A method for predicting health risks of outdoor sports based on WRF-Chem

Abstract

The present invention discloses a method for predicting health risks of outdoor sports based on WRF-Chem, comprising the following steps: WRF-Chem parameterization scheme selection: obtaining meteorological data, topographic data and pollution source emission inventory of the target area, and obtaining risk level simulation values ​​of risk types of each grid point in the simulation area involved in the motion trajectory through WEF-Chem model simulation, and further constructing a risk level matrix corresponding to different risk types of grid points in the simulation area, and obtaining the outdoor sports health risk value of the simulation area as a basis for determining the health of outdoor sports. The present invention realizes the forecast of meteorological elements, atmospheric pollutant concentrations and health risk prediction of outdoor sports at a specific time in the future, and provides scientific and technological support for policies and decisions for pollution warnings and convenient reminders.

Description

Technical Field

[0001] The invention belongs to the technical field of atmospheric environmental pollutant monitoring and forecasting, and in particular relates to a method for predicting outdoor sports health risks based on WRF-Chem. Background Art

[0002] The WRF-Chem model is a three-dimensional numerical model that can calculate the mutual influence and feedback between meteorological and chemical processes in real time, and can well reflect the physical and chemical processes in the real atmosphere. It is currently widely used in atmospheric chemistry research and has reliable simulation capabilities. It is credible in simulating air pollution and meteorological elements. The impact of meteorological elements and air pollutants on human health is very important. Accurate predictions can effectively help social groups plan their behavior. However, the simulation results of the WRF-Chem model are usually only used as simple air quality forecasts and cannot evaluate the health risks of outdoor sports at specific times in specific areas. Summary of the invention

[0003] Purpose of the invention: In view of the problems and shortcomings in the above-mentioned prior art, the purpose of the present invention is to provide a method for predicting health risks of outdoor sports based on WRF-Chem.

[0004] Technical solution: To achieve the above invention purpose, the present invention adopts the following technical solution: A WRF-Chem-based outdoor sports health risk prediction method, comprising the following steps:

[0005] S1, WRF-Chem simulates meteorological elements and air pollutant concentrations, obtains meteorological and topographic data of the simulation area and pre-processes them; selects appropriate parameterization schemes to obtain emission inventories of anthropogenic and natural sources, and obtains the concentrations of ozone, nitrogen dioxide and fine particulate matter, surface temperature, surface wind speed and precipitation in the air of the simulation area based on the WRF-Chem model simulation;

[0006] S2, determine all grids involved in the outdoor motion trajectory in the simulation area, and record the grid points of all grids to form a grid point coordinate set , represents the coordinates (x, y) of the grid point numbered k, and the total number of grid points involved in the motion trajectory is N;

[0007] S3, Calculation of risk index of meteorological factors for outdoor sports

[0008] (1) Based on the WRF-Chem numerical simulation, the surface temperature is obtained, and the temperature data set at all grid points involved in the motion trajectory is determined and obtained. , T k represents the temperature corresponding to the grid point numbered k, and then the mean surface temperature T on the outdoor sports trajectory is obtained. , and based on the mean surface air temperature T Determine the level of health risk Y k1 ;

[0009] ,

[0010] Y k1 Indicates that the grid point number is k Risk Category No. a= Risk level of 1;

[0011] (2) Based on the WRF-Chem numerical simulation, the surface wind speed is obtained, and the wind speed data set at all grid points involved in the motion trajectory is determined and obtained. , and then get the average surface wind speed V on the outdoor sports trajectory, , and based on the mean surface wind speed V Determine the level of health risk Y k2 ,

[0012] ,

[0013] Y k2 Indicates that the grid point number is k Risk Category No. a= Risk level 2;

[0014] (3) Based on the WRF-Chem numerical simulation, the 24-hour rainfall data set at all grid points involved in the motion trajectory is determined and obtained. , and then get the 24h rainfall mean R on the outdoor sports trajectory, , and based on the 24h rainfall average R Determine the level of health risk Y k3 ,

[0015] ,

[0016] Y k3 Indicates that the grid point number is k Risk Category No. a= Risk level 3;

[0017] S4. Calculation of risk index of air pollutant concentration for outdoor sports

