration, that is:

[0062] C p =C m ×F;

[0063] Among them, C p represents the odor peak concentration. Different atmospheric stability levels are obtained through the air quality model. C m is the odor concentration under different atmospheric stability levels, and F is the peak mean factor under different atmospheric stability levels. The peak mean factor is the conversion factor for converting the mean concentration to the instantaneous concentration. For example, the conversion factor for converting the mean concentration of 0.5h and 1h to the short-time peak concentration of 1s and 5s.

[0064] The peak-to-mean factor can convert the mean concentration into the instantaneous concentration, which can meet the requirements of the instantaneous maximum value for odor supervision and improve the accuracy of odor pollution simulation.

[0065] The peak-to-mean factors under different atmospheric stability levels can be obtained by long-term monitoring of the 1s instantaneous odor concentration and the synchronous meteorological field using an online electronic nose.

[0066] It is set that the instantaneous concentration of odor generation should not exceed 1 OU / m 3 .

[0067] The odor occurrence frequency is the frequency exceeding the olfactory perception threshold of humans. The olfactory perception threshold of odor concentration by humans is 1 OU / m3. Therefore, if the odor peak concentration is greater than 1 OU / m 3 , then the corresponding time is the time of odor occurrence. Count the number of hours in the annual cycle when the odor peak concentration is greater than 1 OU / m 3 , and calculate the odor occurrence frequency value. The calculation method is:

[0068] Odor occurrence frequency value = the number of hours in the annual cycle when the odor peak concentration is greater than 1 OU / m 3 / 8760;

[0069] Obtain the numerical value of the odor occurrence frequency for odor sensory evaluation. The odor sensory evaluation and control indicators are determined according to specific circumstances. It can be referred that the odor occurrence frequency in residential areas and school areas is less than 6%; the odor occurrence frequency in industrial parks is less than 9%.

[0070] Evaluate the human health risks, including carcinogenic risk assessment and non-carcinogenic risk assessment using the concentrations of characteristic odor substances, and obtain the carcinogenic risk assessment value and non-carcinogenic risk assessment value.

[0071] Use the concentrations of characteristic odor substances for carcinogenic risk assessment, specifically including: According to the long-term average pollution concentration C i , calculate the exposure dose CDI (mg·kg -1 ·d -1 ) of the carcinogenic pollutant. The calculation method is:

[0072] CDI = (C i × IR × ET × EF × ED) / (365 × BW × AT);

[0073] Among them, IR represents the adult breathing rate (m 3 ·h -1 ), ET represents the daily exposure time (h·d -1 ), EF represents the exposure frequency (d·a -1 ), ED represents the exposure duration (a), BW represents the adult body mass (kg), and AT represents the average exposure time (a);

[0074] Chronic Daily Intake (CDI);

[0075] The lifetime carcinogenic risk (LCR) is obtained from the CDI of carcinogenic pollutants, and the calculation method is as follows:

[0076] LCR = CDI × SF;

[0077] where SF represents the carcinogenic slope factor of the pollutant (kg·d·mg -1 ); The lifetime carcinogenic risk LCR is used as a measure of carcinogenic risk assessment, and the obtained lifetime carcinogenic risk LCR is the carcinogenic risk assessment value.

[0078] For multiple characteristic odor pollutants, it is necessary to superimpose the risk assessment values of multiple substances.

[0079] Non-carcinogenic risk assessment using characteristic odor substances specifically includes: calculating the hazard index (HI) through the CDI of carcinogenic pollutants, and the calculation method is as follows:

[0080] HI = CDI / RfD;

[0081] where RfD represents the non-carcinogenic reference dose of the pollutant (mg·kg -1 ·d -1 ); The hazard index HI is used as a measure of non-carcinogenic risk assessment, and the obtained hazard index HI is the non-carcinogenic risk assessment value.

[0082] S5. Compare the odor occurrence frequency value and the evaluation value of human health risk assessment with the corresponding evaluation and control indicators. The specific method is to compare the odor occurrence frequency value, carcinogenic risk assessment value, and non-carcinogenic risk assessment value with the threshold values of the evaluation and control indicators respectively:

[0083] If the odor occurrence frequency value, carcinogenic risk assessment value, and non-carcinogenic risk assessment value are all lower than the threshold values of the evaluation and control indicators, then the odor concentration and the pollution concentration corresponding to the characteristic odor substances are the pollutant control concentrations;

[0084] If the odor occurrence frequency value, carcinogenic risk assessment value, and non-carcinogenic risk assessment value cannot all be lower than the threshold values of the evaluation and control indicators, then reduce the input data value and execute S2 again, and re-simulate using the CALPUFF model until it meets the evaluation and control indicators simultaneously to obtain the corresponding pollutant control concentration.

