Coking enterprise peripheral soil benzopyrene monitoring distribution optimization method based on CALPUFF model

Abstract

The invention discloses a coking enterprise peripheral soil benzopyrene monitoring distribution optimization method based on a CALPUFF model, and belongs to the technical field of environmental monitoring, and the method comprises the steps: obtaining annual discharge amount and pollution source parameters of benzopyrene, inputting the CALPUFF model, and integrating meteorological, terrain and land coverage data to construct a three-dimensional meteorological element field, the atmospheric diffusion and dry-wet sedimentation process of benzopyrene is simulated to determine the pollution range; monitoring points are arranged in high-value areas around an enterprise and a factory boundary based on settlement flux distribution, the downstream of a prevailing wind direction and a soil background value area, and the benzopyrene content in soil is detected by referring to the national standard. The monitoring efficiency and the data reliability are improved by accurately simulating the pollution transmission process and the differentiated point distribution strategy, and the method is suitable for monitoring the benzopyrene pollution of the soil around the coking enterprise.

Description

Technical Field

[0001] The present invention belongs to the technical field of environmental monitoring, and in particular relates to a method for optimizing soil benzopyrene monitoring points around a coking enterprise based on a CALPUFF model. Background Art

[0002] Polycyclic aromatic hydrocarbons (PAHs) have become a global focus of environmental health concern due to their potent carcinogenicity and persistence. Benzo[a]pyrene, one of the most toxic PAHs, has been classified as a Group 1 carcinogen by the International Agency for Research on Cancer (IARC). Coking plants are a major anthropogenic source of benzo[a]pyrene. The flue gas, tar residue, and fugitive emissions generated by the high-temperature dry distillation of coal during production contain high concentrations of benzo[a]pyrene, which is easily accumulated in soil through atmospheric deposition and other pathways. With the centralization and scale-up of coking capacity in my country, benzo[a]pyrene contamination of soil around coking plants has become increasingly prominent, necessitating the use of scientific and efficient monitoring methods to accurately assess contamination risks. Currently, the main methods for monitoring soil around coking plants include grid, zone, and random sampling. However, grid sampling can lead to inefficient sampling, zone sampling can distort data due to bias, and random sampling can miss core areas of contamination. The traditional monitoring method lacks dynamic tracking capabilities and is difficult to accurately trace the source. The distribution of benzopyrene pollution around coking enterprises is easily affected by complex factors such as atmospheric deposition, land use type, wind direction, and terrain. Therefore, conducting research on optimizing the monitoring points for soil benzopyrene around coking enterprises is of great significance for accurately identifying the pollution status of soil benzopyrene around coking enterprises. Summary of the Invention

[0003] In response to the above-mentioned pain points, the present invention provides a method for optimizing the distribution of benzopyrene monitoring points in the soil around coking enterprises based on the CALPUFF model. By simulating the atmospheric transmission and deposition process of pollutants, the layout of monitoring points is scientifically optimized to achieve efficient and accurate monitoring of the benzopyrene content in the soil.

[0004] The scheme of the present invention is as follows: A method for optimizing soil benzopyrene monitoring points around a coking enterprise based on a CALPUFF model, characterized by comprising the following steps: S1, obtaining the annual benzopyrene emissions and pollution source parameters of the coking enterprise, wherein the pollution source parameters include the spatial coordinates of the pollution source, the emission height, the flue gas thermodynamic parameters, and the benzopyrene emission intensity; S2. Input the annual emissions of benzo(a)pyrene and pollution source parameters mentioned above into the CALPUFF atmospheric diffusion simulation system, integrate near-surface meteorological observation data, the three-dimensional atmospheric dynamic field output by the mesoscale WRF numerical model, the 90-meter resolution digital elevation model (DEM), and the 30-meter accuracy global land cover data (GLCD), and construct a high-resolution three-dimensional meteorological element field through terrain disturbance correction, slope wind field reconstruction, and mass conservation constraints. Simulate the diffusion, transmission, dry and wet deposition process of benzo(a)pyrene in the atmosphere and determine the spatial distribution range of the contaminated sites; S3. Based on the simulated spatial distribution difference characteristics of benzo(a)pyrene dry and wet deposition fluxes, monitoring points were set up around coking enterprises and in soil background value areas, and the benzo(a)pyrene content in the soil was detected in accordance with relevant standards.

