Plateau air pollution diffusion meteorological condition prediction method

Abstract

The invention relates to the technical field of environmental science and meteorology, in particular to a plateau air pollution diffusion meteorological condition prediction method, which is based on analysis of long-term observation data of an atmospheric self-purification capability index (ASI) and meteorological factors (such as temperature, rainfall, wind speed and the like) and is combined with a stepwise regression prediction method. And a prediction model of synchronous and early-stage factors is established. The ASI of different months from January to April is predicted, and the reliability of the method is verified. Through the method, the air pollution condition of the plateau region in the specific time period can be accurately predicted, timely early warning information is provided for the government, the environmental protection department and the public, corresponding control measures are taken, the environment and the health of people are protected, compared with the prior art, the method has the advantages of being high in prediction accuracy, easy and convenient to operate and the like, and popularization is easy. The blank in the field of plateau area air pollution prediction is filled, and the method has a remarkable application prospect.

Description

Technical Field

[0001] The invention relates to the technical field of environmental science and meteorology, and in particular to a method for predicting meteorological conditions for plateau air pollution diffusion. Background Art

[0002] At present, the improvement of air quality and air pollution control in plateau areas have become one of the hot topics worldwide. However, the atmospheric environment in plateau areas is complex, and its special geographical and meteorological conditions make the applicability of traditional air pollution diffusion prediction methods in plateau areas challenging. Especially in certain seasons and meteorological conditions, the pollution diffusion behavior in plateau areas may show completely different characteristics from that in plain areas, which poses a severe challenge to accurate pollution control and prevention.

[0003] The existing technologies have the following deficiencies in predicting the diffusion of air pollution in plateau areas:

[0004] Lack of specialized models for plateau areas: Traditional air pollution diffusion prediction models are often based on meteorological data and environmental characteristics of plain areas, and cannot fully consider the unique geographical and meteorological conditions of plateau areas, resulting in low prediction accuracy.

[0005] Poor adaptability to special seasons and meteorological conditions: In plateau areas, especially in winter and spring, the diffusion behavior of air pollution may change significantly due to atmospheric inversion and topographic influences, and traditional models often cannot accurately predict these situations.

[0006] Insufficient data collection and processing: It is relatively difficult to collect meteorological and environmental data in plateau areas, and the existing data may not be sufficient to meet the needs of modeling and prediction, which limits the accuracy of the prediction model.

[0007] Model simplification affects accuracy: Traditional models often simplify the impact of meteorological factors and ignore many factors that may affect the diffusion of air pollution, resulting in low accuracy of prediction results.

[0008] Prediction time lag: Traditional methods often take a long time to collect, process and analyze data, which limits the timeliness of prediction results and makes it difficult to meet the needs of emergency response.

[0009] In summary, existing technologies have a series of limitations in predicting the diffusion of air pollution in plateau areas, and new methods and technical means are needed to make up for these shortcomings. Summary of the invention

[0010] In view of this, the purpose of the present invention is to provide a method for predicting meteorological conditions for plateau air pollution diffusion to solve the above-mentioned problems.

[0011] Based on the above purpose, the present invention provides a method for predicting meteorological conditions for plateau air pollution diffusion.

[0012] A method for predicting meteorological conditions for plateau air pollution diffusion comprises the following steps:

[0013] 1.1. Data collection: Collect and organize the atmospheric self-purification index (ASI) from January to April in the plateau area in the past years and the historical meteorological data of monthly average temperature, precipitation, average wind speed, maximum wind speed, maximum temperature, minimum temperature, relative humidity and sea level pressure in the same period (January to April) and the previous period (December to March).

[0014] 1.2. Correlation analysis: Conduct statistical correlation analysis on the historical meteorological data to screen out relevant factors that pass the significance test, including but not limited to temperature, precipitation, wind speed, humidity, and air pressure, as input factors of the prediction model.

[0015] 1.3. Stepwise regression modeling: Using the stepwise regression method, a prediction model based on the meteorological factors of the same period and previous period is established for each month (January to April). Specifically, it includes:

[0016] 1.3.1. Modeling of concurrent factors: For each month, a forecasting model based on concurrent meteorological factors is established. For example, for January, a model based on the meteorological data of January is established.

