阅读说明：本技术 一种基于大气活动的北京市春季大气pm2.5浓度变化预报方法 (Atmospheric activity-based forecasting method for PM2.5 concentration change of atmosphere in spring of Beijing city ) 是由 王叶 李书一 林嘉希 于 2019-11-22 设计创作，主要内容包括：一种基于大气活动的北京市春季大气PM2.5浓度变化预报方法。本发明涉及PM2.5预报方法领域。提出了一种逻辑清晰、步骤有序且准确度高,在使用过程中综合考虑了气旋以及反气旋对PM2.5浓度的影响的基于大气活动的北京市春季大气PM2.5浓度变化预报方法。按以下步骤进行预报：1)、样本采集；2)、气旋波数据处理；3)、反气旋波数据处理；4)、建立回归模型；5)、PM2.5浓度预报。本发明在使用过程中综合考虑了气旋以及反气旋对PM2.5浓度的影响,从大气环流角度预测未来北京市春季大气PM2.5浓度变化,有效的提高了北京市春季大气PM2.5浓度预报的精度和准确度。(An atmospheric activity-based forecasting method for atmospheric PM2.5 concentration change in spring of Beijing city. The invention relates to the field of PM2.5 forecasting methods. The forecasting method for the concentration change of the PM2.5 in spring of Beijing city based on atmospheric activity is clear in logic, orderly in steps and high in accuracy, and comprehensively considers the influence of cyclone and anti-cyclone on the PM2.5 concentration in the using process. Forecasting is carried out according to the following steps: 1) collecting a sample; 2) cyclone wave data processing; 3) processing the data of the anti-cyclone wave; 4) establishing a regression model; 5) and predicting PM2.5 concentration. The influence of the cyclone and the anti-cyclone on the PM2.5 concentration is comprehensively considered in the using process, the future change of the PM2.5 concentration of the atmosphere in spring of Beijing city is predicted from the angle of atmospheric circulation, and the accuracy and precision of the PM2.5 concentration prediction of the atmosphere in spring of Beijing city are effectively improved.)

1. A forecast method for the change of PM2.5 concentration of the atmosphere in spring of Beijing city based on the atmosphere activity is characterized by forecasting according to the following steps:

1) and collecting a sample:

1.1) collecting year-by-year observation data of the concentration of PM2.5 in the atmosphere in spring of Beijing, wherein the geographic coordinates of the Beijing are 116.23 degrees from east longitude and 39.54 degrees from north latitude;

1.2) collecting and calculating the cyclone wave value and the anti-cyclone wave value of the atmosphere at the moment corresponding to the step 1.1) of each lattice point in the area near Beijing City, wherein the range of the area near Beijing City is 70-150 degrees of east longitude and 20-60 degrees of north latitude;

2) cyclone wave data processing: finding out the point of the central value of the cyclone wave;

3) and (3) performing anti-cyclone data processing: finding out the point of the central value of the anti-cyclone wave;

4) establishing a regression model:

4.1) counting the cyclone wave values of all the atmosphere at the point where the cyclone wave center value is located and the corresponding time of the step 1.1);

4.2) counting the anti-cyclone wave values of all the atmospheres at the time corresponding to the point of the anti-cyclone wave center value in the step 1.1);

4.3) establishing a multiple linear regression model by taking the values in the step 4.1) and the step 4.2) as independent variables and the values in the step 1.1) as dependent variables;

5) and PM2.5 concentration forecasting:

5.1) setting the time required to forecast;

5.2) collecting and calculating the cyclone wave value of the point of the cyclone wave center value at the time of the step 5.1);

5.3) collecting and calculating the anti-cyclone wave value of the point of the anti-cyclone wave center value at the moment of the step 5.1);

5.4) and inputting the values of the steps 5.2) and 5.3) into a regression model to obtain a forecast value of the concentration of PM2.5 in Beijing.

2. The method for forecasting the change of the PM2.5 concentration of the spring atmosphere in Beijing based on atmospheric activities according to claim 1, wherein the step 2) is specifically as follows:

2.1) extracting all the moments when the concentration of PM2.5 is more than 90% from the values collected in the step 1.1);

2.2) extracting a cyclone wave value of each lattice point in the area near Beijing City when the PM2.5 concentration of the Beijing City is more than 90% from the values collected in the step 1.2) according to the step 2.1);

2.3) calculating the average value of the cyclone waves of each grid point in the step 2.2) one by one;

2.4) and finding the point with the maximum average value of all the grid points in the step 2.3), and recording the point as the point of the cyclone wave center value.