[0005] Preferably, the annual emission of benzopyrene is calculated by the formula Calculate, where is the annual emission, kg / a; is the annual output of the product, t / a; is the emission factor, kg / t.

[0006] Preferably, the CALPUFF atmospheric diffusion simulation system adopts a three-dimensional Lagrangian puff tracking algorithm, which is composed of a CALMET meteorological field module, a CALPUFF diffusion calculation module and supporting auxiliary programs; the near-surface meteorological observation data include wind speed, wind direction, humidity, precipitation, temperature and air pressure data on a daily or hourly scale; the three-dimensional atmospheric dynamic field output by the mesoscale WRF numerical model is generated based on the following parameters: the projection method adopts Lambert projection, and the standard latitudes are N24° and N46°; the boundary conditions are NCEPds083.21° reanalysis data; the microphysical process adopts WSM3-classsimpleicescheme, the long-wave radiation scheme adopts the RRTM scheme, and the short-wave radiation scheme adopts the Dudhia scheme; the boundary layer physics scheme adopts the YSU scheme, and the cumulus parameter scheme adopts the shallow convection Kain-Fritsch (newEta) scheme.

[0007] Preferably, the control file parameters of the CALPUFF diffusion calculation module include: grid resolution, 100m×100m, matching the spatial scale of the 30-meter precision Global Land Cover Data (GLCD); pollutant type, PM 10 , as the transmission carrier of benzopyrene; the reference time zone is East 8, which is Beijing time; the operating time is consistent with the annual emission statistical period of benzopyrene; the meteorological data format is CALMET binary file.

[0008] Preferably, the dry and wet deposition parameter settings of the CALPUFF diffusion calculation module include: particle geometric mass mean diameter, 0.48 μm; geometric standard deviation, 2.00 μm; liquid precipitation clearance coefficient, 0.0004s -1 ; Freezing precipitation removal coefficient, 0.00005s -1 The simulation process does not take into account the decay and chemical transformation of benzopyrene. The long-term enrichment of benzopyrene in the soil environment around the project (ng / m 2 ), and analyze the spatial distribution of contaminated sites.

[0009] Preferably, in the CALPUFF diffusion calculation module, the ground concentration of pollutants is calculated using the following formula:

[0010]

[0011] Where, is the ground concentration, g / m 2 ; For the source of strength; 、 、 is the diffusion coefficient; d a Downwind distance refers to the horizontal distance from the center of the pollution source to the monitoring point along the dominant wind direction; d c The horizontal distance refers to the horizontal distance from the center of the pollution source to the monitoring point perpendicular to the dominant wind direction; He is the effective height; h is the height of the mixing layer; g is the vertical term of the Gaussian equation, which solves the problem of multiple reflections between the mixing layer and the ground.

[0012] Preferably, the 90-meter resolution digital elevation model (DEM) is derived from the United States Geological Survey (USGS), and the 30-meter precision global land cover data (GLCD) is derived from a 30-meter precision public database.

[0013] Preferably, the layout of the monitoring points includes: concentrated and dense layout around the factory boundary for high-value deposition flux areas caused by unorganized emissions; extending the layout along the main direction of benzopyrene deposition such as downstream of the dominant wind; and setting soil background value control points in areas far away from the factory and with low-value benzopyrene deposition flux.

[0014] Preferably, the layout of the monitoring points also needs to couple topographic data, land use data and meteorological data to comprehensively analyze the spatial differentiation characteristics of benzopyrene dry and wet deposition fluxes, so as to more accurately determine the density and location of high-value aggregation areas, main transmission directions and background value areas.