[0017] 1.3.2. Modeling of previous factors: For each month, a prediction model based on previous (previous month) meteorological factors is established.

[0018] 1.4. Application of forecast model: Based on the established forecast model, by inputting the meteorological data of the same period and previous period, the forecast value of the meteorological conditions for air pollution diffusion in the plateau area in the corresponding month is calculated, that is, the ASI index of the corresponding month is predicted.

[0019] Furthermore, in the correlation analysis, the meteorological factors related to the atmospheric self-purification index (ASI) were screened out through a significance test at the 0.05 level.

[0020] Furthermore, in the stepwise regression method, a statistical algorithm is used to iteratively select the most influential meteorological factors and establish a prediction model for the contemporaneous and previous factors.

[0021] Furthermore, the prediction model includes an equation based on concurrent meteorological factors and an equation based on previous meteorological factors to improve prediction accuracy.

[0022] Furthermore, the prediction model gives the predicted value of the atmospheric self-purification index (ASI) of the corresponding month in the prediction results to provide a prediction of the meteorological conditions for air pollution diffusion in the plateau area.

[0023] Furthermore, the method is applicable to air quality prediction in plateau areas, providing a reference for environmental protection and pollution control.

[0024] Beneficial effects of the present invention:

[0025] 1. Improved accuracy: Through in-depth research on the special geographical and meteorological conditions of the plateau area, this method takes into account the impact of many factors on pollution diffusion, so its prediction results are more accurate than traditional methods.

[0026] 2. Enhanced reliability: Through the accumulation and analysis of a large amount of historical meteorological and pollution data, this method has established a relatively complete prediction model, making the prediction results more reliable and applicable to different plateau areas.

[0027] 3. Excellent practicality: It provides a prediction model based on concurrent and previous meteorological factors, making the prediction process relatively simple and providing strong support for actual environmental protection decisions.

[0028] 4. Timely early warning response: Since this method uses advanced data analysis techniques such as stepwise regression, it can give relatively accurate prediction results in a shorter period of time, which helps to take corresponding control measures in time before the air quality deteriorates. Description of the drawings:

[0029] Figure 1 A schematic diagram of the structure of the prediction method flow in an embodiment of the present invention;

[0030] Figure 2 This is a schematic diagram of the annual change of the atmospheric self-purification capacity index according to an embodiment of the present invention;

[0031] Figure 3 This is a schematic diagram of the annual change of the atmospheric self-purification capacity index according to an embodiment of the present invention;

[0032] Figure 4 This is a schematic diagram of the monthly changes in the gas self-purification capacity index according to an embodiment of the present invention;

[0033] Figure 5 ASI prediction and actual curve diagram of an embodiment of the present invention;

[0034] Figure 6 ASI prediction and actual curve diagram of an embodiment of the present invention;

[0035] Figure 7 Schematic diagram of ASI prediction and actual curve diagram according to an embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0037] Example:

[0038] like Figures 1 to 6 As shown, a method for predicting meteorological conditions for plateau air pollution diffusion comprises the following steps:

[0039] 1.1. Data collection: Collect and organize the atmospheric self-purification index (ASI) from January to April in the plateau area in the past years and the historical meteorological data of monthly average temperature, precipitation, average wind speed, maximum wind speed, maximum temperature, minimum temperature, relative humidity and sea level pressure in the same period (January to April) and the previous period (December to March).

[0040] The calculation method of the atmospheric self-purification capacity index is as follows:

[0041] In the formula, A is the atmospheric self-purification capacity index, in units of 104km·a-1; VE is the ventilation volume, in units of m 2·s-1; R is the precipitation, in units of mm·d-1; S is the unit area, which is 100km2. Referring to the "Grades of Atmospheric Self-Purification Capacity" (GB / T34299-2017), the atmospheric self-purification capacity is divided into grades, as shown in Table 29.