[0018] (1) Based on the WRF-Chem numerical simulation, the surface ozone concentration CON-O3 is obtained, and the surface ozone concentration dataset at all grid points involved in the motion trajectory is determined and obtained. , and then get the average surface ozone concentration CON_O3 on the outdoor sports trajectory, , and based on the mean surface ozone concentration CON_O3 Determine the level of health risk Y k4 ,

[0019] ,

[0020] Y k4 Indicates that the grid point number is k Risk Category No. a= Risk level of 4;

[0021] (2) Obtain surface nitrogen dioxide based on WRF-Chem numerical simulation, and determine and obtain the surface nitrogen dioxide dataset at all grid points involved in the motion trajectory , and then get the average value of surface nitrogen dioxide CON_NO2 on the outdoor sports track, , and based on the average surface nitrogen dioxide CON_NO2 Determine the level of health risk Y k5 ,

[0022] ,

[0023] Y k5 Indicates that the grid point number is k Risk Category No. a=5 The risk level;

[0024] (3) Based on the WRF-Chem numerical simulation, the surface particle concentration is obtained, and the surface particle concentration data set at all grid points involved in the motion trajectory is determined and obtained. , and then get the mean surface particle concentration CON_PM on the outdoor movement trajectory, , and based on the mean surface particle concentration CON_PM Determine the level of health risk Y k6，

[0025] ,

[0026] Y k6 Indicates that the grid point number is k Risk Category No. a= Risk level 6;

[0027] S5: Number the risk types including surface temperature, surface wind speed, rainfall, surface ozone concentration, surface nitrogen dioxide concentration, and surface fine particulate matter concentration in sequence as a=1, 2, 3, 4, 5, 6, and construct a risk level matrix Y corresponding to different risk types at the simulation area grid points. ka , the outdoor sports health risk value P of the simulated area is calculated by the following formula:

[0028] ,

[0029] Where Y ka Represents the risk level matrix with grid point number k and risk type number a; when P>4, it is judged that the health risk of outdoor sports is high and it is not suitable to engage in outdoor sports.

[0030] Furthermore, the meteorological data in step S1 is derived from NCEP Global Numerical Weather Forecast Model GFS data.

[0031] Furthermore, the preprocessing in step S1 includes: using the ungrib program and the metgrid program in the WRF-Chem model to match the meteorological data and the terrain data, so that the meteorological data and the terrain data match the grid points of the simulation area.

[0032] Furthermore, the specific method of selecting a suitable parameterization scheme to obtain an inventory of anthropogenic and natural source emissions in step S1 includes: selecting the RRTMG scheme or the Goddard scheme for radiation processes, selecting the Noah scheme for land surface processes, selecting the MYJ scheme for boundary layer processes, selecting the Lin scheme for microphysical processes, selecting the Grell scheme for cloud physics processes, and selecting the CMBZ-MOSAIC scheme or the RADM2 scheme for aerosol chemical processes.

[0033] Furthermore, the simulation area in step S2 is a gridded area, and the grid resolution is set below 1 km.

[0034] Beneficial effects: Compared with the prior art, the present invention establishes a WRF-Chem-based method for predicting health risks of outdoor sports, which can realize the forecast of meteorological elements and atmospheric pollutant concentrations at a specific time in the future and the health risk prediction of outdoor sports, and provide scientific and technological support for pollution warning and convenience reminders in terms of policy and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the process of the outdoor sports health risk prediction method based on WRF-Chem according to the present invention;

[0036] Figure 2 It is a schematic diagram of the outdoor sports trajectory in the simulation area according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0038] The technical ideas and processes of the outdoor sports health risk prediction method based on WRF-Chem of the present invention are described in detail. Figure 1 As shown, the following steps are included:

[0039] 1. WRF-Chem simulates meteorological elements and air pollutant concentrations

[0040] (1) Determine the evaluation time, obtain the NCEP Global Numerical Weather Forecast Model (GFS) data as meteorological data, obtain terrain data, set the simulation area, and preprocess the meteorological and terrain data.

[0041] (2) Select an appropriate parameterization scheme, obtain an inventory of anthropogenic and natural source emissions, and use the WRF-Chem model to simulate the concentrations of ozone, nitrogen dioxide, and fine particulate matter in the air, surface temperature, surface wind speed, and precipitation.

