Method and system for analyzing vertical distribution characteristics of atmospheric pollutants

Abstract

The application discloses a kind of atmospheric pollutant vertical distribution characteristic analysis method and system, it is related to atmospheric pollution analysis field, including: obtaining the vertical distribution data of wind speed, wind direction, temperature, relative humidity and atmospheric pollutant concentration in atmosphere and all vertical distribution data are layered according to height direction;In each layer, wind field is divided according to multiple directions contained in wind direction in wind field;In each direction divided in wind field, the vertical distribution data of wind speed, temperature and relative humidity and the height where it is located are respectively regressed with the vertical distribution data of atmospheric pollutant concentration, and the optimal regression equation is obtained;Linear reconstruction is carried out to optimal regression equation to obtain contribution value equation;The vertical distribution data of wind speed, temperature, relative humidity and the height where it is located are substituted into contribution value equation, obtain the contribution value in multiple directions divided in wind field in multiple layers, analyze the influence of wind speed, temperature, relative humidity and the height where it is located in atmosphere on atmospheric pollutant concentration, and obtain the vertical distribution characteristic of atmospheric pollutant.The application can analyze the vertical distribution relationship between meteorological parameter and atmospheric pollutant.

Description

Technical Field

[0001] This invention relates to the field of air pollution analysis, and in particular to a method and system for analyzing the vertical distribution characteristics of air pollutants. Background Technology

[0002] The vertical distribution of air pollutants is affected by meteorological conditions, geographical environment, location of pollution sources and emission intensity. Research on the vertical distribution characteristics of air pollution with high spatiotemporal resolution is of great significance for air pollution prevention and control.

[0003] In existing technologies, the analysis methods for the vertical distribution characteristics of air pollution are mainly qualitative studies, and usually focus on the deformation characteristics of the vertical profiles of individual meteorological fields and air pollutants. For example, the deformation characteristics of the vertical profiles of pollutants obtained by vertical detection using remote sensing equipment such as meteorological towers, weather balloons, aerial surveys by aircraft, moored boats, or radar are combined with the vertical distribution of meteorological parameters to conduct a qualitative coupling analysis of physicochemical properties.

[0004] However, existing technologies use the analysis of the vertical distribution profiles of pollutants in individual meteorological fields as a research method, without systematically quantifying and analyzing the relationship between meteorological parameters and the vertical distribution of atmospheric pollutants. Summary of the Invention

[0005] This invention provides a method and system for analyzing the vertical distribution characteristics of air pollutants, which can solve the problem in the prior art that there is no systematic quantification and analysis of the relationship between meteorological parameters and the vertical distribution of air pollutants.

[0006] This invention provides a method for analyzing the vertical distribution characteristics of atmospheric pollutants, comprising the following steps: acquiring vertical distribution data of wind speed, wind direction, temperature, relative humidity, and atmospheric pollutant concentration in the atmosphere, and stratifying all vertical distribution data according to the height direction; within each stratum, dividing the wind field according to multiple directions included in the wind direction, wherein the wind field is described by the wind direction and the corresponding wind speeds in the multiple directions included in the wind direction, which together describe the spatial distribution and intensity of air flow; in each direction divided in the wind field, performing regression analysis on the vertical distribution data of wind speed, temperature, and relative humidity and their respective heights with the vertical distribution data of atmospheric pollutant concentration to obtain the optimal regression equation; linearly reconstructing the optimal regression equation to obtain the contribution value equation; the contribution value equation represents the contribution value of wind speed, temperature, relative humidity, and height to the atmospheric pollutant concentration; substituting the vertical distribution data of wind speed, temperature, and relative humidity and their respective heights into the contribution value equation to obtain the contribution values ​​in multiple directions divided in the wind field within multiple strata, analyzing the influence of wind speed, temperature, relative humidity, and height in the atmosphere on the atmospheric pollutant concentration, and obtaining the vertical distribution characteristics of atmospheric pollutants.

[0007] Furthermore, within each layer, the wind field is divided according to the multiple directions included in the wind direction within the wind field. Specifically, based on the wind direction data of each layer in the height direction, the wind field is divided into 16 wind field directions in intervals of 22.5 degrees within the range of 0 to 360 degrees.

[0008] Furthermore, the specific steps for obtaining the optimal regression equation include:

[0009] Regression analyses were performed on the vertical distribution data of temperature and the vertical distribution data of atmospheric pollutant concentrations, using linear, reciprocal, and exponential relationships.