[0015] Preferably, the detection of benzopyrene content in the soil includes: the sampling frequency is determined according to the Technical Guidelines for Soil and Groundwater Monitoring Around Industrial Enterprises (Trial); the detection method adopts the high performance liquid chromatography method for the determination of polycyclic aromatic hydrocarbons in soil and sediments HJ 784-2016, specifically: the soil sample is subjected to Soxhlet extraction and silica gel column purification, and then analyzed using a high performance liquid chromatograph equipped with a fluorescence detector.

[0016] Compared with the prior art, the advantages of the present invention are: (1) Multi-source data integration and precise simulation: This method integrates the benzopyrene emission data of coking enterprises (e.g., pollution permit monitoring, environmental impact assessment reports), near-surface meteorological observation data, WRF three-dimensional atmospheric dynamic field, DEM terrain data, and GLCD land cover data to construct a high-precision three-dimensional meteorological element field. The CALPUFF model is used to simulate the diffusion, transmission, and dry and wet deposition processes of benzopyrene in the atmosphere, accurately determining the spatial distribution range of contaminated sites. This solves the problem that traditional methods rely on empirical judgment and lack simulation accuracy. (2) Considering long-term enrichment and deposition characteristics: The decay and chemical transformation of benzopyrene are not considered in the simulation process. The multi-year enrichment of benzopyrene in the soil is calculated based on the project commissioning and shutdown time. At the same time, the dry and wet deposition parameters are set in combination with the physical properties of particulate matter (such as particle size and clearance coefficient) to truly reflect the long-term accumulation effect of pollutants in the soil and provide a scientific basis for assessing long-term pollution risks. (3) Differentiated point distribution strategy optimizes monitoring efficiency: Based on the spatial distribution differences of dry and wet deposition fluxes obtained by simulation, the points are concentrated and densely distributed in the high-value areas of unorganized emissions around the factory boundary, and the points are extended along the main deposition directions such as the downstream of the dominant wind. Background value control points are set in the low-value soil areas far away from the factory area, forming a three-dimensional point distribution pattern of "high-value area density-diffusion path extension-background value control", which significantly improves the pertinence and effectiveness of monitoring points and avoids waste of resources or monitoring blind spots caused by blind distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a schematic diagram of soil monitoring points for an optimization method of soil benzopyrene monitoring points around a coking enterprise based on the CALPUFF model; Figure 2 This is a technical roadmap for optimizing the location of soil benzopyrene monitoring points around coking enterprises based on the CALPUFF model. DETAILED DESCRIPTION

[0018] The technical solutions of the embodiments of the present invention are explained and described below, but the following embodiments are only preferred embodiments of the present invention and are not exhaustive. Based on the embodiments in the implementation manner, other embodiments obtained by those skilled in the art without creative work are all within the scope of protection of the present invention.

[0019] This example takes a specific coking enterprise as the object and elaborates on the actual application process of the soil benzopyrene monitoring point optimization method based on the CALPUFF model. By clarifying the basic data of the enterprise, model parameter settings, simulation process and point distribution strategy, it reflects the operability and practicality of the invention and provides a specific reference example for the monitoring point distribution of similar enterprises.

[0020] Example: Scenario: A coking plant with an annual coke production capacity of 3 million tons has been in operation for 10 years. The plant includes two 60-hole coke ovens, equipped with supporting coal loading, coke pushing, coke quenching, and gas purification processes. Benzopyrene emissions primarily come from unorganized escape from the coke ovens. The target area is a 5km radius around the plant, with primarily plain terrain and a river 1.5km east of the plant. The prevailing wind direction is northwest-southeast, with an average annual wind speed of 2.8m / s.

[0021] S1. Obtain annual benzopyrene emissions and pollution source parameters.