[0042] It can be seen from formula 1 that the atmospheric self-purification capacity is determined by ventilation and precipitation. Ventilation refers to the total amount of air passing through a unit width of lateral wind per unit time in the entire mixing layer. It is a parameter that describes the atmospheric dilution and diffusion capacity of pollutants. The specific calculation method refers to the national standard "Grades of Atmospheric Self-Purification Capacity" (GB / T 34299-2017).

[0043] The above calculation formula is combined with the following table:

[0044] Table 1. Changes in atmospheric self-purification capacity and AQI index during the heating period in Xining City from 2014 to 2017

[0045]

[0046] Table 2. Classification of atmospheric self-purification capacity

[0047]

[0048] 1.2. Correlation analysis: Conduct statistical correlation analysis on the historical meteorological data to screen out relevant factors that pass the significance test, including but not limited to temperature, precipitation, wind speed, humidity, and air pressure, as input factors of the prediction model.

[0049] 1.3. Stepwise regression modeling: Using the stepwise regression method, a prediction model based on the meteorological factors of the same period and previous period is established for each month (January to April). Specifically, it includes:

[0050] 1.3.1. Modeling of concurrent factors: For each month, a forecasting model based on concurrent meteorological factors is established. For example, for January, a model based on the meteorological data of January is established.

[0051] 1.3.2. Modeling of previous factors: For each month, a prediction model based on previous (previous month) meteorological factors is established.

[0052] 1.4. Application of forecast model: Based on the established forecast model, by inputting the meteorological data of the same period and previous period, the forecast value of the meteorological conditions for air pollution diffusion in the plateau area in the corresponding month is calculated, that is, the ASI index of the corresponding month is predicted.

[0053] The stepwise regression forecasting method is used to establish a forecasting model based on concurrent and previous meteorological factors.

[0054] The contemporaneous factor prediction equation given by the regression prediction method is:

[0055] Y^=7.72X2+0.23X1+0.45X3-7.48

[0056] The above equation gives the complex correlation coefficient Rxy=0.98 and the residual standard deviation Sy=0.19 of the prediction equation of the atmospheric self-purification capacity index in March based on the meteorological factors of the same period. The key factors selected in the prediction equation are temperature, average wind speed, maximum wind speed, maximum temperature, minimum temperature and sea level pressure, indicating that the above meteorological factors in March have a greater impact on the atmospheric self-purification capacity index ASI, and the impact of the maximum temperature is less than that of other factors.

[0057] Judging from the forecast results of the forecast equation established by the same period factors, the March ASI forecast curve and the actual curve are almost the same, indicating that the model can better predict the atmospheric self-purification capacity index in March. The model predicts that the ASI in March 2020 is 12.8, with an error of only 0.4 from the actual value, which is slightly higher than the actual value (the actual value of ASI in 2020 is 12.4).

[0058] According to the results of the consistency test analysis of the anomaly signs of the ASI predicted values ​​and the actual values, the accuracy of the qualitative forecast with the consistent signs of the predicted and actual anomalies from 1981 to 2019 was 94.9%, and only two years had opposite anomaly signs. However, the quantitative average error between the predicted values ​​and the actual values ​​was ≤0.5, that is, the qualitative forecast accuracy with basically consistent anomaly signs and small errors was 100%. The trend prediction of the negative ASI anomaly in 2020 was accurate, and the error between the predicted anomaly value and the actual value was 0.4.

[0059] ② The prediction equation based on the previous factors given by the regression prediction method is:

[0060] Y^=2.13X3+0.85

[0061] The above equation gives the complex correlation coefficient of the March atmospheric self-purification index prediction equation based on the previous meteorological factors, Rxy = 0.68, and the residual standard deviation is Sy = 0.78. The key factors selected for the prediction equation in February are precipitation, average wind speed, maximum wind speed, relative humidity and sea level pressure, among which wind speed has a greater impact, while relative humidity and sea level pressure field have a smaller impact.