[0042] 2. Determine the location of outdoor movement trajectories in the WRF-Chem simulation area

[0043] (1) Determine the location of outdoor movement trajectories in the WRF-Chem simulation area

[0044] The WRF-Chem simulation area is a gridded area. The meteorological elements and ozone, nitrogen dioxide and fine particulate matter concentration values ​​calculated by the model are the values ​​at each grid point. For outdoor sports, the grid resolution should be set to less than 1km. Taking the resolution of 1km as an example, according to the location of the outdoor sports, the trajectory of the outdoor sports is determined in the simulation area. The specific areas involved in the outdoor sports trajectory are as follows: Figure 2 shown.

[0045] (2) Determine the grid points involved in outdoor sports

[0046] Determine all grid points in the simulation area , x is the horizontal coordinate of the grid point, y is the vertical coordinate of the grid point, and the specific coordinates of each grid point are recorded to form a grid point coordinate set , the total number of grid points is N.

[0047] 3. Calculate the risk index of meteorological factors for outdoor sports

[0048] (1) Calculate the impact of temperature on the risk of outdoor sports

[0049] The surface temperature T obtained by WRF-Chem numerical simulation is the temperature data set at the specific grid points involved in outdoor sports: , so the average surface temperature over the entire outdoor exercise trajectory is , set the health risk level of temperature for outdoor sports as shown in Table 1:

[0050] Table 1 The impact of temperature on the health risk level of outdoor sports

[0051]

[0052] (2) Calculate the impact of wind speed on health risks of outdoor sports

[0053] The surface wind speed V obtained by WRF-Chem numerical simulation is the wind speed data set at the specific grid points involved in outdoor sports: , so the average surface wind speed over the entire outdoor motion trajectory is , set the health risk level of temperature for outdoor sports as shown in Table 2:

[0054] Table 2 Health risk levels of wind speed affecting outdoor sports

[0055]

[0056] (3) Calculate the impact of rainfall on health risks of outdoor sports

[0057] The rainfall R every 24 hours obtained by WRF-Chem numerical simulation and the wind speed data set at the specific grid points involved in outdoor sports are: , so the average surface wind speed over the entire outdoor motion trajectory is , the health risk level of rainfall for outdoor sports is set as follows in Table 3:

[0058] Table 3 Health risk level of outdoor sports affected by rainfall

[0059]

[0060] 4. Calculate the risk index of air pollutant concentration for outdoor sports

[0061] (1) Calculate the risk of ozone concentration on outdoor sports

[0062] The surface ozone concentration CON-O3 obtained by WRF-Chem numerical simulation is the surface ozone concentration value set at the specific grid points involved in outdoor sports: , so the average surface ozone concentration over the entire outdoor sports trajectory is , set the health risk level of ozone concentration for outdoor sports as shown in Table 4:

[0063] Table 4 Health risk levels of ozone concentration affecting outdoor sports

[0064]

[0065] (2) Calculating the risk of nitrogen dioxide on outdoor sports

[0066] The surface nitrogen dioxide concentration obtained by WRF-Chem numerical simulation is the surface nitrogen dioxide concentration value set at the specific grid points involved in outdoor sports: , so the average surface ozone concentration over the entire outdoor sports trajectory is , the health risk levels of ozone concentration for outdoor sports are set as follows in Table 5:

[0067] Table 5 Health risk levels of nitrogen dioxide concentration affecting outdoor sports

[0068]

[0069] (3) Calculate the risk of fine particulate matter on outdoor sports

[0070] The surface fine particle concentration CON_PM obtained by WRF-Chem numerical simulation is the numerical set of surface fine particle concentration at specific grid points involved in outdoor sports: , so the average concentration of fine particles on the surface of the entire outdoor movement track is , the health risk levels of ozone concentration for outdoor sports are set as follows in Table 6:

[0071] Table 6 Health risk levels of fine particulate matter concentration affecting outdoor sports

[0072]

[0073] 5. Provide a health risk level matrix for outdoor sports

[0074] According to Tables 1-6, the meteorological elements and air pollutant concentrations at the prediction time are converted into risk levels. The health risk level of the grid point k involved in the outdoor sports trajectory at the prediction time is set to Y ka ,in k is the grid point identifier, a Indicates the risk type. a =1,2,3,4,5,6, corresponding to surface temperature, surface wind speed, rainfall, surface ozone concentration, surface nitrogen dioxide concentration and surface fine particulate matter concentration, and finally the risk level matrix of all grid points involved in outdoor sports is listed as follows in Table 7:

[0075] Table 7 Risk level matrix of all grid points involved in outdoor sports

[0076]

[0077] 6. Prediction of health risks of outdoor sports

[0078] The risk value P calculation formula for the area involved in outdoor sports is:

[0079] ,

[0080] When P is greater than 4, it is judged that the health risk of outdoor sports is high and it is not suitable to engage in outdoor sports.