[0022] 1. Emissions Inventory Preparation: Collect the company's 2024 Pollutant Discharge Permit monitoring data, environmental impact assessment report, and acceptance report to determine the emission factors for each process: Coal loading process: (coke); Pushing process: (coke); Coke quenching process: (coke); Annual output of products , according to the formula Calculate the annual emissions of each process:

[0023] 2. Pollution source parameters Spatial coordinates of coke oven pollution sources: with the center of the plant as the origin (E116.5°, N37.5°), the coordinates of the coal loading port (50 meters east, 30 meters south), the coke pushing port (80 meters east, 20 meters north), and the coke quenching tower (100 meters west, 50 meters south); Emission height: physical height of coke oven chimney 30m, flue gas lifting height 15m, effective height ; Flue gas thermodynamic parameters: temperature 150°C, flow rate 20m / s; Emission intensity: total source intensity That is 0.3mg / s.

[0024] S2. CALPUFF model construction and diffusion simulation 1. Basic data integration: 1-1. Meteorological data: Ground data: Hourly observation data from 2015 to 2024, including wind speed, wind direction, humidity, precipitation, temperature, and air pressure. The data comes from the factory's own meteorological station and stations in neighboring countries; 1-2. Three-dimensional atmospheric dynamic field: generated by WRF model, parameter settings strictly follow Table 1: Table 1 WRF simulation parameters

[0025] 1-3. Geospatial Data: DEM: A 90-meter resolution digital elevation model (DEM) provided by the USGS was used, with a regional average elevation of 50 meters and a maximum elevation difference of 20 meters. This data was generated using InSAR technology, with a vertical accuracy error of ≤±10 meters. Its spatial resolution is compatible with the 100m×100m grid scale of the CALPUFF model, with a difference of 1-3 times, meeting the requirements for mesoscale terrain simulation in the "Technical Guidelines for Environmental Impact Assessment - Atmospheric Environment" (HJ2.2-2018). Using a terrain-following coordinate algorithm and six-point interpolation, it accurately captures regional microtopography, such as a maximum elevation difference of 20 meters, avoiding terrain flattening caused by lower resolutions (e.g., 250 meters). This ensures the accuracy of the CALMET module's slope wind field reconstruction and mass conservation constraints. GLCD: 30-meter precision global land cover data (GLCD) was used. The area surrounding the plant area accounts for 40% cultivated land, 25% forest land, 30% construction land, and 5% water. The data comes from public databases such as GlobeLand30, with a classification accuracy of >85%. The 30-meter precision can effectively distinguish different surface types, such as cultivated land, forest land, and construction land. Its spatial scale is resampled to a 100-meter grid and then matched with the model to avoid distortion of parameters such as surface roughness and albedo caused by mixed pixels. The CALPUFF model uses this data to calculate surface energy flux through the NOAH land surface model. The 30-meter precision ensures the authenticity of land surface process simulation. Similar studies have shown that the consistency between this resolution data and monitoring results is over 85%.

[0026] 2. CALPUFF model parameter settings 2-1. Table 2 CALPUFF control file parameter setting table

[0027] 2-2. Table 3 Dry and wet deposition parameter settings

[0028] 3. Simulation process: 3-1. Calculation of ground concentration:

[0029] Among them, the vertical term For processing mixed layers ( ) and multiple reflections from the ground, the calculation formula is:

[0030] 1km downstream from the dominant wind direction (downwind distance , lateral distance ) as an example, the diffusion coefficient calculated by the CALPUFF model 、 , substitute source strength and effective height , the ground concentration is calculated as:

[0031] 3-2. Sedimentation flux and soil enrichment: The CALPUFF model directly outputs second-by-second dry and wet deposition fluxes in units of: The annual precipitation flux is generated by accumulating the simulation results throughout the year. According to the model output, the maximum precipitation flux at 1 km southeast of the plant is 120 , through time unit conversion: 1 year = 365 days × 24 hours × 3600 seconds ≈ 3.154 × 10 7 seconds; the annual cumulative deposition flux is:

[0032] Ignoring the decay and chemical transformation of benzopyrene, the cumulative soil enrichment over 10 years is:

[0033] 3-3. Output results: The contaminated sites are mainly distributed within 1-3 km southeast of the plant area, downstream of the dominant wind direction, with a maximum deposition flux of 120 , background value area, 5km to the northwest, deposition flux <3 , which is consistent with the terrain and land use characteristics.