[0010] A regression analysis was performed on the vertical distribution data of wind speed and the vertical distribution data of air pollutant concentrations to determine the linear relationship. The linear equation for wind speed is as follows:

[0011] PM = K2 * WS;

[0012] Where K2 is the fitting coefficient for the linear relationship, and WS is the wind speed;

[0013] Regression analyses were performed on the vertical distribution data of relative humidity and the vertical distribution data of air pollutant concentrations, using linear, reciprocal, and exponential relationships.

[0014] A regression analysis was performed on the vertical distribution data of atmospheric pollutant concentrations at different altitudes. The linear equation for altitude is as follows:

[0015] PM = K4 * H;

[0016] Where K4 is the fitting coefficient for the linear relationship, and H is the height;

[0017] The regression equation with the best fit was selected as the regression analysis method for the vertical distribution data of wind speed, temperature and relative humidity, as well as the vertical distribution data of atmospheric pollutant concentrations at different altitudes.

[0018] Furthermore, the regression analysis of linear, reciprocal, and exponential relationships between the vertical distribution data of temperature and the vertical distribution data of air pollutant concentrations includes the following steps:

[0019] When temperature and atmospheric pollutant concentration have a linear relationship, the linear equation for temperature is:

[0020] PM = K1 * T;

[0021] When temperature and atmospheric pollutant concentration have an inverse relationship, the inverse equation for temperature is:

[0022]

[0023] When temperature and atmospheric pollutant concentration have an exponential relationship, the temperature exponential equation is:

[0024] PM = K1 * e (f(T)) ;

[0025] Where PM is the concentration of air pollutants, K1 is the fitting coefficient for linear or nonlinear relationships, and T is the temperature.

[0026] Furthermore, the regression analysis of linear, reciprocal, and exponential relationships between the vertical distribution data of relative humidity and the vertical distribution data of air pollutant concentrations includes the following steps:

[0027] When relative humidity and atmospheric pollutant concentration have a linear relationship, the linear equation for relative humidity is:

[0028] PM = K3 * RH;

[0029] When relative humidity and atmospheric pollutant concentration are inversely related, the equation for the reciprocal of relative humidity is:

[0030]

[0031] When relative humidity and atmospheric pollutant concentration have an exponential relationship, the equation for the relative humidity index is:

[0032] PM = K3 * e (f(RH)) ;

[0033] Where K3 is the fitting coefficient for linear or nonlinear relationships, and RH is the concentration of air pollutants.

[0034] Furthermore, the specific steps for linearly reconstructing the optimal regression equation to obtain the contribution value equation include:

[0035] When the linear relationship between temperature and atmospheric pollutant concentration shows the best fit, the regression equation that best fits relative humidity and atmospheric pollutant concentration is selected.

[0036] When the linear relationship between relative humidity and atmospheric pollutant concentration yields the best fit, the equation for the first contribution value is:

[0037] PM=K1′*T+K′2*WS+K′3*RH+K′4*H;

[0038] When the fit between relative humidity and atmospheric pollutant concentration is inversely proportional, the equation for the second contribution value is:

[0039]

[0040] When the relative humidity and air pollutant concentration show the best fit, the equation for the third contribution value is:

[0041] PM=K1′*T+K′2*WS+K′3″*e (f(RH)) +K′4*H;

[0042] Where K1′, K′2, K′3, and K′4 are the fitting coefficients for linear relationships, and K′3′ and K′3″ are the fitting coefficients for nonlinear relationships;

[0043] When the fit between temperature and air pollutant concentration is inversely proportional, the regression equation with the best fit between relative humidity and air pollutant concentration is selected; when the fit between temperature and air pollutant concentration is exponential, the regression equation with the best fit between relative humidity and air pollutant concentration is selected; the corresponding contribution value is obtained based on a determined contribution value equation.

[0044] Furthermore, when the fit between temperature and atmospheric pollutant concentration is inversely proportional, the regression equation that best fits relative humidity and atmospheric pollutant concentration is selected. Specific steps include:

[0045] When the linear relationship between relative humidity and atmospheric pollutant concentration yields the best fit, the equation for the fourth contribution value is:

[0046]

[0047] When the fit between relative humidity and atmospheric pollutant concentration is inversely proportional, the equation for the fifth contribution value is:

[0048]

[0049] When the relative humidity and atmospheric pollutant concentration show the best fit, the equation for the sixth contribution value is:

[0050]

[0051] Where K′2, K′3, and K′4 are the fitting coefficients for linear relationships, and K1″, K′3′, and K′3″ are the fitting coefficients for nonlinear relationships.