3. The method for forecasting the change of the PM2.5 concentration of the spring atmosphere in Beijing based on atmospheric activities according to claim 1, wherein the step 3) is specifically as follows:

3.1) extracting all the moments when the concentration of PM2.5 is more than 90% from the values collected in the step 1.1);

3.2) extracting an anti-cyclone wave value of each lattice point in the area near Beijing City when the PM2.5 concentration of the Beijing City is more than 90% from the values collected in the step 1.2) according to the step 3.1);

3.3) calculating the average value of the cyclone waves of each grid point in the step 3.2) one by one;

3.4) and finding the point with the maximum average value of all the grid points in the step 3.3), and recording the point as the point of the center value of the cyclone wave.

4. The method for forecasting the PM2.5 concentration change of the spring atmosphere in Beijing city based on atmospheric activities according to claim 1, wherein the longitude λ₀Latitude phi_eThe local anticyclonic wave at (a) is defined as:

A_Nnot less than 0, representing the anticyclonic wave towards the north;

the cyclonic wave is defined as:

in the northern hemisphere, A_sIs less than or equal to 0 and represents a cyclone wave, and a is the radius of the earth.

Technical Field

The invention relates to the field of PM2.5 forecasting methods, and relates to an improvement of a spring atmosphere PM2.5 forecasting method in Beijing.

Background

Along with the acceleration of urbanization and industrialization process, in recent years, haze weather frequently occurs, the primary pollutants are mainly suspended dust particles (PM2.5) with the diameter less than or equal to 2.5 micrometers, and the mass concentration of the suspended dust particles often exceeds the national secondary standard (75 mu g/m)³) The method brings serious harm to environmental quality, human body health and traffic safety, so that the method is particularly important for accurately forecasting the PM2.5 concentration change.

In contrast, the applicant filed a chinese patent application entitled "a method for predicting the concentration change of PM2.5 in the atmosphere in winter in beijing" on 23/6/2017 and having application number "201710484048.8", filed a chinese patent application entitled "a method for predicting the concentration change of PM2.5 in atmosphere" on 16/8/2017 and having application number "201710700302.3", and the applicant filed methods for predicting the concentration change of PM2.5 by using cyclone waves and anti-cyclone waves, respectively. However, in practical use, it is found that only considering the influence of the cyclone or the anti-cyclone on PM2.5 alone still has a certain error rate, and has a certain defect in the accuracy of prediction.

Disclosure of Invention

Aiming at the problems, the invention provides the atmospheric activity-based forecasting method for the concentration change of the PM2.5 in spring in Beijing city based on the atmospheric activity, which has clear logic, ordered steps and high accuracy and comprehensively considers the influence of cyclone and anti-cyclone on the PM2.5 concentration in the using process.

The technical scheme of the invention is as follows: forecasting is carried out according to the following steps:

1) and collecting a sample:

1.1) collecting year-by-year observation data of the concentration of PM2.5 in the atmosphere in spring of Beijing, wherein the geographic coordinates of the Beijing are 116.23 degrees from east longitude and 39.54 degrees from north latitude;

1.2) collecting and calculating the cyclone wave value and the anti-cyclone wave value of the atmosphere at the moment corresponding to the step 1.1) of each lattice point in the area near Beijing City, wherein the range of the area near Beijing City is 70-150 degrees of east longitude and 20-60 degrees of north latitude;

2) cyclone wave data processing: finding out the point of the central value of the cyclone wave;

3) and (3) performing anti-cyclone data processing: finding out the point of the central value of the anti-cyclone wave;

4) establishing a regression model:

4.1) counting the cyclone wave values of all the atmosphere at the point where the cyclone wave center value is located and the corresponding time of the step 1.1);

4.2) counting the anti-cyclone wave values of all the atmospheres at the time corresponding to the point of the anti-cyclone wave center value in the step 1.1);

4.3) establishing a multiple linear regression model by taking the values in the step 4.1) and the step 4.2) as independent variables and the values in the step 1.1) as dependent variables;

5) and PM2.5 concentration forecasting:

5.1) setting the time required to forecast;

5.2) collecting and calculating the cyclone wave value of the point of the cyclone wave center value at the time of the step 5.1);

5.3) collecting and calculating the anti-cyclone wave value of the point of the anti-cyclone wave center value at the moment of the step 5.1);

5.4) and inputting the values of the steps 5.2) and 5.3) into a regression model to obtain a forecast value of the concentration of PM2.5 in Beijing.

The step 2) is specifically as follows:

2.1) extracting all the moments when the concentration of PM2.5 is more than 90% from the values collected in the step 1.1);

2.2) extracting a cyclone wave value of each lattice point in the area near Beijing City when the PM2.5 concentration of the Beijing City is more than 90% from the values collected in the step 1.2) according to the step 2.1);

2.3) calculating the average value of the cyclone waves of each grid point in the step 2.2) one by one;

2.4) and finding the point with the maximum average value of all the grid points in the step 2.3), and recording the point as the point of the cyclone wave center value.