[0034] S3. Layout and testing of soil monitoring points.

[0035] 1. Distribution principles: 1-1. High-value area intensification: To address the sedimentation accumulation caused by fugitive emissions, one point is set up at 50m and 200m outside the factory boundary: two points each in the east, south, west and north, for a total of eight points; 1-2. Extension in the dominant wind direction: The stations are arranged at intervals of 500 m along the southeast direction, i.e., downstream of the dominant wind direction. Six stations are set in the 1-3 km high-impact zone, and four stations are set in the 3-5 km transition zone. 1-3. Background value comparison: Two background value points were set up in the forest 5km to the northwest, which is the non-pollution-affected area, and one control point was set up in the non-production area within the factory area; 1-4. Coupling of topography and land use: In the southeastern farmland area, a highly sensitive area, the point density was increased by 50%, with intervals of 300 m. In the western hilly area, where the impact of subsidence is relatively small, the intervals were expanded to 800 m, resulting in a total of 21 points. Figure 1 uses different symbols to distinguish between high-density and sparsely distributed areas.

[0036] 2. Testing method: Sampling frequency: According to the "Technical Guidelines for Soil and Groundwater Monitoring Around Industrial Enterprises (Trial)", sampling is conducted once in spring (April) and once in autumn (October) each year; Testing standards: Adopting the "High Performance Liquid Chromatography Method for the Determination of Polycyclic Aromatic Hydrocarbons in Soil and Sediment" (HJ784-2016), using high performance liquid chromatography-fluorescence detector (HPLC-FLD, Agilent 1260) for analysis, after Soxhlet extraction and silica gel column cleanup, the detection limit is 0.05 ng / g. Implementation effect verification: 1. Comparison of monitoring data and model predictions: Sampling was conducted at 21 monitoring points for five consecutive years (2020-2024). The annual average concentration of benzopyrene at the point 50m south of the factory boundary was 148ng / g in 2020, 152ng / g in 2021, 145ng / g in 2022, 150ng / g in 2023, and 149ng / g in 2024, respectively. The five-year average was 148.8±3.2ng / g, and the interannual variation rate was <5%; the simulation result of the corresponding grid point of the CALPUFF model was 142±15ng / g, and the average relative error was stable at 5.6%±1.2%, and all monitoring values ​​fell within the 95% confidence interval of 112-173ng / g predicted by the model, indicating that the model has good consistency in predicting concentrations in high-value areas.

[0037] 2. Regional validity of background values: The background value point concentration in the woodland 5 km to the northwest was 2.1±0.3 ng / g, which is consistent with the regional soil environmental background value of 2.0 ng / g according to the "National Soil Pollution Status Survey Bulletin" and is significantly lower than the points around the factory area, verifying that the layout of background control points can effectively distinguish between human pollution and natural background contributions.

[0038] 3. Point efficiency comparison: The traditional equally spaced point distribution method requires 30 points for a 5 km × 5 km area with a grid spacing of 500 m. However, this method, guided by the model, requires only 21 points, a 30% reduction in the number of points. It also increases the point density in high-pollution risk areas (1-3 km) by 60%, achieving the optimization goal of "intensive monitoring in pollution hotspots and reasonable decentralization in low-impact areas." This avoids the waste of resources and missed inspections in key areas caused by the traditional "indiscriminate point distribution" method.

[0039] 4. Comparative analysis of implementation effects: 4-1. Capability to accurately capture pollution hotspots: This method uses a model to identify pollution concentration areas, such as those downstream of the dominant wind direction, and increases monitoring points in key areas, significantly increasing the density of high-concentration points (accounting for nearly half of the total points). This ensures that all pollution hotspots are effectively covered and eliminates the risk of missed detections. Traditional methods use evenly spaced monitoring points. This results in insufficient monitoring density in pollution-intensive areas, leading to missed detection of some high-pollution hotspots and blind spots in key monitoring areas. Conclusion: The differentiated deployment strategy guided by the model solves the problem of missed detection of pollution hotspots caused by the traditional "indiscriminate monitoring" method, and achieves accurate monitoring of high-risk areas.