[0052] Furthermore, when the relationship between temperature and atmospheric pollutant concentration shows the best fit, the regression equation that best fits relative humidity and atmospheric pollutant concentration is selected. Specific steps include:

[0053] When the linear relationship between relative humidity and atmospheric pollutant concentration yields the best fit, the equation for the seventh contribution value is:

[0054] PM=K1″′*e (f(T)) +K′2*WS+K′3*RH+K′4*H;

[0055] When the fit between relative humidity and atmospheric pollutant concentration is inversely proportional, the equation for the eighth contribution value is:

[0056]

[0057] When the relative humidity and atmospheric pollutant concentration show the best fit, the equation for the ninth contribution value is:

[0058] PM=K1″′*e (f(T)) +K′2*WS+K′3″*e (f(RH)) +K′4*H;

[0059] Where K′2, K′3, and K′4 are the fitting coefficients for linear relationships, and K1″′, K′3′, and K′3″ are the fitting coefficients for nonlinear relationships.

[0060] Furthermore, the specific steps for obtaining the vertical distribution characteristics of air pollutants include: analyzing the physicochemical coupling characteristics of wind speed, wind direction, temperature, and relative humidity with air pollutants in each height layer based on the contribution values ​​in multiple directions within multiple layers; and summarizing the physicochemical coupling characteristics of air pollutants with wind speed, wind direction, temperature, and relative humidity in each layer as the result of the vertical distribution characteristics of air pollutants.

[0061] This invention provides a system for analyzing the vertical distribution characteristics of atmospheric pollutants, comprising: a data layering module for acquiring vertical distribution data of wind speed, wind direction, temperature, relative humidity, and atmospheric pollutant concentration in the atmosphere and layering all vertical distribution data according to the height direction; an atmosphere partitioning module for dividing the wind field within each layer according to multiple directions included in the wind direction, wherein the wind field is described by the wind direction and the corresponding wind speeds in the multiple directions included in the wind direction, which together describe the spatial distribution and intensity of air flow; and an equation construction module for integrating the vertical distribution data of wind speed, temperature, and relative humidity in each direction of the wind field. Regression analysis is performed on the vertical distribution data of atmospheric pollutant concentrations at different altitudes to obtain the optimal regression equation. The optimal regression equation is then linearly reconstructed to obtain the contribution value equation. The contribution value equation represents the contribution of wind speed, temperature, relative humidity, and altitude to the atmospheric pollutant concentration. The vertical distribution feature analysis module is used to substitute the vertical distribution data of wind speed, temperature, and relative humidity, as well as the altitude, into the contribution value equation to obtain the contribution values ​​in multiple directions within multiple layers of the wind field. This analyzes the influence of wind speed, temperature, relative humidity, and altitude on the atmospheric pollutant concentration and obtains the vertical distribution characteristics of atmospheric pollutants.

[0062] This invention provides a method and system for analyzing the vertical distribution characteristics of air pollutants. Compared with the prior art, its advantages are as follows:

[0063] The vertical distribution data of wind speed, wind direction, temperature, relative humidity, and atmospheric pollutant concentration in the atmosphere were stratified according to altitude. Within each stratum, the wind field was divided according to multiple directions included in the wind direction. In each direction of the wind field, regression analysis was performed on the vertical distribution data of wind speed, temperature, and relative humidity, along with their respective altitudes, with the vertical distribution data of atmospheric pollutant concentration to obtain the optimal regression equation. The optimal regression equation was then linearly reconstructed. Finally, based on the contribution values ​​of wind speed, wind direction, temperature, relative humidity, and altitude in multiple directions within the wind field across multiple strata, the influence of wind speed, wind direction, temperature, relative humidity, and altitude on atmospheric pollutant concentration was analyzed to obtain the vertical distribution characteristics of atmospheric pollutants.

[0064] The process involves regressing the vertical distribution data of wind speed, temperature, and relative humidity, along with their respective altitudes, with the vertical distribution data of air pollutant concentrations to obtain the optimal regression equation. Linear reconstruction of this optimal regression equation quantifies the relationship between meteorological parameters such as wind speed, temperature, and relative humidity and the vertical distribution of air pollutants into specific contribution values ​​through regression analysis and linear reconstruction. This allows for a direct numerical reflection of the impact of meteorological parameters on air pollutants. Attached Figure Description

[0065] Figure 1 A schematic diagram of the technical solution for a method and system for analyzing the vertical distribution characteristics of air pollutants provided in an embodiment of the present invention;

[0066] Figure 2 A wind field partitioning diagram of a method and system for analyzing the vertical distribution characteristics of air pollutants provided in this embodiment of the invention;

[0067] Figure 3 A diagram illustrating the relationship between temperature and atmospheric pollutant concentration in a method and system for analyzing the vertical distribution characteristics of atmospheric pollutants, provided in this embodiment of the invention;

[0068] Figure 4 This invention provides a diagram showing the relationship between relative humidity and atmospheric pollutant concentration in a method and system for analyzing the vertical distribution characteristics of atmospheric pollutants. Detailed Implementation

[0069] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0070] See Figures 1-4This invention provides a method for analyzing the vertical distribution characteristics of atmospheric pollutants, comprising the following steps:

[0071] Step 1: Obtain the vertical distribution data of wind speed, wind direction, temperature, relative humidity, and atmospheric pollutant concentration in the atmosphere, and stratify all vertical distribution data according to the height direction.