[0081] The process of the outdoor sports health risk prediction method based on WRF-Chem of the present invention is further described below through a specific case, which specifically includes the following steps:

[0082] 1. This example mainly simulates the outdoor health risks in Nanjing, and the predicted simulation time is 17:00 on June 28, 2024.

[0083] 2. Step 1: Obtain the meteorological data that drives the operation of WRF-Chem during the simulation time, the NCEP Global Numerical Weather Forecast Model GFS data on June 28, 2024.

[0084] Step 2: Obtain the terrain data, MODIS land use type data, and ground elevation data required for WRF-Chem operation within the simulation time.

[0085] Step 3: Set the simulation area according to its geographical location and size, pre-process the meteorological and terrain data, set the model grid, and use Lambert projection to set a nested grid with a horizontal grid spacing of 1 km and a grid range of 31~33. 0 N, 117.5~120 0 E, including the Nanjing area, uses the eta coordinate system in the vertical direction and is divided into 32 layers.

[0086] Step 4: Select parameterization schemes for combination: RRTMG process for radiation process, Noah scheme for land surface process, MYJ scheme for boundary layer process, Lin scheme for microphysical process, Grell scheme for cloud physics process, and CMBZ-MOSAIC scheme for aerosol chemistry process.

[0087] Step 5: Obtain the pollution source emission inventory, where the anthropogenic source emission inventory comes from the Chinese Multi-Scale Emission Inventory Model MEIC, and the natural source emission inventory comes from the natural source gas particle emission model MEGAN. Preprocess the pollution source data to obtain input data consistent with the time, horizontal, and vertical distribution settings of the WRF-Chem simulation area.

[0088] Step 6: Run WRF-Chem according to the parameterization scheme set in Step 4 to obtain the meteorological elements and air pollutant concentration data at 17:00 on June 28, 2024. This time, the health risks of outdoor sports at all grid points in Nanjing are evaluated, and the surface temperature, wind speed, rainfall, ozone concentration, nitrogen dioxide concentration and fine particulate matter concentration at all grid points in Nanjing are output.

[0089] Step 7: Calculate the risk levels for different grid points. According to the surface temperature, wind speed, rainfall, ozone concentration, nitrogen dioxide concentration and fine particulate matter concentration at each grid point, look up the corresponding risk level setting table to obtain the surface temperature risk level, wind speed risk level, rainfall risk level, ozone risk level, nitrogen dioxide risk level and fine particulate matter risk level for each grid point.

[0090] Step 8: Add the surface temperature risk level, wind speed risk level, rainfall risk level, ozone risk level, nitrogen dioxide risk level and fine particulate matter risk level of each grid point to obtain the health risk level of each grid point.

[0091] Step 9: Take the average of the health risk levels of all grid points involved to obtain the outdoor sports health risk prediction index for Nanjing area.

[0092] Step 10: The outdoor health risk prediction index in Nanjing at 17:00 on June 28, 2024 is 2, which is less than 4. It is determined that the outdoor sports health risk in Nanjing at this moment is low.