[0040] 4-2. Efficient coverage of background areas This method: By excluding pollution-affected areas through the model, only necessary control points are set up in truly conventional background areas (such as low-deposition areas far away from the plant), significantly reducing unnecessary monitoring points and improving the data's representativeness of the natural background. Traditional method: Uniform distribution of points results in a large number of redundant points in low-pollution or non-pollution areas, resulting in a waste of monitoring resources and insufficient data discrimination against background values; Conclusion: The model assists in background area identification and enables on-demand deployment of monitoring points, which reduces monitoring load and improves data validity.

[0041] 4.3 Summary of comprehensive optimization benefits

[0042] 5. Consistency of long-term enrichment trend: Comparing the monitoring data in 2023 and 2024, the interannual variation rate of benzopyrene concentration at points around the factory boundary was less than 5%, while the 10-year cumulative enrichment of 37.8g / m² simulated by the model was consistent with the measured value of 35.2±4.1g / m² within the error range, indicating that the model can accurately reflect the long-term accumulation process of benzopyrene in the soil, providing a reliable basis for environmental risk assessment of contaminated sites.

[0043] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and improvements may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and improvements fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing soil benzopyrene monitoring points around a coking enterprise based on the CALPUFF model, characterized in that: The following steps are involved: S1. Obtain the annual benzopyrene emissions and pollution source parameters of the coking enterprise, wherein the pollution source parameters include the spatial coordinates of the pollution source, emission height, flue gas thermodynamic parameters, and benzopyrene emission intensity; S2. Input the annual emissions of benzo(a)pyrene and pollution source parameters mentioned above into the CALPUFF atmospheric diffusion simulation system, integrate near-surface meteorological observation data, the three-dimensional atmospheric dynamic field output by the mesoscale WRF numerical model, the 90-meter resolution digital elevation model (DEM), and the 30-meter accuracy global land cover data (GLCD), and construct a high-resolution three-dimensional meteorological element field through terrain disturbance correction, slope wind field reconstruction, and mass conservation constraints. Simulate the diffusion, transmission, dry and wet deposition process of benzo(a)pyrene in the atmosphere and determine the spatial distribution range of the contaminated sites; S3. Based on the simulated spatial distribution difference characteristics of benzo(a)pyrene dry and wet deposition fluxes, monitoring points were set up around coking enterprises and in soil background value areas, and the benzo(a)pyrene content in the soil was detected in accordance with relevant standards.

2. The method for optimizing soil benzopyrene monitoring points around a coking enterprise based on the CALPUFF model according to claim 1, characterized in that: The annual emission of benzopyrene is calculated by the formula Calculate, where is the annual emission, kg / a; is the annual output of the product, t / a; is the emission factor, kg / t.

3. The method for optimizing soil benzopyrene monitoring points around a coking enterprise based on the CALPUFF model according to claim 1, characterized in that: The CALPUFF atmospheric diffusion simulation system adopts a three-dimensional Lagrangian puff tracking algorithm and consists of a CALMET meteorological field module, a CALPUFF diffusion calculation module and supporting auxiliary programs. The near-surface meteorological observation data include wind speed, wind direction, humidity, precipitation, temperature and air pressure data at the daily or hourly scale. The three-dimensional atmospheric dynamic field output by the mesoscale WRF numerical model is generated based on the following parameters: the projection method adopts the Lambert projection, the standard latitudes are N24° and N46°, the boundary conditions are the NCEPds083.21° reanalysis data, the microphysical process adopts the WSM3-classsimpleicescheme, the longwave radiation scheme adopts the RRTM scheme, and the shortwave radiation scheme adopts the Dudhia scheme; the boundary layer physics scheme adopts the YSU scheme, and the cumulus parameter scheme adopts the shallow convection Kain-Fritsch (newEta) scheme.