[0072] Step 2: Within each layer, the wind field is divided according to the multiple directions included in the wind direction within the wind field.

[0073] Step 3: In each direction of the wind field, perform regression analysis on the vertical distribution data of wind speed, temperature and relative humidity and their respective altitudes with the vertical distribution data of air pollutant concentrations to obtain the optimal regression equation; reconstruct the optimal regression equation linearly to obtain the contribution value equation.

[0074] Step 4: Substitute the vertical distribution data of wind speed, temperature, and relative humidity, along with their corresponding altitudes, into the contribution value equation to obtain contribution values ​​in multiple directions within the wind field across multiple strata. Analyze the impact of wind speed, temperature, relative humidity, and altitude on atmospheric pollutant concentrations to obtain the vertical distribution characteristics of atmospheric pollutants. Specifically, based on the contribution values ​​within the atmospheric flow range in multiple directions across multiple strata, analyze the physicochemical coupling characteristics of wind speed, wind direction, temperature, and relative humidity with atmospheric pollutants in each altitude stratum. Summarize the physicochemical coupling characteristics of atmospheric pollutants with wind speed, wind direction, temperature, and relative humidity for each stratum as the result of the vertical distribution characteristics of atmospheric pollutants.

[0075] Specifically, the atmospheric flow range needs to be defined in each direction based on wind speed data. Specifically: when the wind speed is less than 3 m / s, the atmospheric flow range is classified as local transport. When the wind speed is between 3 and 7 m / s, the atmospheric flow range is classified as regional transport. When the wind speed is greater than 7 m / s, the atmospheric flow range is classified as long-distance transport. The wind speed boundaries are 3 m / s and 7 m / s. At a wind speed of 3 m / s, the airflow travels approximately 260 kilometers per day, and at 7 m / s, it travels approximately 600 kilometers per day. The wind speed boundaries can be adjusted according to the specific analysis scenario, for example, based on the area of ​​the province or city where the monitoring station is located.

[0076] The regression equation with the best fit was selected as the regression analysis method for the vertical distribution data of wind speed, temperature, relative humidity and altitude, respectively, and the vertical distribution data of air pollutant concentration.

[0077] When temperature and atmospheric pollutant concentration have a linear relationship, the linear equation for temperature is:

[0078] PM = K1 * T;

[0079] When temperature and atmospheric pollutant concentration have an inverse relationship, the inverse equation for temperature is:

[0080]

[0081] When temperature and atmospheric pollutant concentration have an exponential relationship, the temperature exponential equation is:

[0082] PM = K1 * e (f(T)) ;

[0083] Where PM is the concentration of air pollutants, K1 is the fitting coefficient for linear or nonlinear relationships, and T is the temperature.

[0084] When wind speed and air pollutant concentration have a linear relationship, the linear equation for wind speed is:

[0085] PM = K2 * WS;

[0086] Where K2 is the fitting coefficient for the linear relationship, and WS is the wind speed.

[0087] When relative humidity and atmospheric pollutant concentration have a linear relationship, the linear equation for relative humidity is:

[0088] PM = K3 * RH;

[0089] When relative humidity and atmospheric pollutant concentration are inversely related, the equation for the reciprocal of relative humidity is:

[0090]

[0091] When relative humidity and atmospheric pollutant concentration have an exponential relationship, the equation for the relative humidity index is:

[0092] PM = K3 * e (f(RH)) ;

[0093] Where K3 is the fitting coefficient for a linear or nonlinear relationship, and RH is the concentration of air pollutants. When altitude and air pollutant concentration have a linear relationship, the altitude linear equation is:

[0094] PM = K4 * H;

[0095] Where K4 is the fitting coefficient for the linear relationship, and H is the height.