The step 3) is specifically as follows:

3.1) extracting all the moments when the concentration of PM2.5 is more than 90% from the values collected in the step 1.1);

3.2) extracting an anti-cyclone wave value of each lattice point in the area near Beijing City when the PM2.5 concentration of the Beijing City is more than 90% from the values collected in the step 1.2) according to the step 3.1);

3.3) calculating the average value of the cyclone waves of each grid point in the step 3.2) one by one;

3.4) and finding the point with the maximum average value of all the grid points in the step 3.3), and recording the point as the point of the center value of the cyclone wave.

Longitude λ₀Latitude phi_eThe local anticyclonic wave at (a) is defined as:

A_Nnot less than 0, representing the anticyclonic wave towards the north;

the cyclonic wave is defined as:

in the northern hemisphere, A_sIs less than or equal to 0 and represents a cyclone wave, and a is the radius of the earth.

The influence of the cyclone and the anti-cyclone on the PM2.5 concentration is comprehensively considered in the using process, the future change of the PM2.5 concentration of the atmosphere in spring of Beijing city is predicted from the angle of atmospheric circulation, and the accuracy and precision of the PM2.5 concentration prediction of the atmosphere in spring of Beijing city are effectively improved.

Detailed Description

The invention carries out forecast according to the following steps:

1) and collecting a sample:

1.1) collecting year-by-year observation data (P1, P2, P3 … … Pn) of the concentration of PM2.5 in the atmosphere in spring of Beijing, wherein the geographic coordinates of the Beijing are 116.23 degrees of east longitude and 39.54 degrees of north latitude;

1.2) collecting and calculating the cyclone wave value and the anti-cyclone wave value of the atmosphere at the moment corresponding to the step 1.1) of each lattice point in the area near Beijing City, wherein the range of the area near Beijing City is 70-150 degrees of east longitude and 20-60 degrees of north latitude; assuming that there are X grid points in the region, the cyclone wave value of the X-th grid point may be denoted as WX1, WX2, WX3 … … WXn, and the anti-cyclone wave value may be denoted as AX1, AX2, AX3 … … AXn;

longitude λ₀Latitude phi_eThe local anticyclonic wave at (a) is defined as:

A_Nnot less than 0, representing the anticyclonic wave towards the north;

the cyclonic wave is defined as:

in the northern hemisphere, A_sLess than or equal to 0, representing cyclone wave, a is the earth radius;

2) cyclone wave data processing:

2.1) extracting all the moments when the concentration of PM2.5 is more than 90% from the values collected in the step 1.1);

2.2) extracting a cyclone wave value of each lattice point in the area near Beijing City when the PM2.5 concentration of the Beijing City is more than 90% from the values collected in the step 1.2) according to the step 2.1);

2.3) calculating the average value of the cyclone waves of each grid point in the step 2.2) one by one;

2.4) finding out the point with the maximum average value of all the grid points in the step 2.3), and recording the point as the point of the cyclone wave center value;

3) and (3) performing anti-cyclone data processing:

3.1) extracting all the moments when the concentration of PM2.5 is more than 90% from the values collected in the step 1.1);

3.2) extracting an anti-cyclone wave value of each lattice point in the area near Beijing City when the PM2.5 concentration of the Beijing City is more than 90% from the values collected in the step 1.2) according to the step 3.1);

3.3) calculating the average value of the cyclone waves of each grid point in the step 3.2) one by one;

3.4) finding out the point with the maximum average value of all the grid points in the step 3.3), and recording the point as the point of the central value of the anti-cyclone wave;

4) establishing a regression model:

4.1) counting the cyclone wave values of all the atmosphere at the point where the cyclone wave center value is located and the corresponding time of the step 1.1); assuming that the 56 th point is the point of the center value of the cyclone wave, counting W561, W562, W563 … … and W56 n;

4.2) counting the anti-cyclone wave values of all the atmospheres at the time corresponding to the point of the anti-cyclone wave center value in the step 1.1); assuming that the 98 th point is the point of the center value of the anti-cyclone wave, A981, A982, A983 … … A98n are counted;

4.3) establishing a multiple linear regression model by taking the values in the step 4.1) and the step 4.2) as independent variables and the values in the step 1.1) as dependent variables;

5) and PM2.5 concentration forecasting:

5.1) setting the time required to forecast;

5.2) collecting and calculating the cyclone wave value of the point of the cyclone wave center value at the time of the step 5.1);

5.3) collecting and calculating the anti-cyclone wave value of the point of the anti-cyclone wave center value at the moment of the step 5.1);

5.4) and inputting the values of the steps 5.2) and 5.3) into a regression model to obtain a forecast value of the concentration of PM2.5 in Beijing.