[0062] Judging from the prediction results of the prediction equation established by the previous factors, the prediction curve can generally reflect the trend of ASI changes, but before 1990 and after 2010, the deviation from the actual situation is large, and the years with large ASI deviation values ​​are more than 3.0. The model predicts that the ASI in March 2020 will be 13.0, with an error of 0.6 from the actual situation, which is slightly higher than the actual value (the actual value of ASI in 2020 is 12.4).

[0063] From the results of the consistency test of the anomaly signs of the ASI forecast and actual values, the accuracy of the qualitative forecast of the consistent signs of the forecast and actual values ​​from 1981 to 2019 was 82.1%. There were 7 years with opposite anomaly signs. The ASI anomaly in 2020 was a negative trend and the prediction was accurate. The error between the forecast anomaly value and the actual value was 1.0

[0064] In a specific implementation manner, in the correlation analysis, meteorological factors related to the atmospheric self-purification index (ASI) are screened out through a significance test at the 0.05 level.

[0065] Specifically, in the stepwise regression method, a statistical algorithm is used to iteratively select the most influential meteorological factors and establish a prediction model for the contemporaneous and previous factors.

[0066] Specifically, the prediction model includes an equation based on concurrent meteorological factors and an equation based on previous meteorological factors to improve prediction accuracy.

[0067] Specifically, the prediction model gives the predicted value of the atmospheric self-purification index (ASI) of the corresponding month in the prediction results to provide a prediction of the meteorological conditions for air pollution diffusion in the plateau area.

[0068] Specifically, the method is applicable to air quality prediction in plateau areas and provides a reference for environmental protection and pollution control.

[0069] The present invention is intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for predicting meteorological conditions for plateau air pollution diffusion, characterized in that it comprises the following steps: 1.

1. Data collection: Collect and organize the atmospheric self-purification index (ASI) from January to April in the plateau area in the past years and the historical meteorological data of the monthly average temperature, precipitation, average wind speed, maximum wind speed, maximum temperature, minimum temperature, relative humidity and sea level pressure during the same period and the previous period; Among them, the same period was from January to April, and the early period was from December to March. 1.

2. Correlation analysis: Conduct statistical correlation analysis on the historical meteorological data to screen out relevant factors that pass the significance test, including but not limited to temperature, precipitation, wind speed, humidity, and air pressure, as input factors of the prediction model. 1.

3. Stepwise regression modeling: Using the stepwise regression method, a prediction model based on the meteorological factors of the same period and previous period is established for each month from January to April. Specifically, it includes: 1.3.

1. Modeling of concurrent factors: For each month, a forecasting model based on concurrent meteorological factors is established. For example, for January, a model based on the meteorological data of January is established. 1.3.

2. Modeling of previous factors: For each month, a prediction model based on previous meteorological factors is established, with the previous period being the previous month. 1.

4. Application of forecast model: Based on the established forecast model, by inputting the meteorological data of the same period and previous period, the forecast value of the meteorological conditions for air pollution diffusion in the plateau area in the corresponding month is calculated, that is, the ASI index of the corresponding month is predicted.

2. A method for predicting meteorological conditions for plateau air pollution diffusion according to claim 1, characterized in that: In the correlation analysis, meteorological factors related to the atmospheric self-purification capacity index ASI were screened out through a significance test at the 0.05 level.

3. A method for predicting meteorological conditions for plateau air pollution diffusion according to claim 2, characterized in that: In the stepwise regression method, a statistical algorithm is used to iteratively select the most influential meteorological factors to establish a prediction model for the same period and previous period factors.

4. A method for predicting meteorological conditions for plateau air pollution diffusion according to claim 3, characterized in that: The prediction model includes an equation based on concurrent meteorological factors and an equation based on previous meteorological factors, so as to improve the prediction accuracy.

5. A method for predicting meteorological conditions for plateau air pollution diffusion according to claim 4, characterized in that: The prediction model gives the predicted value of the atmospheric self-purification index ASI of the corresponding month in the prediction results to provide a prediction of the meteorological conditions for air pollution diffusion in the plateau area.

6. A method for predicting meteorological conditions for plateau air pollution diffusion according to claim 5, characterized in that: The method is suitable for air quality prediction in plateau areas and provides a reference for environmental protection and pollution control.

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