[0096] Meanwhile, based on the regression equation, the vertical distribution data of wind speed, temperature, and relative humidity, as well as the vertical distribution data of atmospheric pollutant concentration at different altitudes, are linearly reconstructed to obtain the contribution values ​​of wind speed, temperature, relative humidity, and altitude to atmospheric pollutant concentration. The specific process is as follows:

[0097] When the linear relationship between temperature and atmospheric pollutant concentration yields the best fit, and the linear relationship between relative humidity and atmospheric pollutant concentration also yields the best fit, the equation for the first contribution value is:

[0098] PM=K1′*T+K′2*WS+K′3*RH+K′4*H;

[0099] The second contribution value equation shows the best fit when there is a linear relationship between temperature and atmospheric pollutant concentration, and also the best fit when there is a reciprocal relationship between relative humidity and atmospheric pollutant concentration.

[0100]

[0101] The third contribution equation is best when the linear relationship between temperature and atmospheric pollutant concentration is optimal, and also best when the exponential relationship between relative humidity and atmospheric pollutant concentration is optimal.

[0102] PM=K1′*T+K′2*WS+K′3″*e (f(RH)) +K′4*H;

[0103] The fourth contribution equation shows the best fit when temperature and air pollutant concentration have an inverse relationship, and the best fit when relative humidity and air pollutant concentration have a linear relationship.

[0104]

[0105] When the fit between temperature and atmospheric pollutant concentration is inversely proportional, and the fit between relative humidity and atmospheric pollutant concentration is also inversely proportional, the equation for the fifth contribution value is:

[0106]

[0107] The sixth contribution equation is best when the relationship between temperature and atmospheric pollutant concentration is inversely proportional, and when the relationship between relative humidity and atmospheric pollutant concentration is exponentially proportional.

[0108]

[0109] The seventh contribution equation is best when the relationship between temperature and atmospheric pollutant concentration shows an exponential relationship, and also best when the relationship between relative humidity and atmospheric pollutant concentration shows a linear relationship.

[0110] PM=K1″′*e (f(T)) +K′2*WS+K′3*RH+K′4*H;

[0111] The eighth contribution equation is best when the relationship between temperature and atmospheric pollutant concentration is exponential, and also best when the relationship between relative humidity and atmospheric pollutant concentration is inverse.

[0112]

[0113] When the fit between temperature and atmospheric pollutant concentration is exponential, and the fit between relative humidity and atmospheric pollutant concentration is also exponential, the equation for the ninth contribution value is:

[0114] PM=K1″′*e (f(T)) +K′2*WS+K′3″*e (f(RH)) +K′4*H;

[0115] Where K1′, K′2, K′3, and K′4 are the fitting coefficients for the linear relationship, and K1″, K1″′, K′3′, and K′3″ are the fitting coefficients for the nonlinear relationship. The corresponding contribution value is obtained according to a determined contribution value equation.

[0116] This invention focuses on important meteorological factors and performs mathematical and refined correlation analysis on the vertical distribution characteristics of various pollutants.

[0117] This invention provides a system for analyzing the vertical distribution characteristics of atmospheric pollutants, comprising:

[0118] The data stratification module acquires vertical distribution data of wind speed, wind direction, temperature, relative humidity, and atmospheric pollutant concentration, and stratifies all vertical distribution data according to altitude. The atmospheric segmentation module divides the wind field within each segment according to multiple directions included in the wind direction. The wind field is described by the wind direction and the corresponding wind speeds in these directions, collectively representing the spatial distribution and intensity of airflow. The equation construction module performs regression analysis on the vertical distribution data of wind speed, temperature, and relative humidity, along with their corresponding altitudes, against the vertical distribution data of atmospheric pollutant concentrations in each direction within the wind field segment, obtaining the optimal regression equation. The optimal regression equation is then linearly reconstructed to obtain the contribution value equation, which represents the contribution of wind speed, temperature, relative humidity, and altitude to atmospheric pollutant concentration. The vertical distribution characteristic analysis module substitutes the vertical distribution data of wind speed, temperature, and relative humidity, along with their corresponding altitudes, into the contribution value equation to obtain the contribution values ​​in multiple directions within the wind field segment across multiple segments. This analyzes the impact of wind speed, temperature, relative humidity, and altitude on atmospheric pollutant concentrations, revealing the vertical distribution characteristics of atmospheric pollutants.

[0119] A specific example is as follows:

[0120] Step 1: Organize the acquired meteorological parameters and vertical distribution data of air pollutant concentrations to determine the height stratification resolution for vertical distribution characteristic analysis. Meteorological parameters include wind speed, wind direction, temperature, and relative humidity; air pollutants include particulate matter and gases.

[0121] Step 2: Based on the wind direction data for each altitude layer, the wind field is divided into 16 directions.

[0122] Step 3: Based on each direction determined in Step 2, divide the atmospheric flow range according to the wind speed data. This range includes local transport, regional transport, and long-distance transport.

[0123] Step 4: Based on Step 2, establish empirical relationships between wind speed, temperature, humidity, altitude, and pollutants.