[0093] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solutions and technical concepts of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A WRF-Chem-based outdoor sports health risk prediction method, characterized in that The following steps are involved: S1, WRF-Chem simulates meteorological elements and air pollutant concentrations, obtains meteorological and topographic data of the simulation area and pre-processes them; selects appropriate parameterization schemes to obtain anthropogenic and natural source emission inventories, and obtains surface ozone, nitrogen dioxide and fine particulate matter concentrations, surface air temperature, surface wind speed and rainfall in the simulation area based on the WRF-Chem model simulation; S2, determine all grids involved in the outdoor motion trajectory in the simulation area, and record the grid points of all grids to form a grid point coordinate set , represents the coordinates (x, y) of the grid point numbered k, and the total number of grid points involved in the motion trajectory is N; S3, Calculation of risk index of meteorological factors for outdoor sports (1) Based on the WRF-Chem numerical simulation, the surface temperature is obtained, and the temperature data set at all grid points involved in the motion trajectory is determined and obtained. , T k represents the temperature corresponding to the grid point numbered k, and then the mean surface temperature T on the outdoor sports trajectory is obtained. , and based on the mean surface air temperature T Determine the level of health risk Y k1 ; ， Y k1 Indicates that the grid point number is k Risk Category No. a= Risk level of 1; (2) Based on the WRF-Chem numerical simulation, the surface wind speed is obtained, and the wind speed data set at all grid points involved in the motion trajectory is determined and obtained. , and then get the mean surface wind speed V on the outdoor sports trajectory, , and based on the mean surface wind speed V Determine the level of health risk Y k2 , ， Y k2 Indicates that the grid point number is k Risk Category No. a= Risk level 2; (3) Based on the WRF-Chem numerical simulation, the 24-hour rainfall data set at all grid points involved in the motion trajectory is determined and obtained. , and then get the 24h rainfall mean R on the outdoor sports trajectory, , and based on the 24h rainfall average R Determine the level of health risk Y k3 , ， Y k3 Indicates that the grid point number is k Risk Category No. a= Risk level 3; S4. Calculation of risk index of air pollutant concentration for outdoor sports (1) Based on the WRF-Chem numerical simulation, the surface ozone concentration CON-O3 is obtained, and the surface ozone concentration dataset at all grid points involved in the motion trajectory is determined and obtained. , and then get the average surface ozone concentration CON_O3 on the outdoor sports trajectory, , and based on the mean surface ozone concentration CON_O3 Determine the level of health risk Y k4 , ， Y k4 Indicates that the grid point number is k Risk Category No. a= Risk level of 4; (2) Obtain surface nitrogen dioxide based on WRF-Chem numerical simulation, and determine and obtain the surface nitrogen dioxide dataset at all grid points involved in the motion trajectory , and then get the average value of surface nitrogen dioxide CON_NO2 on the outdoor sports track, , and based on the average surface nitrogen dioxide CON_NO2 Determine the level of health risk Y k5 , ， Y k5 Indicates that the grid point number is k Risk Category No. a=5 the risk level; (3) Based on the WRF-Chem numerical simulation, the surface fine particle concentration is obtained, and the surface fine particle concentration data set at all grid points involved in the motion trajectory is determined and obtained. , and then get the mean surface fine particle concentration CON_PM on the outdoor movement track, , and based on the mean surface fine particle concentration CON_PM Determine the level of health risk Y k6， ， Y k6 Indicates that the grid point number is k Risk Category No. a= Risk level 6; S5: Number the risk types including surface temperature, surface wind speed, rainfall, surface ozone concentration, surface nitrogen dioxide concentration, and surface fine particulate matter concentration in sequence as a=1, 2, 3, 4, 5, 6, and construct a risk level matrix Y corresponding to different risk types at the simulation area grid points. ka , the outdoor sports health risk value P of the simulated area is calculated by the following formula: ， Where Y ka Represents the risk level matrix with grid point number k and risk type number a; when P>4, it is judged that the health risk of outdoor sports is high and it is not suitable to engage in outdoor sports.

2. The outdoor sports health risk prediction method based on WRF-Chem according to claim 1 is characterized in that: The meteorological data in step S1 comes from the NCEP Global Numerical Weather Forecast Model GFS data.

3. The outdoor sports health risk prediction method based on WRF-Chem according to claim 1 is characterized in that: The preprocessing in step S1 includes: using the ungrib program and metgrid program in the WRF-Chem model to match the meteorological data and the terrain data, so that the meteorological data and the terrain data match the grid points of the simulation area.

4. The outdoor sports health risk prediction method based on WRF-Chem according to claim 1 is characterized in that: The specific method of selecting a suitable parameterization scheme to obtain anthropogenic and natural source emission inventories in step S1 includes: selecting the RRTMG scheme or the Goddard scheme for radiation processes, selecting the Noah scheme for land surface processes, selecting the MYJ scheme for boundary layer processes, selecting the Lin scheme for microphysical processes, selecting the Grell scheme for cloud physics processes, and selecting the CMBZ-MOSAIC scheme or the RADM2 scheme for aerosol chemical processes.

5. The outdoor sports health risk prediction method based on WRF-Chem according to claim 1 is characterized in that: The simulation area in step S2 is a gridded area, and the grid resolution is set below 1 km.

Images