4. The method for optimizing the monitoring points of benzopyrene in soil around a coking enterprise based on the CALPUFF model according to claim 3, characterized in that: The control file parameters of the CALPUFF diffusion calculation module include: grid resolution, 100m×100m, matching the spatial scale of the 30-meter precision Global Land Cover Data (GLCD); pollutant type, PM 10 , as the transmission carrier of benzopyrene; the reference time zone is East 8, which is Beijing time; the operating time is consistent with the annual emission statistical period of benzopyrene; the meteorological data format is CALMET binary file.

5. The method for optimizing soil benzopyrene monitoring points around a coking enterprise based on the CALPUFF model according to claim 3, characterized in that: The dry and wet deposition parameter settings of the CALPUFF diffusion calculation module include: particle geometric mass mean diameter, 0.48 μm; geometric standard deviation, 2.00 μm; liquid precipitation clearance coefficient, 0.0004 s -1 ; Freezing precipitation removal coefficient, 0.00005s -1 The simulation process does not take into account the decay and chemical transformation of benzopyrene. The long-term enrichment of benzopyrene in the soil environment around the project (ng / m 2 ), and analyze the spatial distribution of contaminated sites.

6. The method for optimizing the monitoring points of benzopyrene in soil around a coking enterprise based on the CALPUFF model according to claim 3, characterized in that: In the CALPUFF diffusion calculation module, the ground concentration of pollutants is calculated using the following formula: ； ； Where, is the ground concentration, g / m 2 ; For the source of strength; 、 、 is the diffusion coefficient; d a Downwind distance refers to the horizontal distance from the center of the pollution source to the monitoring point along the dominant wind direction; d c The horizontal distance refers to the horizontal distance from the center of the pollution source to the monitoring point perpendicular to the dominant wind direction; He is the effective height; h is the height of the mixing layer; g is the vertical term of the Gaussian equation, which solves the problem of multiple reflections between the mixing layer and the ground.

7. The method for optimizing soil benzopyrene monitoring points around a coking enterprise based on the CALPUFF model according to claim 1, characterized in that: The 90-meter resolution digital elevation model (DEM) comes from the United States Geological Survey (USGS), and the 30-meter accuracy global land cover data (GLCD) comes from a 30-meter accuracy public database.

8. The method for optimizing soil benzopyrene monitoring points around a coking enterprise based on the CALPUFF model according to claim 1, characterized in that: The layout of the monitoring points includes: concentrated and dense layout around the factory boundary in areas with high deposition flux caused by unorganized emissions; layout along the downstream of the dominant wind direction determined by statistics of near-surface meteorological observation data, and in the direction of benzopyrene pollution diffusion determined by model simulation; setting up soil background value control points in areas far away from the factory and with low benzopyrene deposition flux.

9. The method for optimizing the monitoring points of benzopyrene in soil around a coking enterprise based on the CALPUFF model according to claim 8, characterized in that: The layout of the monitoring points also needs to couple topographic data, land use data and meteorological data to comprehensively analyze the spatial differentiation characteristics of benzopyrene dry and wet deposition fluxes, so as to more accurately determine the density and location of high-value aggregation areas, main transmission directions and background value areas.

10. The method for optimizing soil benzopyrene monitoring points around a coking enterprise based on the CALPUFF model according to claim 1, characterized in that: The detection of benzopyrene content in the soil includes: the sampling frequency is determined according to the Technical Guidelines for Soil and Groundwater Monitoring Around Industrial Enterprises (Trial); the detection method adopts the high-performance liquid chromatography method for the determination of polycyclic aromatic hydrocarbons in soil and sediments HJ 784-2016, specifically: the soil sample is subjected to Soxhlet extraction and silica gel column purification, and then analyzed using a high-performance liquid chromatograph equipped with a fluorescence detector.

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