[0124] Step 5: Based on the empirical relationships in Step 4, reconstruct the contribution and characteristics of wind speed, relative humidity, temperature, and altitude to the concentration of air pollutants.

[0125] Step 6: Based on steps 2, 3, 4, and 5, summarize the vertical distribution characteristics of air pollutants at different altitudes in the near-surface atmosphere.

[0126] Vertical distribution data acquisition: In the first step, specific methods for acquiring meteorological parameters can include online meteorological data measured at different heights using meteorological towers (e.g., measurement height from the ground to approximately 300 meters), meteorological data measured by radiosondes carried by weather balloons (e.g., measurement height from 0-30 km), wind field data measured by wind profiler radars (e.g., measurement height from the ground to 6 km), and meteorological data acquired by meteorological monitoring stations equipped on large moored boats (CETC38). Specific methods for acquiring vertical distribution data of pollutants can include vertical distribution data measured by online monitoring instruments for atmospheric pollutants, such as observations using mobile pods on meteorological towers (Beijing 325-meter meteorological tower, Boulder 300-meter Elephant Tower in the United States), and data on gases (e.g., NH3 measured by LGR ammonia analyzer, Thermo Fisher Scientific 17i ammonia analyzer, CO, SO2, NOx, O3, etc. measured by Thermo Fisher Scientific gas analyzers) and particulate matter (aerosol chemistry data acquired by ACSM aerosol mass spectrometry). Source apportionment data for particulate and organic factors, such as organic matter, sulfates, nitrates, ammonium salts, chlorides, food and beverage sources, biomass combustion sources, motor vehicle sources, and secondary sources; aerosol optical property data, such as aerosol extinction and scattering coefficients measured by the photoacoustic aerosol extinction meter PAX; black carbon measured by the black carbon meter AE33; vertical profiles of gas and particulate matter obtained by online monitoring equipment for air pollutants mounted on a large moored vessel (CETC38); and vertical profiles of gas and particulate matter concentrations obtained by remote sensing equipment such as ozone and particulate matter lidar.

[0127] Determining the height stratification resolution: In the first step, the vertical distribution height stratification is determined according to research needs. For example, based on the 325-meter meteorological tower in Beijing, the height stratification can be 15 layers (8, 15, 32, 47, 65, 80, 103, 120, 140, 160, 180, 200, 240, 280, and 320 meters). For observations by large moored boats and remote sensing equipment at a detection height of 1000 meters, the height stratification resolution can be 50 meters. Missing temperature, humidity, and pollutant concentration data are interpolated or smoothed to obtain more refined height distribution information. Wind field data is vector data and no interpolation is performed.

[0128] Analysis and classification of wind direction data: In the second step, the wind field is divided into 16 wind field directions in 22.5-degree intervals, from 0 to 360 degrees, based on the wind direction data of each height layer.

[0129] Wind speed data division: In the third step, the wind speed under each wind direction is divided into three intervals, including local, regional and long-distance transmission intervals, with wind speed ranges of <3 m / s, 3-7 m / s and >7 m / s respectively.

[0130] Establishment of a vertical distribution characteristic analysis method: In step four, mathematical algorithms such as linear regression and exponential trend fitting are used to construct the relationship between wind speed, temperature, and relative humidity and the vertical distribution of air pollutants, thus initially establishing a method for analyzing the vertical distribution characteristics of near-surface air pollutants based on meteorological parameters. In step five, referring to the individual meteorological parameters and pollutant relationships in step four, the influence contributions of wind speed, temperature, and relative humidity on air pollutant concentrations under each wind direction are linearly reconstructed. In step six, based on the wind field and direction information from step three, the wind speed information from step three, the empirical relationship information from step four, and the meteorological factor influence contribution information from step five, the vertical distribution characteristics of air pollutants at different altitudes in the near-surface atmosphere are summarized.

[0131] Among them, mathematical algorithms such as linear regression and exponential trend fitting are used to construct the relationship between wind speed, temperature and relative humidity and the vertical distribution of air pollutants, and the relationship with the best fitting effect is selected.

[0132] Analyze the physicochemical coupling characteristics of atmospheric pollutants and meteorological factors at various altitude levels. For example, determine the relative contribution of pollution transport from different directions based on the relationship between multiple or single profiles and the wind field; determine the distribution and diffusion trend of horizontally transported pollutants in the vertical direction based on the relationship between multiple vertical distribution profiles of pollutants in adjacent spaces and meteorological factors; determine the rate of change of pollution concentration with altitude for certain sources (surface, upper atmosphere, etc.); or determine the characteristics of pollutant concentration increase under the influence of humidity and temperature (wet removal, chemical degradation and aging, etc.).