[0085] Assessment of carcinogenic risk: When the lifetime carcinogenic risk LCR is greater than 1.0×10 -4 , it is considered that there is a definite carcinogenic risk; when LCR is 1.0×10 -4 ~1.0×10 -5, is considered to possibly have a relatively high carcinogenic risk; LCR is 1.0×10 -5 ~1.0×10 -6 , is considered to possibly have a carcinogenic risk; when LCR is less than 1.0×10 -6 , the carcinogenic risk is considered to be very small.

[0086] Assessment of non-carcinogenic risk: When the hazard index HI is greater than 1, it is considered to have a non-carcinogenic risk; when HI is less than 1, the non-carcinogenic risk is considered to be very small; and when HI is greater than 0.1, it is considered to possibly have a potential non-carcinogenic risk.

[0087] The simulation results need to simultaneously meet three conditions, that is, the frequency of odor occurrence < 6%, LCR < 1.0×10 -6 , HI < 1. At this time, it is both the maximum emission concentration of the pollution source and also known as the pollutant control concentration.

[0088] If there are multiple emission outlets, each emission outlet corresponds to a set of odor concentrations and characteristic odor substances, and it is necessary to obtain the contribution rate of each emission outlet to the pollution source. The present invention provides a reference method for obtaining the contribution rate, which can obtain the contribution rate based on the concentration normalization of characteristic pollutants; or first simulate the diffusion of characteristic pollutants of a single emission outlet and then normalize to obtain the contribution rate.

[0089] A source term release inversion prediction method based on the CALPUFF model provided by the present invention is not limited to the CALPUFF model, and other atmospheric diffusion models such as CFD, Aermod, etc. are equally applicable.

[0090] According to the disclosure and teachings of the above specification, those skilled in the art to which the present invention pertains can also make changes and modifications to the above embodiments. Therefore, the present invention is not limited to the specific embodiments disclosed and described above, and some modifications and changes to the present invention should also fall within the protection scope of the claims of the present invention. In addition, although some specific terms are used in this specification, these terms are only for convenience of description and do not constitute any limitation to the present invention.

Claims

1. A source term release inversion prediction method based on the CALPUFF model, characterized in that The CALPUFF model includes the CALMET module, the CALPUFF module, and the CALPOST module; it includes the following steps: S1. Determine the pollution sources and sensitive points within the area to be simulated through the CALPUFF model, collect the basic data of the area to be simulated, and preprocess the basic data through the CALMET module to obtain meteorological data; S2. Collect the data information of the pollution sources and the data information of the sensitive points, and input the pollution source data information, the sensitive point data information, and the meteorological data into the CALPUFF model, and use the CALPUFF module to perform simulations and obtain simulation data; S3. Reprocess the simulation data on the CALPOST module to extract the simulation results of the odor concentration and the concentration of characteristic odor substances at the sensitive points; S4. Obtain the odor occurrence frequency value based on the odor concentration, and evaluate the human health risk based on the concentration of characteristic odor substances to obtain the evaluation value of the human health risk assessment; S5. Compare the odor occurrence frequency value and the evaluation value of the human health risk assessment with their corresponding evaluation and control indicators. If they meet the evaluation and control indicators, the odor concentration and the pollution concentration corresponding to the characteristic odor substances are the pollutant control concentrations; if they do not meet the evaluation and control indicators, adjust the information and data input into the CALPUFF model and perform simulations again until they meet the evaluation and control indicators, and the corresponding pollution concentration obtained is the pollutant control concentration.

2. The source term release inversion prediction method based on the CALPUFF model according to claim 1, wherein In S1, the basic data includes topographic data, land use data, surface meteorological data, and mesoscale meteorological data. Input the basic data into the CALMET module for preprocessing, and the output is meteorological data.

3. The source term release inversion prediction method based on the CALPUFF model according to claim 1, characterized in that In S2, the data information of the pollution sources includes the x-axis coordinate of the pollution source, the y-axis coordinate of the pollution source, the altitude of the pollution source, the height above the ground of the pollution source, the outlet diameter, the outlet flow rate, the outlet flue gas temperature, the types of pollutants, the odor concentration, and the concentration of characteristic odor substances; The sensitive point information includes the x-axis coordinate of the sensitive point, the y-axis coordinate of the sensitive point, the height above the ground of the sensitive point, and the altitude of the sensitive point.