[0133] This allows for the comprehensive collection and summarization of the physicochemical characteristics of the vertical distribution of atmospheric pollutants at different altitudes within the spatiotemporal range covered by the data.

[0134] In summary, by unifying the height resolution of meteorological and pollutant vertical distribution data, classifying them according to wind field and direction, and establishing mathematical relationships between meteorological parameters (wind, temperature, relative humidity) closely related to the development of pollution processes, we can conduct vertical characteristic analysis of atmospheric pollutants at different altitudes to obtain the vertical distribution characteristics of near-surface atmospheric pollution.

[0135] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A method for analyzing the vertical distribution characteristics of air pollutants, characterized in that, Includes the following steps: Acquire vertical distribution data of wind speed, wind direction, temperature, relative humidity and atmospheric pollutant concentration in the atmosphere, and stratify all vertical distribution data according to the height direction; Within each layer, the wind field is divided according to the multiple directions included in the wind direction. The wind field is described by the wind direction and the corresponding wind speeds in the multiple directions included in the wind direction, which together describe the spatial distribution and intensity of air flow. In each direction of the wind field, the vertical distribution data of wind speed, temperature, and relative humidity, along with their respective altitudes, are subjected to regression analysis with the vertical distribution data of air pollutant concentrations to obtain the optimal regression equation. The optimal regression equation is then linearly reconstructed to obtain the contribution value equation. The contribution value equation represents the contribution of wind speed, temperature, relative humidity, and altitude to the air pollutant concentration. By substituting the vertical distribution data of wind speed, temperature, and relative humidity, along with their altitude, into the contribution value equation, we can obtain the contribution values ​​in multiple directions within the wind field, across multiple layers. This allows us to analyze the impact of wind speed, temperature, relative humidity, and altitude on the concentration of air pollutants and obtain the vertical distribution characteristics of air pollutants. The specific steps for obtaining the optimal regression equation include: Regression analyses were performed on the vertical distribution data of temperature and the vertical distribution data of atmospheric pollutant concentrations, using linear, reciprocal, and exponential relationships. A regression analysis was performed on the vertical distribution data of wind speed and the vertical distribution data of air pollutant concentrations to determine the linear relationship. The linear equation for wind speed is as follows: PM= *WS; in, is the fitting coefficient for the linear relationship, and WS is the wind speed; Regression analyses were performed on the vertical distribution data of relative humidity and the vertical distribution data of air pollutant concentrations, using linear, reciprocal, and exponential relationships. A regression analysis was performed on the vertical distribution data of atmospheric pollutant concentrations at different altitudes. The linear equation for altitude is as follows: PM= *H; in, Here, H represents the fitting coefficient for the linear relationship, and H represents the height. The regression equation with the best fit was selected as the regression analysis method for the vertical distribution data of wind speed, temperature and relative humidity, as well as the vertical distribution data of atmospheric pollutant concentration at different altitudes. The specific steps for linearly reconstructing the optimal regression equation to obtain the contribution value equation include: When the linear relationship between temperature and atmospheric pollutant concentration shows the best fit, the regression equation that best fits relative humidity and atmospheric pollutant concentration is selected. When the linear relationship between relative humidity and atmospheric pollutant concentration yields the best fit, the equation for the first contribution value is: PM= ; When the fit between relative humidity and atmospheric pollutant concentration is inversely proportional, the equation for the second contribution value is: PM= ; When the relative humidity and air pollutant concentration show the best fit, the equation for the third contribution value is: PM= ; in, , , , The fitting coefficients represent the linear relationship. , The fitting coefficients represent the nonlinear relationship. When the fit between temperature and atmospheric pollutant concentration is the inverse, the regression equation with the best fit between relative humidity and atmospheric pollutant concentration is selected. When the relationship between temperature and atmospheric pollutant concentration shows the best fit, the regression equation that best fits relative humidity and atmospheric pollutant concentration is selected. The corresponding contribution value is obtained based on a defined contribution value equation.

2. The method for analyzing the vertical distribution characteristics of air pollutants as described in claim 1, characterized in that, Within each layer, the wind field is divided according to multiple directions included in the wind direction within the wind field, specifically as follows: Based on the wind direction data of each layer in the height direction, the wind field is divided into 16 wind field directions in 22.5-degree intervals within the range of 0 to 360 degrees.