4. A source term release inversion prediction method based on the CALPUFF model according to claim 1, characterized in that In S3, the odor concentration of the pollution source at the sensitive point includes the hourly pollution concentration, and the concentration of the characteristic odor substance of the pollution source at the sensitive point includes the long-term average pollution concentration.

5. A source term release inversion prediction method based on the CALPUFF model according to claim 4, characterized in that, In S4, to obtain the odor occurrence frequency value, specifically, it includes calculating the odor occurrence frequency based on the odor concentration and using the pollution diffusion model to obtain the peak odor concentration, that is: C p = C m × F; Among them, C p represents the peak concentration of odor. Different atmospheric stability levels are obtained through the air quality model. C m is the odor concentration under different atmospheric stability levels, and F is the peak mean factor under different atmospheric stability levels. The peak mean factor is a conversion factor for converting the mean concentration into the instantaneous concentration.

6. The source term release inversion prediction method based on the CALPUFF model according to claim 5, wherein If the peak concentration of odor is greater than 1 OU / m 3 , then the corresponding time is the time when the odor occurs. Count the number of hours when the peak concentration of odor is greater than 1 OU / m 3 within the annual cycle, and calculate the odor occurrence frequency value. The calculation method is as follows: Frequency value of malodor occurrence = Number of hours when the peak concentration of malodor in a year cycle is greater than 1 OU / m 3 / 8760; Obtain the odor occurrence frequency value.

7. A source term release inversion prediction method based on the CALPUFF model according to claim 4, characterized in that In S4, to evaluate the human health risk, it includes using the concentration of characteristic odor substances to conduct carcinogenic risk assessment and non-carcinogenic risk assessment to obtain the carcinogenic risk assessment value and the non-carcinogenic risk assessment value.

8. The source term release inversion prediction method based on the CALPUFF model according to claim 7, characterized in that Conduct carcinogenic risk assessment using the concentration of characteristic odoriferous substances, specifically including: Based on the long-term average pollution concentration C i , calculate the exposure dose CDI of carcinogenic pollutants, and the calculation method is as follows: CDI=(C i ×IR×ET×EF×ED) / (365×BW×AT); Among them, IR represents the adult breathing rate, ET represents the daily exposure time, EF represents the exposure frequency, ED represents the exposure duration, BW represents the adult body mass, and AT represents the average exposure time; Through the exposure dose CDI of the carcinogenic pollutant, obtain the lifetime carcinogenic risk LCR, and the calculation method is: LCR = CDI × SF; Among them, SF represents the carcinogenic slope factor of pollutants; the lifetime carcinogenic risk LCR is used as a measure of carcinogenic risk assessment, and the obtained lifetime carcinogenic risk LCR is the carcinogenic risk assessment value.

9. The source term release inversion prediction method based on the CALPUFF model according to claim 8, characterized in that Non-carcinogenic risk assessment is carried out using characteristic odor substances, specifically including: calculating the hazard index HI through the exposure dose CDI of carcinogenic pollutants, and the calculation method is: HI = CDI / RfD; Among them, RfD represents the non-carcinogenic reference dose of pollutants; the hazard index HI is used as a measure of non-carcinogenic risk assessment, and the obtained hazard index HI is the non-carcinogenic risk assessment value.

10. The source term release inversion prediction method based on the CALPUFF model according to claim 9, characterized in that In the step S5, the odor occurrence frequency value and the evaluation value of human health risk assessment are compared with the corresponding evaluation and control indicators. Specifically, the odor occurrence frequency value, the carcinogenic risk assessment value, and the non-carcinogenic risk assessment value are respectively compared with the threshold values of the evaluation and control indicators: If the odor occurrence frequency value, the carcinogenic risk assessment value, and the non-carcinogenic risk assessment value are all lower than the threshold values of the evaluation and control indicators, then the odor concentration and the pollution concentration corresponding to the characteristic odor substances are the pollutant control concentrations; If the odor occurrence frequency value, the carcinogenic risk assessment value, and the non-carcinogenic risk assessment value cannot all be lower than the threshold values of the evaluation and control indicators, then reduce the input data value, and execute S2 again, and re-simulate using the CALPUFF model until it meets the evaluation and control indicators simultaneously, and obtain the corresponding pollutant control concentration.

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