3. The method for analyzing the vertical distribution characteristics of air pollutants as described in claim 1, characterized in that, The regression analysis of linear, reciprocal, and exponential relationships between the vertical distribution data of temperature and the vertical distribution data of air pollutant concentrations includes the following steps: When temperature and atmospheric pollutant concentration have a linear relationship, the linear equation for temperature is: PM= ; When temperature and atmospheric pollutant concentration have an inverse relationship, the inverse equation for temperature is: PM= ; When temperature and atmospheric pollutant concentration have an exponential relationship, the temperature exponential equation is: PM= ; PM represents the concentration of air pollutants. The fitting coefficients represent the linear or nonlinear relationships, and T represents the temperature.

4. The method for analyzing the vertical distribution characteristics of air pollutants as described in claim 1, characterized in that, The regression analysis of linear, reciprocal, and exponential relationships between the vertical distribution data of relative humidity and the vertical distribution data of air pollutant concentrations includes the following steps: When relative humidity and atmospheric pollutant concentration have a linear relationship, the linear equation for relative humidity is: PM= *RH; When relative humidity and atmospheric pollutant concentration are inversely related, the equation for the reciprocal of relative humidity is: PM= ; When relative humidity and atmospheric pollutant concentration have an exponential relationship, the equation for the relative humidity index is: PM= ; in, RH represents the fitting coefficient for linear or nonlinear relationships, and RH represents the concentration of air pollutants.

5. The method for analyzing the vertical distribution characteristics of air pollutants as described in claim 1, characterized in that, When the fit between temperature and air pollutant concentration is inversely proportional, the regression equation that best fits relative humidity and air pollutant concentration is selected. Specific steps include: When the linear relationship between relative humidity and atmospheric pollutant concentration yields the best fit, the equation for the fourth contribution value is: PM= ; When the fit between relative humidity and atmospheric pollutant concentration is inversely proportional, the equation for the fifth contribution value is: PM= ; When the relative humidity and atmospheric pollutant concentration show the best fit, the equation for the sixth contribution value is: PM= ; in, , , The fitting coefficients represent the linear relationship. , , The fitting coefficients represent the nonlinear relationship.

6. The method for analyzing the vertical distribution characteristics of air pollutants as described in claim 1, characterized in that, When the relationship between temperature and atmospheric pollutant concentration shows the best fit, the regression equation that best fits relative humidity and atmospheric pollutant concentration is selected. Specific steps include: When the linear relationship between relative humidity and atmospheric pollutant concentration yields the best fit, the equation for the seventh contribution value is: PM= ; When the fit between relative humidity and atmospheric pollutant concentration is inversely proportional, the equation for the eighth contribution value is: PM= ; When the relative humidity and atmospheric pollutant concentration show the best fit, the equation for the ninth contribution value is: PM= ; in, , , The fitting coefficients represent the linear relationship. , , The fitting coefficients represent the nonlinear relationship.

7. The method for analyzing the vertical distribution characteristics of air pollutants as described in claim 1, characterized in that, The specific steps for obtaining the vertical distribution characteristics of air pollutants include: Based on the contribution values ​​in multiple directions within multiple strata, the physicochemical coupling characteristics of wind speed, wind direction, temperature, and relative humidity with air pollutants in each height stratum are analyzed. The physicochemical coupling characteristics of air pollutants with wind speed, wind direction, temperature, and relative humidity in each layer are summarized as the results of the vertical distribution characteristics of air pollutants.

8. An atmospheric pollutant vertical distribution characteristic analysis system applied to the atmospheric pollutant vertical distribution characteristic analysis method according to any one of claims 1 to 7, comprising: The data stratification module is used to acquire the vertical distribution data of wind speed, wind direction, temperature, relative humidity and atmospheric pollutant concentration in the atmosphere and stratify all vertical distribution data according to the height direction; The atmospheric division module is used to divide the wind field within each layer according to the multiple directions included in the wind direction. The wind field is described by the wind direction and the corresponding wind speeds in the multiple directions included in the wind direction, which together describe the spatial distribution and intensity of air flow. The equation construction module is used to perform regression analysis on the vertical distribution data of wind speed, temperature, and relative humidity and their respective altitudes in each direction of the wind field, and on the vertical distribution data of atmospheric pollutant concentrations to obtain the optimal regression equation; the optimal regression equation is then linearly reconstructed to obtain the contribution value equation; the contribution value equation represents the contribution of wind speed, temperature, relative humidity, and altitude to the atmospheric pollutant concentration. The vertical distribution characteristic analysis module is used to substitute the vertical distribution data of wind speed, temperature and relative humidity and their altitude into the contribution value equation to obtain the contribution values ​​in multiple directions within multiple layers of the wind field. This allows for the analysis of the influence of wind speed, temperature, relative humidity and altitude on the concentration of air pollutants and the acquisition of the vertical distribution characteristics of air pollutants.

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